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Electromyography biofeedback (EMG-BF) has been used to train muscle activation or relaxation but the application of EMG-BF to improve dynamic motion (e.g., walking or pedaling) is open to investigation. This chapter deals with an introduction to our previous work and the latest research we are working on. In our previous study, we investigated whether auditory EMG-BF is effective in improving muscle co-contraction. Unfortunately, we found that individual EMG-BF does not immediately improve muscle co-contraction during pedaling. To improve muscle co-contraction by EMG-BF, it may be necessary to convert muscle activation into muscle co-contraction. In our latest study, we investigated whether visual EMG-BF is effective in stabilizing walking. We found that EMG-BF during normal walking partially stabilizes the center of mass acceleration. Finally, based on our research findings, I will discuss the construction of an EMG-BF system that can contribute to the improvement of dynamic movements.
Graduate School of Human Development and Environment, Kobe University, Kobe, Japan
*Address all correspondence to: kibushi.b@ruby.kobe-u.ac.jp
1. Introduction
Biofeedback has been utilized as a rehabilitative approach to facilitate normal movement patterns. Humans have sensory receptors such as muscle spindles and tendon spindles, which engage in feedback control based on the information received from sensory receptors. Patients can access physiological data that would typically unnoticed in real-time by biofeedback. This aims to enhance the acquisition of normal movement patterns.
In this chapter, the focus is on electromyography biofeedback (EMG-BF). One of the functions of muscles is to move joints through contraction. Muscle contraction occurs when electrical signals from the brain reach the muscles via nerves. As electrical activity arises during muscle contraction, the measurement of electrical activity during muscle contraction on the surface of the skin has been employed as a means to examine the dynamics of muscle contraction. Obtaining electrical signals during muscle contraction is known as an electromyogram (EMG). Electromyography biofeedback (EMG-BF) involves converting the EMG into a visual or auditory feedback signal, establishing a novel feedback system, and contributing to the reacquisition of movements or learning new movements. Electromyography biofeedback (EMG-BF) is employed to enhance the activity of weakened or paralyzed muscles, as well as to alleviate tension in spastic muscles.
In this chapter, I will begin by briefly introducing previous studies on the effects of EMG-BF. A more comprehensive review of EMG-BF can be found in the works of Giggins et al. [1] and Huan et al. [2]. Furthermore, I will present our research on the utilization of EMG-BF for improving dynamic movements. Finally, based on our research findings, I will discuss the construction of an EMG-BF system that can contribute to the improvement of dynamic movements.
1.1 Static EMG-BF
Previous research on EMG-BF has been addressed by various experimental tasks. Among these tasks, there were simple movements unrelated to daily activities, such as users adjusting specific parameters while in a static state [3, 4, 5, 6]. Huang et al. [2] referred to this as “static biofeedback” [2]. Static EMG-BF also proves useful in rehabilitation. For example, applying static EMG-BF in rehabilitation after knee joint surgery leads to improvements in knee joint range of motion [7], peak torque during knee extension [8], and activation level of the knee extensor muscles [9]. However, static EMG-BF might have limited effectiveness. This is because the improvement is observed when the purpose is to alter the activity of the target muscle itself or when the target muscle is directly related to joint control. Moreover, several studies and reviews on static EMG-BF have not demonstrated significant contributions to the recovery of motor function [3, 10, 11]. For instance, Wolf et al. [11] used static EMG-BF to suppress the activity of antagonist muscles of the elbow extensor and activate the agonist’s muscle. However, this method prevented stroke patients from fully extending their elbows during a reaching task and caused muscle co-contraction during coordinated movements [11]. Additionally, applying static EMG-BF to the lower limbs of hemiparetic patients had no impact on functional gait [3]. Therefore, static EMG-BF might have limited effectiveness in promoting the recovery of motor function [12].
1.2 EMG-BF for improving walking abilities
The effectiveness of EMG-BF in improving walking abilities remains a subject of debate. According to a systematic review, it was concluded that EMG-BF does not have a significant effect on joint range of motion, functional capacity, stride, or walking speed following stroke [13]. However, several studies have reported positive results. For instance, adding EMG-BF to conventional exercise programs significantly reduced the usage time of walking aids in patients who underwent surgery for arthroscopic partial meniscectomy, compared to those who received only conventional exercise training [14]. Particularly, in children with cerebral palsy, many positive outcomes have been reported [15, 16, 17]. For example, EMG-BF targeting the tibialis anterior during walking led to improvements in symmetry and greater ankle power for push-off in children with cerebral palsy [15]. Furthermore, when children with cerebral palsy used EMG-BF, it resulted in improved foot clearance during the swing phase of walking and the acquisition of new abilities for contraction and relaxation of the anterior tibialis muscle [16]. Thus, opinions on whether EMG-BF improves walking abilities are divided. Such contradictions might depend on the specific type of EMG-BF utilized. Earlier, it was explained that static EMG-BF might have limited effectiveness in promoting the recovery of motor function. Therefore, static EMG-BF might not be suitable for improving walking. Consequently, it is believed that EMG-BF directly related to walking movement would be effective in enhancing walking performance.
