MUAP abnormalities and indicated anatomical changes.
\r\n\t
",isbn:"978-1-83962-547-3",printIsbn:"978-1-83962-546-6",pdfIsbn:"978-1-83962-548-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"e5ba02fedd7c87f0ab66414f3b07de0c",bookSignature:"Dr. John P. Tiefenbacher",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10765.jpg",keywords:"Managing Urbanization, Managing Development, Managing Resource Use, Drought Management, Flood Management, Water Quality Monitoring, Air Quality Monitoring, Ecological Monitoring, Modeling Extreme Natural Events, Ecological Restoration, Restoring Environmental Flows, Environmental Management Perspectives",numberOfDownloads:18,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 12th 2021",dateEndSecondStepPublish:"February 9th 2021",dateEndThirdStepPublish:"April 10th 2021",dateEndFourthStepPublish:"June 29th 2021",dateEndFifthStepPublish:"August 28th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A geospatial scholar working at the interface of natural and human systems, collaborating internationally on innovative studies about hazards and environmental challenges. Dr. Tiefenbacher has published more than 200 papers on a diverse array of topics that examine perception and behaviors with regards to the application of pesticides, releases of toxic chemicals, environments of the U.S.-Mexico borderlands, wildlife hazards, and the geography of wine.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"73876",title:"Dr.",name:"John P.",middleName:null,surname:"Tiefenbacher",slug:"john-p.-tiefenbacher",fullName:"John P. Tiefenbacher",profilePictureURL:"https://mts.intechopen.com/storage/users/73876/images/system/73876.jfif",biography:"Dr. John P. Tiefenbacher (Ph.D., Rutgers, 1992) is a professor of Geography at Texas State University. His research has focused on various aspects of hazards and environmental management. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"65853",title:"A Review of EMG Techniques for Detection of Gait Disorders",doi:"10.5772/intechopen.84403",slug:"a-review-of-emg-techniques-for-detection-of-gait-disorders",body:'\nEMG is an electrodiagnostic technique used to record the electrical activity in skeletal muscles. EMG signals are complex and exhibit intricate patterns that are dependent on the anatomical properties of the muscle [1, 2, 3]. The signal manifests the neuromuscular activation underlying muscle contraction [1, 3]. Therefore, an abnormality in the contraction of a muscle due to an injury, nerve damage, or muscular or neurological disorder that causes motor dysfunction can be identified through EMG signal diagnosis. The motor neuron signal carries information from the CNS aimed for limb displacement by flexing and extending the joints [4, 5]. The dynamic electrical activity of these motor units is called motor unit action potentials (MUAPs). These are super-positioned and recorded by the EMG device [6]. EMG can be recorded using surface electrodes, fine wire electrodes as well as anal and vaginal probes for pelvic floor muscles [2]. A simple model of an EMG signal is given by Eq. (1), where,
Our aim in this article is to review EMG signal processing techniques that facilitate detection of gait and movement disorders. We discuss techniques from simple enveloping to complex computational machine learning algorithms that may help detect alterations in EMG patterns while performing daily life activities. We may note that there are number of highly cited review articles such as Raez et al. [7], and Chowdhury et al. [8], that review EMG processing and classification techniques. The novelty in our review is that in addition to discussing innovative processing techniques we have emphasized their applications, particularly focusing on lower limb disorders. In Section 2, we review the basic techniques such as EMG enveloping, followed by EMG onset/offset detection in Section 3. In Section 4, we review current literature on the decomposition of EMG signals into MUAPs and muscle synergies. In Section 5, we discuss the analysis of the EMG signal in the frequency and time-frequency domain to understand changes due to motor impairment. When working with a larger sample size, a machine learning system can be used to classify subjects with altered muscle activation and abnormal gait patterns [9, 10]. In Section 6, we discuss algorithms that employ supervised and unsupervised learning to detect patterns of gait disorders, followed by a discussion of future trends and conclusion in Section 7.
\nVisual inspection of the raw EMG plot or its envelope requires high dexterity and clinical experience to detect motor impairment. The methodology to obtain the EMG envelope includes preprocessing, signal filtering, rectification, smoothing, standardization, statistical testing, and intricate computational algorithms. Scientific recommendations by SENIAM project and International society of electromyography and Kinesiology (ISEK) suggest use of bandpass filters (10–500 Hz) to reduce aliasing effects when using a sampling frequency of 1 kHz. Intramuscular and needle recordings should be made with the low-pass cut-off set at 1500 Hz. Avoiding notch filter is recommended as it destroys the signal information [2]. De Luca et al. recommended root mean square (RMS) value to compute the signal amplitude of the EMG during voluntary contraction [3]. Methods to form EMG envelopes include moving average, root mean square, spline interpolation over local maxima, integrated EMG etc. EMG envelope can also be obtained from low pass Butterworth 6 Hz filter. Hilbert finite impulse response (FIR) filter computes magnitude of the analytic EMG signal.
\nA decrease in EMG amplitude was visually observable for chronic spinal cord injury (SCI) patients while walking for 3 min [11]. Biceps femoris (BF) and gastrocnemius medial (GM) revealed consistent activity, but that was not the case for tibialis anterior (TA) and rectus femoris (RF). The RMS magnitude of the signal from BF and GM muscles decreased with longer activity duration (10 min) followed by an EMG burst resulting from muscle spasm. Identification of chronic SCI was done by simple visual inspection of the raw EMG [11]. The inter-neuronal degradation was the cause of decreased locomotor performance [11]. The RMS amplitude of the EMG signal using a paired t-test showed a higher duration of muscle activity for BF and TA among cervical spondylotic myelopathic patients (CSM) [12]. The amplitude of the muscle burst activity was not statistically different between the healthy group and CSM [12]. The muscle stretch analyzed from kinematic data did not relate with spasticity, but the ratio of EMG RMS amplitude to the mechanomyogram data showed statistically significant results for healthy and myotonic control groups [12, 13].
\nThe stochastic and nonstationary nature of EMG signals makes it harder to study the innate patterns of the electrical activity of the muscles. Statistical tests such as Pearson’s, Pearson’s r, the Kolmogorov-Smirnov T-test, ANOVA F ratio and t-test, and Wilcoxon Signed Rank Test can demonstrate significant changes in the EMG profiles associated with different behavior [14, 15]. Domingo et al. performed an ANOVA on the normalized EMG amplitude of spinal cord injured patients, which led to the conclusion that with increased speed and no manual assistance the EMG pattern exhibited statistical significance when compared to the control group. The shape and timing of EMG patterns were less similar to controls [16]. Among stroke patients, the EMG activity displayed heterogeneity in comparison with healthy individuals [17]. Nieuwboer et al. [18] demonstrated that raw EMG and its linear envelopes of Parkinson’s patients during freezing episodes displayed abnormal activity of TA and GM. Nonparametric tests on the RMS EMG envelope of the hemiplegic patient showed statistical significance during push off and early stance phase [14]. EMG data acquired from Parkinson patients’ shoulder muscles revealed higher activation than those of healthy control subjects [19]. Average and maximum EMG amplitude were calculated for comparison [19].
\nTraditional statistical testing of the EMG uses ANOVA techniques that may not identify visually differentiable waveform features. McKay et al. [20] developed a more reliable statistical method to find the underlying patterns with the wavelet-based functional test (wfANOVA). Its performance to detect the changes in the magnitude and shape of EMG was more precise than the time domain ANOVA test. Wilcoxon signed rank tests were also used in studies with non-parametric data [12]. EMG envelope extraction using time domain features from multichannel sensors and their statistical tests can assist in the detection of altered myoelectric activity. Specific features such as EMG onset/offset, MUAP etc. can be analyzed from the envelopes for the diagnosis of gait disorders. Figure 1 shows signal envelope extracted from the EMG signal with RMS. MATLAB functions were used to extract envelope and perform a statistical hypothesis test for a healthy individual and other disorders.
\nRMS envelope from a healthy, a myopathic, and a neuropathic patient. A non-overlapping window of 200 samples was used and a paired student t-test revealed statistical significance (p < 0.05) between healthy and neuropathic, and healthy and myopathic conditions. The data was obtained from physionet [
EMG onset parameters define the duration for the muscles to stay active [2]. Onset estimation is useful to diagnose abnormality in muscle coordination. To detect the EMG onset, visual inspection or measurement of nerve conduction velocity may be used [22]. The basic thresholding method for onset detection is sensitive to the type of trials, EMG amplifiers and noise level in the signal. The thresholding based on SD baseline noise can be improved with local peak value. In a study [23], integrated EMG provided more information about early activation. During preconditioning, Teager-Kaiser Energy Operator (TKEO) also improved the onset detection accuracy by constricting the energy of the baseline noise [24, 25]. Staude et al. compared onset detection methods based on the statistical optimal decision threshold [26]. The simple threshold algorithm of Hodges and Bui [26] identifies the onset at a point where the mean of the samples within a fixed time window surpasses the baseline level by a defined multiple of standard deviation [27].
\nThe basic framework of the threshold detection algorithm includes signal conditioning (rectification, filtering, whitening etc.), detection (Test Function and Decision rule), and postprocessing [26]. A block diagram is shown in Figure 2.
\nEMG onset estimation framework;
Double threshold methods are considered better in comparison to single threshold methods [7]. The Bonato algorithm [28] includes pre-whitening filter and data sample squaring in the conditioning unit. The test function is computed between two successive samples from the conditioned EMG signal. The onset point identification is based on the following rules: (1) x out of y samples must exceed the threshold and (2) activation state of the muscle after surpassing the threshold should last for a certain number of samples or duration of time [26].
\nIn Lidierth [29] method, the signal conditioning unit performs full wave rectification. The test function and decision rule are based on Hodges [26]. Additional post-processing rules increase the efficiency of the algorithm. The test function unit detects the onset if the sEMG signal exceeds the threshold. Any decline in the activity below threshold within a defined duration, should not be longer than the defined range of samples [29]. The power spectral correlation coefficient method performs better than TKEO and utilizes the moving average method of Hodges and Bui [30]. The statistical estimation algorithm includes an optimal estimator and approximated generalized likelihood-ratio detector. The statistically optimized algorithms are more robust in terms of signal parameters [26]. Tenan et al. [25] reviewed three classes of standard EMG (linear envelope, entropy, TKEO) and six classes of statistical EMG onset detection (general time series/mean–variance, sequential change point detection with parametric and non-parametric methods, batch change point detection, and Bayesian change point analysis). The Bayesian Change Point analysis algorithm showed higher reliability and accuracy for the singular EMG onset detection.
\nMaximum voluntary contraction (MVC) is a common scaling technique for EMG onset detection. MVC is the largest RMS amplitude a muscle generates in maximum contraction [31]. MVC has a curvilinear relationship with the muscle force production, where less force production amount to muscle weakness. EMG onset on a normalized time series with MVC can help diagnose gait disorders associated with atrophy [2]. Muscle spasticity/co-contraction during tremors among patients with neurological gait disorder exhibited abnormality in EMG onset compared to healthy individuals [12, 32]. EMG envelope indicated alterations in EMG onset for patients with Parkinson’s during freezing episodes [20]. A premature activation of TA and GM muscles before a freezing episode was observed. In gait impairment, due to cervical spondylotic myelopathy, delayed onset and prolonged activation were present [12]. In cerebral palsy earlier onset suppression of EMG within cutaneous muscular reflex is associated with motor dysfunction, which results in inhibitory postsynaptic potentials [33].
\nRaw EMG signal consists of superpositioned motor unit activation potentials (MUAP) and noise components. Muscle crosstalk is a major issue during recording of the biological signals. The crosstalk is dependent on factors such as anatomical site for the placement of electrodes, type of movement, and skin thickness. Since it is harder for sEMG to detect the origin of muscle electrical activity, the chances of muscle crosstalk are higher in sEMG than needle EMG [13]. Besides, low spatial resolution, high movement artifact, and narrow frequency range makes needle EMG more promising as a diagnostic tool in nerve conduction studies for assessing neurological disorders [13]. Changes in the shape of MUAPs, large dynamic range of action potential among motor units and superposition of motor units pose major challenges to decomposing the sEMG.
\nFang et al. [34] decomposed EMG into MUAP by wavelet transform. The technique utilized spectrum matching in wavelet domain as opposed to waveform matching. De Luca et al. [35] proposed a method to decompose the sEMG into MUAP during cyclic dynamic contractions. The algorithm solved two main problems, the first associated with the displacement of the electrode on the surface of the skin leading to alteration in the shape of MUAPs, and second regarding lengthening and shortening of the muscle fibers while undergoing those contractions. The algorithm was an extension of the algorithm by Nawab et al. The process was followed as an extracting time-varying time template parameter, performing time-varying filter analysis, clustering on MUAP trains, shape refinement, test, and decomposition. If the test failed, the iterations were done again for shape refinement of MUAPs. Precision Decomposition I (PD I), which was earlier used to decompose needle EMG data was updated to decompose sEMG and referred as PD (III). An updated approach of PD III reported by Nawab et al. has PD-IPUS (Integrated Processing and Understanding) and PD-IGAT (Iterative Generate and Test) [36, 37]. Another method to decompose sEMG into MUAP trains included a hybrid approach of K-means clustering and convolution kernel compensation method. K-means clustering was performed to estimate the pulse trains, which were later updated iteratively by convolution kernel compensation method [38].
\nThe question arises, what changes may a neurological disorder or injury bring to MUAPs? The features of a MUAP (rise time, duration, amplitude, phases/turns, recruitment and, stability) are vital to diagnosing the cause of abnormality in muscle coordination leading to gait or other movement disorders. A normal motor unit and a motor unit after injury (axonal injury) are distinguishable [32, 39, 40, 41]. MUAPs from needle EMG are not only adequate in diagnosing neuropathy (nerve injury) but can also determine the severity of the neuropathic condition [41]. Abnormal motor units constitute polyphasic potentials, unlike diphasic or triphasic potentials that exist in healthy individuals. Polyphasic potentials are a result of nascent potentials and terminal collateral sprouting [40]. Rodriguez-Carreno et al. [6] reported MUAPs shape abnormality pertinent to the anatomical phenomena shown in Table 1. A study conducted on mice with amyotrophic lateral sclerosis (ALS) using single unit extracellular recording within the spinal cord and EMG revealed gait variability [32]. In ALS mice, the low frequency of motor neuron and irregularities in the motor burst were co-occurring with fractionated EMG.
\nMUAP abnormality | \nAnatomical relation to changes | \n
---|---|
Increased amplitude | \nIncrement in connective tissues, loss of muscle fibers | \n
Decreased amplitude | \nMuscle fibers grouping | \n
Decreased duration | \nLoss of muscle fibers | \n
Increased duration | \nIncreased muscle fibers | \n
Increased spike duration | \nVariation in muscle diameter and increased endplate thickness | \n
Increase in number of turns and phases | \nSlow conduction of terminal axons/increased diameter of muscle fiber and end plate | \n
Increase in firing rate | \nLoss of motor units | \n
Increase in the jiggle | \nAtypical neuromuscular transmission | \n
MUAP abnormalities and indicated anatomical changes.