2. Auditory EMG-BF for improving muscle co-contraction
2.1 Introduction
The inhibition of smooth movement often arises from muscle co-contraction occurring between agonist and antagonist muscles. Effectively improving muscle co-contraction could potentially contribute to promoting motor learning. However, there is limited research demonstrating the adjustment of muscle coordination patterns through EMG-BF. Torricelli et al. [18] reported a change in synchronous muscle activity patterns by providing biofeedback for the tibialis anterior muscle during pedaling [18]. Nevertheless, the effects of EMG-BF on muscle co-contraction remain unclear.
Biofeedback systems primarily use visual or auditory feedback [13]. Previous studies showed auditory EMG-BF reduced frontalis muscle activation, unlike visual EMG-BF [19]. Additionally, auditory feedback facilitated higher sensory integration than visual feedback [20]. Moreover, motor learning through the auditory feedback achieved a higher motor learning effect [20] and higher accurate posture control ability [21] even after removing auditory feedback, but the visual feedback did not. Faster auditory reaction times to stimuli [22] suggest auditory EMG-BF may prompt more immediate muscle adjustments than visual EMG-BF. Thus, we hypothesized that auditory EMG-BF enhances muscle co-contractions. We aim to investigate its effectiveness in improving muscle co-contraction and contribute to more effective EMG-BF methodologies’ development.
2.2 Auditory EMG-BF system
The participants had EMG electrodes attached to them to record muscle activity. The vastus lateralis (VL) and the semitendinosus (ST) on the right side were the muscles tested for feedback. Using an analog data acquisition system, the EMG data was transferred into a personal computer and monitored via a specially designed LabVIEW program. The EMG data, which was recorded at 1000 Hz, was then processed in real-time through full-wave rectification and smoothing via the weighted moving average.
During the determining a threshold, participants engaged in a one-minute pedaling session at the workload assigned for adjustment or measurement tasks. The VL and ST were rectified and smoothed in order to establish each muscle’s peak value. Thresholds were defined when the normalized EMG amplitude exceeded 5% of the maximum amplitude during pedaling compared to the amplitude at rest, a method determined through preliminary experiments. The threshold was progressively altered, and participants have queried whether the beeping corresponded to their muscle activation timing. We established the lowest threshold that could consistently produce an appropriate sound. Beep frequencies were designated as 400 Hz for VL and 800 Hz for ST.
2.3 Experimental procedures
Six women and seven men participated in this study (women: age 25 ± 3 years, height 160 ± 5 cm, body weight 53 ± 4 kg; men: age 25 ± 3 years, height 171 ± 5 cm, body weight 69 ± 9 kg, [mean ± standard deviation]).
Pedaling was chosen for the experiment due to its simplicity and the co-contraction phase it includes. Skill level correlates with muscle co-contraction during pedaling, with cyclists displaying less co-contraction than triathletes [23]. Improving co-contraction may require increasing agonist activation while decreasing antagonist activities.
The experiment followed this order: instruction, initial threshold adjustment for EMG-BF system, adaptation to clipless pedals and EMG-BF system, workload determination, second threshold adjustment, and kinematics and EMG measurements (Figure 1). Participants pedaled for 20 minutes to adapt to the clipless pedal and the EMG-BF system [24]. The threshold adjustment was necessary as the EMG-BF system beeped when muscle activation exceeded a certain threshold, chosen from a study finding pure tone sounds acceptable for athletes during rowing [25].
Figure 1.
Experimental procedures.
In the study, the workload was determined based on heart rate monitoring. During adaptation, cadence and workload were adjusted to keep heart rates between 100 and 120 bpm. Beep sounds were used for muscle activity feedback, aiming to reduce overlap. After a 10-minute rest, the workload for measurement was set so that the heart rate maintained around 150 bpm at 80-cadence. The workload increased by 20 W/min until 150 bpm could no longer be maintained. The relative workload was selected based on heart rate, considering both sexes participated. Average workloads for women and men were 123 ± 26 W and 176 ± 26 W, respectively. A 10-minute rest followed workload determination. During measurement tasks, participants pedaled for 90 seconds at 80-cadence with a determined workload.