Among patients with myopathy, short, small, long duration, polyphasic and early recruitment of MUAPs were observed [39]. Different myopathy disorder studies in relation to MUAP trains were conducted using needle EMG by Paganoni et al. [39]. In early phases of disorders due to loss in muscle fibers the compound muscle action potential amplitude is lower. The result was short, small and early recruitment of MUAPs, but in Lambert-Eaton Myasthenic Syndrome, higher CMAP amplitude was observed. The shapes of MUAPs also alter with chronicity. Instead of positive sharp wave and fibrillation in the needle EMG, a mixture of long and short duration of EMG is prevalent [39]. Use of sEMG in comparison to needle EMG for postural disorder is preferable. sEMG is very good at detecting kinesiological disorders such as myotonia, myoclonus and tremors [13]. It can further be decomposed into MUAPs with the PD (III) algorithm, or hybrid of K-means and convolution kernel compensation method.
\nLinear decomposition of multi-source EMG signal is another method to diagnose the alteration in EMG patterns of patients with gait disorders [5, 42]. The muscle synergy hypothesis can be employed to understand better the physiological aspects of gait disorders using a number of linear decomposition algorithms such as principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), and non-negative matrix factorization algorithm (NNMF). Each algorithm is unique and extracts the synergy structure based on the assumption made on the synergy (e.g. orthogonality, non-negativity, statistical independence, etc.). After applying the factorization algorithm, the multi-electrode EMG signal is decomposed into the activation coefficients and synergies. The synergy vectors from the healthy group can be compared with a group suffering from the neurological or non-neurological disorder [43]. Statistical tests including cosine correlation, Pearson correlation or cluster analysis are generally used to compare the similarity and alterations in synergy structures [44, 45]. The application of a clustering algorithm for diagnosing gait disorder is discussed in a later section. Patients with thoracic spinal cord injury revealed lesser modules, higher co-contraction and, less directional tuning in relation to healthy individuals [46]. It is likely that the number of dimensional space was affected due to the choice of preprocessing [47]. A review cum research by Kieliba et al. [47] supported that increase in the cut off frequency of the filter decreases the variance, accounts for a particular component and increases dimensional space of synergies to be extracted. EMG acquired from children with cerebral palsy and from individual’s post-stroke has shown that the choice of preprocessing (filtering, normalization) had an effect on the number of synergies and differentiation of physiological traits [48, 49]. Figure 3 displays how the choice of low pass filter (10 and 20 Hz), a second-order Butterworth filter, effects the dimensional space. Filters are generally used to remove movement artifact. The principal component variance is higher for 10 than 20 Hz.
\nA variance threshold ≥0.9 reveals five synergies for 10 Hz low pass filter and four synergies for 20 Hz low pass filter for 9-channel EMG data.
From a neurophysiological perspective, the recruitment of fewer spinal modules during movement is due to the loss of supraspinal inflow that results in simple muscle coordination (neuroadaptation). In upper extremities, the neuroadaptation was similarly perceived in the form of changes in the dimensional space of muscle synergy structures. Alteration of synergy structures was also present in patients with chronic stroke (upper extremity), and cerebral palsy [42, 43, 45, 50]. The linear envelopes extracted from the EMG data are subjected to MS extraction. The synergy hypothesis is well suited for capturing the physiological aspects of motor impairment [19]. In chronic stroke, merging and fractionation of synergies were observed. Merging of muscle synergies results in poor muscle coordination. In children with cerebral palsy, the dimensional space was smaller than it was in the control participants (unimpaired) [42]. However, the modules for cerebral palsy were higher for Duchenne muscular dystrophy (DMD) and typical developing (TD) children [43]. Rodriguez et al. revealed that fewer modules were recruited while walking on treadmill among Parkinson’s patients. Thus, the size of dimensional space is crucial for the assessment of gait disorder such as cerebral palsy and Parkinson’s [51, 52]. It is also important to properly choose preprocessing before analyzing the synergies as the dimensional space is sensitive to the preprocessing methods.
\nEMG power spectrum estimation methods can be categorized into parametric and nonparametric techniques. The spectral methods include fast Fourier transform (FFT), multitaper analysis and short-time Fourier transform (STFT) and wavelet transform. The difference between FFT and Wavelet Transformation is that FFT is localized to the frequency domain whereas the latter is localized to time-frequency analysis. Hu [53] recorded cortical and spinal somatosensory evoked potential (CSEP and SSEP), cortical motor evoked potential (CMEP) and spinal cord evoked potential (SCEP). The short time Fourier transformation was applied to the CSEP signal with a Hanning window [53]. The results revealed that the time-frequency analysis is a better marker for spinal injury than time domain analysis. The peak power after spinal injury had lesser energy with more dispersion in time-frequency scale.
\nThe EMG time series signal can be analyzed in the frequency domain for the diagnosis of gait disorders. The frequency spectrum for EMG signals is in range of 0–500 Hz [54]. The FFT algorithm [55] computes the discrete Fourier transform (DFT) of EMG signal more efficiently. The FFT decomposes the EMG signals into periodic sine and cosine waves. We computed the FFT of EMG signal recorded from the Vastus Medialis (VM) during walking (Figure 4).
\n(A) sEMG signal from VM during walking in time domain; (B) frequency domain representation of the signal using FFT.
The FFT allows computation of power spectra by squaring of FFT’s magnitude [56]. In Parkinson disease, the spectral power of the signal has lower amplitude for the usual tremor than for the unusual tremor, which has peak amplitude of 4–6 Hz during an atypical tremor [15]. The signals associated with nonperiodic tremors are differentiable with FFT [57]. The EMG signal from neuropathic patients with SCI also exhibited distinct power spectrum density and amplitude in comparison to healthy individuals [58]. The application of FFT to the EMG envelope revealed muscle burst discharge in frequency domain ranging from 4 to 7 Hz [15]. Average power spectra computed from fractionated EMG of ALS mice by FFT was significantly higher than the control group. In the ALS group the spectra were skewed towards higher frequency content but single unit recordings revealed the absence of higher motor neuron (MN) frequencies or shortening of MN frequency in ALS mice [32], due to small type firing neurons improperly increasing firing frequency. This phenomenon results in co-contraction thus producing fractionated EMG. Co-contraction in muscles can also be observed in spinal cord injured patients [32]. In a study, EMG signals from lower limbs of dystonic and non-dystonic participants while walking were recorded. The non-dystonic participants were also patients suffering from other gait disorders. The power spectral density was computed using FFT with the Welch method of 50% overlap. The median power frequency (MdPF) and total power in low frequency were calculated for each muscle. The results revealed that MdPF for dystonic muscles had shifted to low frequencies and a concurrent increase in total power percentage in low-frequency range was observed [59]. Thus, frequency analysis of EMG signal not only provides us with distinction between normal and abnormal gait behavior but also specific gait abnormalities can be distinguished.
\nShort-time Fourier transformation (STFT) is used to analyze a nonstationary signal in the frequency-domain. The signal is sliced and subjected to Fourier transform. Segmenting the signal is called time domain windowing, and the time localized signal is defined by \n
Mitchell et al. [60] used cross time-frequency analysis to diagnose hypertension of the GM muscle. The study included 57 elderly people with 10 younger adults. Reduced Interference distribution (RID) was utilized to remove cross terms implementing time smoothing window and frequency smoothing window. A Hanning frequency smoothing window was chosen. In the study of gait, it is necessary to consider a time-localized cross-correlation between two signals, such as left and right muscle groups responsible for gait [60]. Hence, cross Wigner distribution (CWD) was selected to preserve the phase information. The results revealed statistical significance for several time-frequency parameters of sEMG between control group and persons with neuropathy, diabetes, osteoporosis, and arthritis patients [60]. STFT does not adopt an optimal time window or frequency resolution for non-stationary signals [7]. For the implementation of FFT and STFT the signals are considered to be stationary [8]. The problem or resolution can be overcome by continuous wavelet transform (CWT) [8]. Multitaper analysis is another and perhaps more efficient method for power spectral analysis to deal with non-stationary signals [61, 62].
\nWavelet transform such as Multitaper is well suited for non-stationary signals. Wavelet transform elicits good localization of energy when the MUAP shape matches that of the wavelet [8]. Continuous wavelet transform (CWT) of bandpass filtered EMG showed alteration in the motor unit among stroke patients when a foot drop stimulator device was used (FDS) [63]. Energy localization below 100 Hz that resulted from foot drop was caused by slow motor unit recruitment. The neuromuscular activation improved with FDS. The time-frequency plot for Gastrocnemius showed that peak energy localization shifted from 50 to 100 Hz as a neuromuscular strategy [63]. Instantaneous mean frequency (IMNF) is the average frequency of power density spectrum of a signal and is computed from time-frequency distribution, W(
In the above,
Time and frequency domain features of the EMG signal may be used to diagnose gait disorders. For example, an image processing technique can be used to detect pathological gait affected by abnormal firing of MUs [65]. Machine learning algorithms are important tools in detecting the pattern of normal and abnormal gait [66, 67]. They do so by making minimum assumptions about the data generating system, as it does not need a carefully controlled experimental design [9]. Application of machine learning algorithms to detect and classify gait disorders is suited to big data. Machine Learning is further divided into: (1) Supervised learning and (2) unsupervised learning. We will now discuss techniques to detect gait disorders using supervised and unsupervised learning algorithms.
\nUnsupervised learning can be used to find structures in the EMG data. For example, cluster analysis has been used to identify alteration in the gait patterns, which are undetected by statistical tests. Patients with Parkinson’s disease can be distinguished from a healthy individual by using cluster analysis of dimensionally reduced feature vector [68, 69]. K-means clustering is a very common clustering technique that initially estimates K centroids randomly or selectively. The algorithm iterates between two steps, data assignment steps and updating centroid. The aim is to minimize objective function, which is given by (5).
where
The hypothesis of muscle synergies has been applied in several studies [44, 45, 70]. Unsupervised Learning helps in grouping identical synergies and can be helpful in diagnosing gait disorders. Kim et al. [70] identified synergies using iterative
A total of four clusters were chosen to group sEMG signal based on 93% variability in data within each cluster. The clusters were plotted for the first two principal components for walking with and without constraint.
In supervised learning, the predictive models are based on the input and output data. Some of the widely used learning algorithms are decision trees, Bayesian networks, support vector machine, artificial neural networks, and linear discriminant analysis (LDA). After feature extraction and classification, the EMG time series can be modeled to control prosthetic or rehabilitative device. The fundamental approach to classification of EMG signal is shown in Figure 6 [66].
\nBlock diagram of an EMG Signal classification system.
The performance of different algorithms (SVM, LDA, MLP) in classifying gait disorders (Cerebral Palsy) was compared [74]. SVM classifier, compared to LDA and MLP, performed better when the analysis was done on kinematic data [74]. The normalization of the EMG data from different limb configurations increased classification accuracy [74, 75]. Feature level fusion is used to extract the feature space from daily life activities [73]. Patients with Parkinson’s were classified with high accuracy using SVM with leave-one-out cross-validation [75]. Results from Nair et al. [76] suggest that least square kernel algorithm performed better than LDA, Neural Network, MLP and learning vector quantification (LVQ) for patients with arthritis. Decision Tree (DT) classifier used to classify toe walking gait disorder revealed three major toe-walking patterns [77]: (1) muscle weakness of TA and quadriceps and spasticity of Tibialis Surae; (2) severe spasticity of Tibialis Surae with limited range of ankle motion; and, (3) hamstring spasticity. The MLP, on the other hand, exhibited higher accuracy while classifying gait disorders associated with myopathy and neuropathy. Based on the literature studied, normalization, feature extraction and selection are important steps for accurately classifying gait disorders [75, 76].
\nArtificial neural networks (ANNs) are considered better at discovering nonlinear relationships in data. Ozsert et al. [78] classified biceps, frontalis and abductor muscles using ANN. The authors used wavelet transform for pre-processing the sEMG signal and an AR model to train the ANN. Senanayake et al. [79] used EMG RMS value and soft tissue deformation parameter (STDP) extracted from the video recordings to train a feed-forward-backward propagation neural network (FFBPN) to identify gait patterns. The proposed evaluation scheme improved classification accuracy between healthy and injured subject’s gait patterns as Vastus Medialis and Lateralis revealed higher positive correlation between EMG and STDP for healthy individuals [79].
\nAn adaptive neuro-fuzzy inference system (ANFIS) successfully diagnosed neurological disorders [8, 80]. In a number of studies, ANN and SVM worked well in diagnosing the gait pathology [7, 8, 71, 81]. Naik et al. [82] decomposed needle EMG from brachial biceps with ensemble empirical mode decomposition (EMD). The authors used Fast ICA and LDA classifier with majority voting to diagnose healthy participants from ALS, and myopathic individuals [82]. The algorithm of Naik et al. [83] for walking, sitting and standing tasks, achieved 86% classification accuracy for participants with and 96% without knee pathology. ICA via entropy bound minimization, time domain feature extraction, and feature selection with fisher score were performed prior to LDA classification. Ai et al. [30] used fused accelerometer and EMG data to discriminate among four participants including an amputee; more amputees in the study could provide better insight of the suggested technique [30].
\nThere is no perfect machine learning algorithm to detect gait disorders. Signal processing techniques for feature extraction and selection, and standardization of the time series play a crucial role in enhancing classification accuracy. We also see consistent improvement in the existing models with increased classification accuracy [84]. ANN classifier has some deficiencies, such as high training process time and overfitting. Extreme Machine Learning algorithm (EML) improves on these anomalies at no cost to classification accuracy [8]. SVM accuracy was low for eight daily life activities including falling. The accuracy for detecting trip fall improved with weighted genetic algorithm [73]. A wide variety of time domain, frequency domain, and time-frequency domain features, and optimization techniques provide multiple options to enhance the classification accuracy of gait diagnosis. The performance of each algorithmic class discussed in this review with respect to the abnormal physiological condition is shown in Table 2.
\nClassifier | \nAuthors | \nYear | \nConditions | \nClassification | \nPerformance | \n
---|---|---|---|---|---|
Neural networks | \nSenanayake et al. | \n2014 | \nSoft tissue deformation | \nGait pattern identification between healthy and injured | \nAccuracy = 98% | \n
\n | Nair et al. | \n2010 | \nOsteoarthritis | \nEMG of healthy and osteoarthritis | \nAccuracy = 89.4 ± 11.8% | \n
\n | Nair et al. | \n2010 | \nRheumatoid arthritis | \nEMG of healthy and rheumatoid arthritis | \nAccuracy = 57 ± 1 8% | \n
\n | Kamruzzaman and Begg. | \n2006 | \nCerebral palsy | \nGait pattern identification using stride length and cadence | \nAccuracy = 94.87% | \n
LDA | \nNaik et al. | \n2018 | \nKnee pathology | \nMovement classification for healthy and patients with knee pathology | \nAccuracy = 86% (Unhealthy) and 96% (Healthy) | \n
\n | Nair et al. | \n2010 | \nRheumatoid arthritis | \nEMG of healthy and rheumatoid arthritis | \nAccuracy = 72 ± 20% | \n
\n | Ai et al. | \n2017 | \nNormal and amputated | \nMovement-based classification for normal and amputee subject | \nAccuracy = 95.6 ± 2.2% | \n
\n | Kamruzzaman and Begg. | \n2006 | \nCerebral palsy | \nGait pattern identification using stride length and cadence | \nAccuracy = 93.59% | \n
SVM | \nKamruzzaman and Begg. | \n2006 | \nCerebral palsy | \nGait pattern identification using stride length and cadence | \nAccuracy = 96.8% | \n
\n | Kugler et al. | \n2013 | \nParkinson | \nDifferentiate between healthy and Parkinson patients by auto-step segmentation | \nSpecificity = 90% and Sensitivity = 90% | \n
\n | Ai et al. | \n2017 | \nNormal and amputated | \nMovement-based classification for normal and amputee subject | \nAccuracy = 98.1 ± 1.6% | \n
\n | Xi et al. | \n2018 | \nFall | \nGait recognition for daily life activities including Fall | \nAccuracy = 100% | \n
Decision tree | \nArmand et al. | \n2006 | \nToe Walking disorders | \nIdentification of ankle kinematic patterns for toe walkers | \nAccuracy = 81% | \n
Least square Kernel Algorithm | \nNair et al. | \n2010 | \nRheumatoid arthritis | \nEMG of healthy and rheumatoid arthritis | \nAccuracy = 91% | \n
\n | Nair et al. | \n2010 | \nOsteoarthritis | \nEMG of healthy and osteoarthritis | \nAccuracy = 97% | \n
EMG classification methods.
The computational methods reviewed in this study have evolved over several decades and continue to do so. For example, ANOVA test’s inability to detect visually observable waveform due to abnormal gait behavior had been improved with wfANOVA test [20]. Apart from factorization algorithms and PCA, artificial neural network were implemented for synergy extraction [5]. New time and frequency domain features and hybrid methods for feature selection have been developed and introduced over the years [67]. In these examples, the conventional techniques were enhanced or detection of gait disorders. There is a consistent effort to augment current computational techniques and improve the EMG based detection methods for motor behavior abnormalities. Optimization algorithms, feature level fusion, and advances in computational methodology point to a future for detecting intricate EMG patterns EMG associated with abnormal gait behavior in machine learning. Recently, application of deep learning algorithms to detect abnormal EMG patterns appears more promising [85], and performs well with EMG acquired directly from the muscles. The main issue in clinical application of deep learning is its real-time implementation. The development of powerful graphics processing unit (GPU) and faster training algorithms will likely resolve such issues in near future.
\nIn conclusion, in this article we reviewed the existing literature on EMG processing techniques from simple thresholding to complex computation algorithms and their application in detecting gait disorders. The pros and cons of the techniques discussed are summarized in Table 3. Besides discussing these techniques in detail, our study cites pertinent literature where these techniques were successfully used to detect gait abnormalities. This study clearly points towards the recent trend in assessing gait disorders from EMG data using an intelligent system. Examples of such systems using supervised and unsupervised learning were also reviewed.
\nEMG method | \nPros | \nCons | \n
---|---|---|
Visual inspection of raw EMG | \n\n
| \n\n
| \n
EMG envelope/onset detection | \n\n
| \n\n
| \n
Frequency and time-frequency analysis | \n\n
| \n\n
| \n
MUAP decomposition | \n\n
| \n\n
| \n
Muscle synergy decomposition | \n\n
| \n\n
| \n
Pros and cons of EMG processing techniques discussed.
Children of parents with a mental illness face childhoods that can be full of challenging experiences, threatening their quality of life, development and long-term outcomes [1, 2, 3, 4]. However, these children are not an officially recognised group in the UK, and data and statistics are not gathered about them. While UK policies recognise the needs of young carers, they do not address the specific challenges experienced by children whose parents have a mental illness. This is not the case in other countries; in Australia, these children are officially known as children of parents with mental illness (COPMI) and as “young relatives” in most Nordic countries. Children of parents with a mental illness remain a hidden group in the UK, and many are reluctant to identify as young carers due to the shame and stigma often associated with mental illness, making them vulnerable and at risk of neglect.
\nThe UK Children’s Commissioner Vulnerability Report (2018) found that in an average classroom, eight children have a parent with mental health problems—this is the equivalent to 25% of the UK school population [3]. In 2018, Our Time, a UK charity that advocates for and offers support to this group did an analysis of the existing data (supported by a team from Ernst and Young), which found that in excess of 3.4 million children and young people in the UK are currently living with a parent with a mental illness [5]. Further evidence indicates that, without support, 70% of these children are likely to go on to develop mental health problems themselves. With two ill parents, there is a 30–50% chance of the child developing a
In Germany, where Our Time’s partners, the “KidsTime Netzwerk”, use the KidsTime Workshop model to support children and families, research has identified 3.8 million children affected by parental mental illness [9].
\n\n
In excess of 3 million children in the UK live with a parent with a mental health issue.
Average of 8 children in an average classroom will be in this situation.
20–25% of the school population.
70% likely to develop a mental health condition.
Parental mental illness is one of the 10 adverse childhood experiences (ACEs), which has a lifetime impact on both physical and mental health.
Parental mental illness (PMI) is a root cause of many other ACEs.
WHO identifies PMI as one of the most important public health issues of our generation.
Intervention late after the onset of an ACE is less likely to be effective. Rising thresholds for acute support are exacerbated by significant reductions in early intervention spending by local authorities.
By focusing on clinically diagnosable mental illnesses, the children and adolescent service (CAMHS) interventions are too late to address ACEs.
In 2018 the Children’s Commissioner reported that despite the new provisions in law, 4 in 5 young carers were not identified.
Research into adverse childhood experiences, known as ACEs [10], identifies parental mental illness as one of the ten most powerful sources of toxic stress in young people. The presence of mental illness in a parent is known to negatively impact a child’s cognitive and language development, educational achievement and social, emotional and behavioural development [2, 3, 4, 10]. It can lead to anxiety and guilt coming from a sense of personal responsibility. Where there is severe mental illness in a parent and no second parent who is well it can lead to neglect or abuse. These children are also at greater risk of bullying, a lower standard of living and financial hardship [2, 3, 4, 5, 9].
\nFigures 1 and 2 show the lifetime impact of adverse childhood experiences affecting the mental and physical health of the individual as a result of toxic stress.
\nThe ACE pyramid (Centers for Disease Control and Prevention,
Long-term effects of ACEs (Centers for Disease Control and Prevention,
The hidden status of these young people in the UK means that they have no statutory entitlement to specific support related to parental mental illness. Provision of formal, organised support or targeted intervention is therefore at the discretion of local funding bodies or entirely dependent on the voluntary sector. Any informal support is dependent on the awareness and understanding of professionals coming into contact with these children to identify and support their needs. However, this sometimes requires stepping outside of the remit of current practice and expertise, adding an additional “burden” to already high workloads. Additionally, many professionals report worrying about talking to children in this situation, as they are concerned about “
This chapter will explain the impact of parental mental illness on children and the associated risk factors. We will provide examples of approaches proven to help children in this situation, using the KidsTime model as a case study. We will describe the approaches and methods of this practice model and explain how a combination of family therapy and systemic therapy approaches, together with drama, can create an effective multi-family therapy intervention. We will provide evidence of the impact of the KidsTime model and highlight some of the barriers to securing investment for preventative approaches. The chapter will conclude with recommendations for practice.
\nThis section outlines some of the common difficulties experienced by children and young people who have a parent with a mental illness. These include but are not limited to:
\nResearch, using case studies and personal testimonies, depict the kinds of difficulties experienced by children and young people growing up in a family where there is a parent with a mental illness. For example, it is common for children, particularly younger children, to report experiencing the same symptoms as their parents, i.e., symptoms caused by the parent’s diagnosis, such as delusions [12]. explains this can be due to the parent’s illness limiting their emotional availability to their child. Both symptoms of the illness and side-effects of the medication can result in emotional withdrawal from the child, which the child typically perceives as rejection. The child therefore intensifies his or her attempts to achieve closeness with the parent, which may cause the parent to withdraw further. Not only does this create a vicious cycle of interaction between the parent and the child, but these attempts can expose the child to further risk, such as the distress of being drawn into the parent’s psychopathological symptoms that are not their own. This is particularly likely in the absence of a sufficient explanation of the parent’s mental illness that could enable the child to differentiate between behaviours caused by the illness and those that are not [12, 13].
\nThe experience of living with a parent who has a mental illness often means that the child or young person often adopts caring roles in their family, which are not age-appropriate. They may fill any gaps in their parent’s role, which the parent is not consistently able to fill themselves due to their illness. This is the case both when the parent is markedly unwell and thus genuinely less able and also when the parent is able, but the child has become used to fulfilling this role or does so in anticipation of the parent’s next period of illness. The young person may care for their parent and other family members practically, through assuming responsibility for structuring the daily life of the family, fulfilling siblings’ needs or household tasks, but also emotionally, in that their mind is occupied by issues related to their parent’s wellbeing [12, 13]. These children also experience frequent role reversal, as they help their parent manage symptoms of their mental illness, such as emotional distress or behavioural difficulties. This often leads to
The long-term impact of such experiences can be that children in this situation gradually form a view of the adults around them as having limited capabilities and therefore do not trust or expect adults to meet their needs. The responsibilities they believe they must fulfil themselves are a large burden for a young person to carry. These young people will often experience feelings of guilt in taking over the parent’s role and inadequacy, while trying, and inevitably failing, to navigate such unrealistic responsibilities. This can also negatively impact their own self-esteem and sense of self-efficacy, and they may start to question their capabilities in other spheres of their life, which also has an adverse effect on their wellbeing. This combination of taking responsibility for others and worrying that they are not up to it is often carried into later life and causes hidden stress and sometimes prevents them from fulfilling their full potential [14, 15].
\nChildren of parents with a mental illness and their families suffer from the shame and stigma surrounding mental illness in multiple ways [9, 14, 16]. It hinders communication about mental illness and emotions more generally within the family. It also hinders communication and the development of supportive bonds outside of the family, i.e., with extended family, community and other social networks. This leads to feelings of isolation and withdrawal from social interaction [9, 14]. As a result, many children of parents with a mental illness feel very different to their peers:
\nSuch shame, stigma and isolation, combined with children’s imagination, means many of these children live with damaging fears and/or misconceptions about mental illness. For example, they fear they will “catch” their parent’s illness, that they are predetermined to developing it themselves, or that they caused the illness or its symptoms [15, 16]. The shame, stigma, fear and isolation further decrease the likelihood that they will ask for help, advice or information that would reassure them and enable them to make sense of their situation and develop strategies for coping with it.
\nThe KidsTime model is built on three principles in its work with children and families affected by parental mental illness and will be described in more detail in the next section [2, 11].
Having a good explanation
Having a trusted adult to talk to
Knowing you are not alone
Many children affected by parental mental illness report receiving little or no information or explanation about their parent’s illness. Even at the point of hospitalisation, only ~1 in 3 young people receive any information about their parent’s situation [17]. Not having an explanation or not understanding what is happening can be an unsettling experience in itself. However, young people who have been given an explanation often identify this as a key factor in helping them to cope with their situation. Receiving an explanation about their parent’s mental illness could make a significant difference in helping affected children to feel more in control of their situation. It could also mitigate the impact or even prevent the development of frightening misconceptions about mental illness and the confusion and self-blame many young people feel about the origins of the illness and its symptoms. This would enable children to differentiate between their parent’s “ill” and “non-ill” behaviours and thus also decrease the likelihood of adopting any of these behaviours themselves [13]. Having a good explanation is one of three protective factors identified by international research as key in building resilience for children whose parent/s have a mental illness.
\nThere is a lack of specialist support for children affected by parental mental illness in the UK. These children may cross paths with multiple services, such as health services, children’s social care, schools or professionals directly involved in their parent’s psychiatric or social care. However, these professionals do not have the awareness or understanding of the unique experiences of children living with, or caring for, a parent with mental health issues and also often lack confidence in speaking to children about mental illness. The negative impact of this is twofold: Firstly, it reinforces these young people’s disillusionment with adults as protective or supportive figures. Secondly, these young carers remain under the radar and are therefore unlikely to receive a satisfactory explanation or helpful support. However, the potential harm and many of the risks associated with having a parent with a mental illness can be addressed by training adults to provide good, child-friendly explanations and appropriate support, which increase the protective factors and develop the child’s resilience, examples of which will be given in the following sections.
\nAdverse childhood experiences (ACE) have recently become the focus of research and public discourse. However, despite its official recognition as an ACE, parental mental illness has been somewhat overlooked in this debate, and there is no recognition or provision for children affected by parental mental illness in England.
\nOur Time is a UK charity that was set up to advocate on behalf of this group through raising awareness of the issue and developing specific support through the KidsTime Workshop approach, which has been adopted across the UK, Germany and Spain. These are multi-family support groups that combine systemic family therapy approaches, drama and play to provide families with the three protective factors outlined above. There are currently 12 KidsTime Workshops operational in England, supporting up to 250 children and their families.
\nKidsTime Workshops take place once a month, after school, for ~2.5 h, and are run by a multidisciplinary team of at least three members of staff. The model requires the following critical staff members:
Clinical Lead, with a clinical background working in mental health services (often a psychiatrist or clinical psychologist or family therapist)
Drama Lead, who has experience in creative and drama-led group work with children
Logistical Lead/Coordinator, responsible for managing referrals, engaging and supporting families to attend the workshop and logistics (venue, equipment, transportation, etc.)
The group begins with all staff and families, (typically 6–10 families per workshop), coming together for a playful activity, followed by a seminar-style session that explores a single topic related to (parental) mental illness. The Clinical Lead facilitates this session using informal discussion and playful activities. Importantly, the particular topic will have been identified by the families themselves as something they want to discuss, for example, what to do in a crisis.
\nThe KidsTime Workshops have developed a model for explaining mental illness to children. Explanations are provided by the Clinical Lead, which is relevant to the seminar topic (i.e., not at every workshop). The Clinical Lead will employ visual aids and clear, simple and child-friendly language to describe how the brain works and how it can become “overloaded” as well as other aspects of mental illness (e.g., side-effects of medication) without being a diagnosis specific. An example of this can be seen in the videos, “
After the seminar, the families separate into two groups, one for adults and the other for children, which run in parallel for 1 h. The children’s group is facilitated by the Drama Lead. It starts with group games to help the children relax and focus, followed by drama work during which the young people create, rehearse, perform, and film a dramatic scene. The drama content will often be related to the seminar topic, but it is important that the children are free to set and interpret the topic themselves. The drama allows the children and young people to address issues of interest or concern without having to expose their own personal situation, giving them a voice and a way to explore different perspectives and reactions to difficult family issues.
\nThe adult group consists of the parents or carers (sometimes guardians, grandparents or close relatives), with or without a mental illness, and explores their experiences of being a parent with a mental illness or supporting the family in which this is the issue, sometimes using the seminar topic as a starting point. The discussion is facilitated by the Clinical Lead who ensures that the experiences and needs of the children are a central focus. The adult group provides an opportunity for parents to talk more openly about their own experience and the challenges of parenting with a mental illness in a non-judgemental environment and to receive support and encouragement from one another.
\nThe children, parents and staff reunite after their respective groups for 30–45 min. First, everyone takes a break and shares food together (traditionally pizza because the children like it and it is easy to prepare). Then, everyone watches the film of the young people’s drama, which leads to a collective group discussion about what the drama communicates and what insights the children and young people have demonstrated in their dramatisation. The parents contribute to the discussion by sharing a summary of their group discussion and their own reflections from watching the drama.
\nWhile the KidsTime Workshop model draws on some therapeutic methods and techniques, KidsTime is not designed as a form of therapy, but it is therapeutic in its effects. The design aims to create a community where the families can safely share their experience and knowledge and are listened to and able to ask the questions they need to ask without fear of judgement or having solutions imposed on them. The aim is to provide information, support and some relief to the families through a social intervention, while children and their needs remain the focus. Cooklin et al. state that an explicitly therapeutic intervention directed at the children may lead to the child seriously misjudging their predicament and adding to the sense that they (the child) are the problem and encourage further mistrust in adults [16] because they are not taken seriously. Firstly, the offer of therapy to the child may be falsely perceived as confirmation that they, like their parents, are going to develop a mental illness. Secondly, as these children will often adopt responsibilities beyond their years, in nature and volume, there is a risk that the child or young person would conclude that they are somehow failing to solve the problem or feel dismissed and undermined, if treated as a passive recipient of therapy. Therefore, the approach of professionals should aspire to take the role of an understanding, friend/mentor or relative rather than the formal and inevitably hierarchical role in which a therapist may be perceived.
\n“
“
This section outlines some of the key approaches employed by the KidsTime model to achieve the desired protective factors, particularly and uniquely, an age-appropriate explanation of mental illness, its treatments and impact.
\nThe model views and encourages families to appreciate the systemic contributors to experiences; that the experience of each individual in the family results from their relationships with other members of the family; and what their feelings and thoughts about these relationships are. Based on this, the individual forms their view of themselves and perceptions of others. Bringing the whole family together to think about their situation and find ways of managing their lives in the context of the illness is one of the innovative and most powerful aspects of the model.
\nThe KidsTime model recognises and aims to counter the potentially damaging effect of parental mental illness on the quality of social interactions within the family and with the wider social environment and support networks (other families and services, etc.) including social care providers, teachers and even the school. It aims to do so through facilitating communication between family members, with the focus of helping them understand the role of each person and the impact of parental mental illness on them. The model aims to promote social ties and trust between family members, neighbours and the general social world within which the family is located.
\nIn general, families develop different patterns of internal communication and sharing of experiences. In families affected by parental mental illness, there is often little or no communication about the mental illness, due to shame and stigma, and a lack of understanding about mental illness [15, 16]. KidsTime Workshops aim to combat this stigma and social withdrawal by encouraging families to speak more freely about mental illness and finding creative ways to make this easier. Adapted systemic therapy methods, such as sculpture work, are used to help families visualise relationships and patterns of communication; this facilitates mutual reflection and discussion in the group helping them to identify their current patterns and how to develop healthier ones [18].
\nWhile the effect of parental mental illness on the children is the overarching focus of the parent and children’s groups within the KidsTime model, parents’ reactions to the impact of their illness are also actively discussed and considered. This results in children communicating their experiences to, and receiving feedback from, their family and the wider group (and vice versa), leading to a multi-systemic perspective rather than one-direction linear communication. This also leads to group interactions in which everyone is considered on the same level and equally able to contribute to discussion, thereby recognising the young people’s knowledge and experience and the roles they perform within family life.
\nAlso consistent with systemic approaches, the KidsTime model puts special emphasis on recognising and promoting families’ capabilities. Families are respected as autonomous, self-organising systems and capable experts in their own situation. Within this, particular efforts are made to appreciate the young people’s knowledge and expertise in their parent’s mental health. Indeed, young carers will often notice signs of crisis or decline in their parents far earlier than the parent themselves or professionals. However, for a number of reasons that can be very frustrating and damaging for the child, this expertise is often invalidated in their interactions with the adults around them. Children and young people express frustration that they are often the closest observer of the parent and have responsibilities beyond their years and yet are not consulted, listened to, and frequently talked over by professionals. This combination of shouldering adult responsibility and being treated as a child who has no information or insight is particularly difficult and leads to mistrust and resignation on the child’s behalf, adding to the notion that they are on their own with the problem and that adults cannot be relied upon, which leads to hyper-independence. The KidsTime model aims to be realistic about the different family situations and challenges and to support and empower affected young people within their roles to develop appropriate coping strategies that will help them to understand and manage their own situation rather than “fixing” the problem for them and importantly knowing what to do in a time of crisis and developing a network of people to whom they can turn to for help when they notice that their parent’s mental health is deteriorating. This means that awareness raising and the education of professionals is a key factor in supporting these children and young people.
\nMulti-family work is based on systemic approaches; it aims to combine the benefits of single-family therapy with group therapy while still encouraging the agency of all individuals participating.
\nThe coming together of families in similar situations has multiple benefits, particularly when the shared experiences are as stigmatised and hidden as those related to parental mental illness. It enables affected families to discuss mental health issues without one child, parent or family feeling exposed, judged or different. It is also crucial that facilitators do not single anyone out. The KidsTime Workshop model encourages openness and reflection, and, through conversations about mental illness and common experiences, it reduces the often-associated stigma and shame-induced isolation. Unlike in the outside world, at KidsTime, the individuals and families are no longer the odd ones out:
\nMulti-family work, in this context, is intended to enable solidarity and a sense of community between families, a sense that “we are all in this together”. The individual family is viewed as part of the wider system of multiple families—a system that all families contribute to and benefit from. The families build a social network and mutually support each other. One of the most powerful ways in which this happens is the socialising and exchanging of experiences, ideas and advice facilitated by the multi-family model. In the KidsTime Workshop, families use each other as resources. Sharing in a multi-family group means they learn from each other’s experiences and perspectives and are empowered to make changes themselves. In this sense, the multi-family model is intended to contribute towards helping families to help themselves; it allows individual parents and children to hear both positive and corrective responses from other adults and children, which may be both more acceptable and meaningful than comments from professionals [16, 18].
\nActively involving families in discussion of similar problems in other families strengthens the self-esteem and agency of all involved. When experiencing difficulties, people tend to develop rigid and narrow ways of problem solving but are still often able to offer useful ideas to others in similar situations. Drawing on the expertise and experiences of families in similar situations leads to families viewing themselves more positively, as more capable. This strengthens self-esteem and the family’s sense of agency and for the adults, in particular, a sense of pride as capable parents. In turn, this may enable families to become more resourceful and creative in finding solutions for their own difficulties [18]. Thus, the group becomes more powerful than any single therapist.
\nMethods of creative therapy and drama work are powerful tools in creating a playful attitude and a relaxed, light-hearted atmosphere. This facilitates young people to have fun and foster positive relationships with each other and their families. It is within this type of setting that the young people are able to relax and to engage with drama as a powerful, therapeutic tool in the ways outlined below. Children of parents with a mental illness are often highly anxious and stressed, and the drama and games, first and foremost, allow them to forget their worries and just have fun, to be a child and to be able to play like a child, free from the burden of looking out for their parents, because they are safe in the parent’s group.
\nIn the young people’s group, playful exercises are combined with devising and acting out fictional scenes together. Designing the content of these dramas acts as a channel of free expression for fear, anger and anxiety or other difficult emotions that a young carer may struggle to access and express in daily life. The invention of fictional characters also means children can choose to play out different perspectives and new narratives—ideals of who they want to be. This encourages optimism and gives them a sense of control over their situation, thereby enhancing their self-esteem and trust in their ability to take action.
\nWhile the dramas do address parental mental illness, they often do so in an indirect or metaphorical way. They allow the children to differentiate from the illness, exploring it from a removed and outside perspective and not getting caught up in it. Indeed, the staff are careful not to lead the young people into sharing their specific experiences, as the drama work is intended to act as a helpful tool to enable young people to explore their experiences from a distance, to make up stories and create roles that focus on general aspects of mental illness and crisis.
\nThe dramas tend to capture the everyday experiences of the children and, in a more or less explicit way, the impact of their parent’s mental illness. The dramas are filmed and played back to parents and staff and therefore serve as an effective channel for young people to communicate their experiences and fears. Moreover, the themes and experiences depicted in the dramas are not owned by one person; they are devised, played out and therefore communicated, as a group; this feels safer and less threatening for the young people to express and for the adults to receive.
\nThe dramas are also useful in communicating important messages and explanations of mental illness to young people. The KidsTime model emphasises that explanations should address and challenge presumptions and fears that young people have about mental illness, for example, that they might “catch the illness themselves”, which the dramas frequently illustrate. In order to reduce rigid ideas and fears about mental illness in young people, the dramas should also present mental illness as a changeable process rather than as a fixed, constant entity. Including the subject of mental illness in dynamic dramas is particularly useful as it depicts mental illnesses through characters’ experiences rather than through listing signs and symptoms of diagnostic criteria.
\nThe drama work contributes to the aim of the workshops in creating a space where “kids can be kids”. The drama is part of a predictable and secure structure within which children do not take the lead, do not have to feel responsible and are thus able to relax and play in their more age-appropriate roles. In this way, the drama work enables the team to strike the important balance between the serious and the playful. The overall aim of the workshops is to provide a relaxed environment within which young people can explore and recognise their own roles, and the challenges within these, and have this validated by others while remaining optimistic and hopeful for the future. At KidsTime, young people are encouraged to recognise their successes and strengths despite their difficult situation and to have fun while doing so, which is enabled by creating an environment where they can engage in more age-appropriate roles and activities. The ability to play is a fundamental aspect of psychological health and creativity, and this is built into the method. It is noticeable that when children first come into the workshop, the ability to join in and play is very low but grows quite quickly once they feel safe.
\nTo date, several evaluations of the KidsTime Workshops have been carried out, using a variety of methodologies, the findings of which are summarised in the following paragraphs. As a general rule, individual feedback forms are completed by the adults and children after each workshop. A study of the German KidsTime Workshops found that [19]:
95% of families submitting evaluations stated they benefited from attending the workshops and wanted to continue attending.
All family members stated they had learned something new about mental illness at the workshops and that the workshops helped them to talk about mental illness within and outside of their families.
Watching and reflecting on the children’s drama film, as well as the multi-family group format (particularly the feeling of solidarity among families) were viewed as helpful catalysts in enabling the open discussion of issues that may have been perceived as being too “shameful” to talk about outside of the group.
Similar themes were present in the children’s feedback; however, the most important impact for children was the sense of freedom they experienced in being able to return some of the responsibility to adults they could trust and talk to and in connecting with adults in a more positive way, challenging their previous thoughts and feelings about adults and professionals coming into contact with them and their families. The feedback especially highlighted how children experienced KidsTime Workshops as a secure framework within which they could act more freely [19].
\nIn England, an evaluation by the Anna Freud Centre for Children and Families found that the workshops increased understanding of mental illness, improved parent-child relationships, reduced feelings of fear, shame and isolation and boosted confidence in children and young people [2, 11]. Due to the nuances and the number of factors at play within the workshops, Our Time has found that case studies are a useful tool in understanding the impact of these interventions on children and families. An analysis of recent family case studies in England has identified the following key themes: Rise in confidence among children and young people, improved relationships within and outside of the family, making new friendships and increased knowledge and understanding of mental illness.
\nFindings from the different evaluations undertaken to date demonstrate that the strength of the workshops lies in their ability to facilitate communication and positive relationship building within and outside of the family, providing effective peer support for children and parents and, in tackling the shame, stigma and misconceptions surrounding mental illness, reducing feelings of fear and isolation and raising young people’s confidence and self-esteem [2, 11, 19].
\nA common barrier to setting up and maintaining a KidsTime Workshop is securing funding for a preventative model. The fundamental rationale for the workshops is to prevent young people from developing psychopathology themselves. However, funding for support for people who do not have a formal diagnosis is almost impossible to obtain within curative and risk-oriented medical systems, which are often the result of restrictive fiscal policies that will only allocate funding for critical interventions. However, what such policies and approaches fail to address is that, without appropriate universal, preventative support in place beneath thresholds for critical services, the demand for these services will continue to grow at an alarming rate, leading to significantly increased costs in the medium to long term.
\nIn relation to children of parents with a mental illness, the stakes are high. An estimated 3.4 million children and young people in the UK live with a parent with a mental illness. Without help, 70% (3.1 million) of these children will go on to develop mental health problems themselves at huge expense to the public purse [6]. For example, if a quarter of these young people develop depression by 2021, the projected cost to the UK government could be up to £470 million [5]. This is the tip of the iceberg—depression is just one of many ill consequences likely to befall this group. Other potential long-term consequences include disrupted education, restricted peer relationships (due to carer role), financial hardship, potential separation from parents, stigma, future physical and mental health problems, greater risk of suicide, unemployment, marital problems and crime and violence [2, 3, 4, 5, 10]. Consequently, without intervention, the long-term prospects are bleak, and the cost of doing nothing could amount to £17 billion per year in the UK alone [20]. In comparison, the cost of preventative approaches is relatively small. To give an example, in England, it costs ~£2000 per family, per year, to take part in a monthly KidsTime Workshop, while an initial assessment by Child and Adolescent Mental Health Services costs £700 per child, prior to any intervention taking place.
\nWhile the case for prevention is clear, support for early intervention requires a culture shift across the health, social care and education system, which can only be achieved through policy change and the allocation of appropriate funds to facilitate this at a more local level. This will have to include training and awareness raising for professionals who deal with children in the course of their work, including adult mental health professionals. For this reason, organisations such as Our Time are campaigning for government to count the numbers of children affected by parental mental illness and to invest in prevention to help break the cycle of intergenerational mental illness.
\nThis chapter has provided an overview of the workings and impact of multi-family approaches in supporting families affected by parental mental illness, using the KidsTime Workshops as a case study example. It has described the benefits of a more informal and non-therapeutic, multi-family intervention in helping children and families to understand and communicate about mental illness. As well as highlighting the potential risks associated with having a parent with a mental illness, it has demonstrated the power of receiving a clear explanation in helping children to understand and cope with their situation. Access to a supportive and non-judgmental environment where families can share experiences and talk to others in the same situation has been identified as a key protective factor for children and their parents, as illustrated in the feedback and testimonials from families listed in this article. Recommendations for professionals and practitioners working with children and young people affected by parental mental illness are to:
Notice these children, and recognise the role they play in caring for their parents.
Recognise and acknowledge that they are experts in their family situation, with often very advanced knowledge and insight into their parent’s illness and/or behaviours.
Provide children with clear explanations of their parent’s illness and what is happening to the parent and for the reasons behind decisions (e.g., when a parent is hospitalised).
Recognise that these children may fear or reject traditional interventions. Ask children what would help and listen to what they have to say, so that any support offered does not undermine or further isolate the child or young person.
Those interested in trialling multi-family interventions for children affected by parental mental illness should pay attention to the following principles:
Create a relaxed, safe and supportive environment that is welcoming for parents and children.
Avoid imposing traditional hierarchical structures, i.e., of professional and patient, and, instead, encourage staff to adopt the role of a friendly helper to facilitate trust and communication within and outside of the family.
Provide clear age-appropriate explanations for mental illness.
Use a range of creative methods, such as drama, to engage and make it a fun experience for children, to enable exploration of the subject from different perspectives and to encourage reflection.
Further information and guidance about the KidsTime model, including how to set up a KidsTime Workshop, is available on the Our Time website: www.ourtime.org.uk.
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