Four feedback conditions during measurement tasks were: no-feedback (NFB), VL feedback (VLFB), ST feedback (STFB), and both VL and ST feedback (VL-STFB). In addition to NFB, the need to compare individual EMG-BF as in the previous study [4] and EMG-BF agonist-antagonist muscles was assumed. In VL-STFB, we asked participants not to overlap the beep sound.
2.4 Measurement and analysis
In order to capture the movements associated with pedaling, reflective markers were affixed to the right pedal and crank. A four-camera 3D motion capture system operating at 100 Hz was used to record these position coordinate values. The topmost position of the pedal stroke was identified as a crank angle of zero, defining one complete cycle as the movement from one topmost position to the next.
Alongside the VL and ST, EMG signals from the rectus femoris (RF) and long head of the biceps femoris (BF) were also measured to study the co-contraction of muscles involved in hip flexion-extension. The EMG signals from RF and BF were recorded at a frequency of 1000 Hz. The EMG signals were high-pass filtered (20 Hz) with a zero-lag fourth-order Butterworth filter to remove motion artifacts. Thereafter, the EMG signals were demeaned, digitally rectified, and low-pass filtered at 15 Hz with a zero-lag fourth-order Butterworth filter. These low-pass filtered signals were then time interpolated over a single cycle of motion to conform to a normalized 200-point time base. Each muscle’s activity was normalized to its peak activity recorded across all conditions.
Normalized EMG data were used to estimate the co-contraction index (COI), which represents the degree of simultaneous activation between agonist and antagonist’s muscle. The COI of the hip flexor-extensor (RF-BF) and knee extensor-flexor (VL-ST) was assessed using the following (Eq. 1) [26, 27]:
COI=2×Common AreaareaEMG1+areaEMG2×100E1
where areaEMG1 and areaEMG2 represent the integral of the sum of agonist and antagonist EMG data. Common Area represents the common area between agonist and antagonist EMG data (Figure 2).
Figure 2.
Illustration of COI.
After confirming normal data distribution, repeated measurement of ANOVA was applied to determine the difference in COI among the different conditions. The results with a p-value < 0.05 were considered significant.
2.5 Auditory EMG-BF did not improve muscle co-contraction
We observed no significant difference in COIs among the conditions (Table 1).
Average ± SD
p-value
COI of VL-ST
NFB (%)
47.8 ± 13.1
0.83
VLFB (%)
48.8 ± 12.1
STFB (%)
48.1 ± 13.0
VL-STFB (%)
49.2 ± 12.0
COI of RF-BF
NFB (%)
54.1 ± 11.3
0.32
VLFB (%)
56.5 ± 9.8
STFB (%)
53.5 ± 12.0
VL-STFB (%)
55.9 ± 11.0
Table 1.
COI of VL-ST and RF-BF.
Contrary to our hypothesis, auditory EMG-BF does not improve muscle co-contractions. This might be because our auditory EMG-BF system might have some problems. We suggest an idea for improving muscle co-contraction by auditory EMG-BF.
Initially, let us discuss how muscle activity did not see enhancement. We hypothesized that the regulation of muscle relaxation during pedaling presents more challenges because modulating force while muscles are relaxed is tougher than when they are generating force [28]. Nevertheless, unsuitable patterns were noted in both the relaxation of antagonist’s muscles and the activation of agonist muscles. In a qualitative observation, the ST was found to be active between 270 and 360 degrees of the crank angle in this study. This insufficient relaxation of antagonist muscle might cause high COI of VL-ST around the top dead center. According to Candotti et al. [23], the BF activation in triathletes was only seen from 0 to 180 degrees of the crank angle, while cyclists showed activity beyond the bottom dead center, leading to the inference that reduced BF activation contributes to a higher COI in triathletes. In our study, the ST and BF activations beyond the bottom dead center were not significant under all conditions. This insufficient activation of the agonist’s muscle could possibly lead to a high COI of VL-ST around the bottom dead center.
2.6 Future issues for auditory EMG-BF to improve muscle co-contraction
Now, let us consider the issues related to the system. The lack of improvement in muscle co-contraction observed in our study might be due to the suboptimal setup of the EMG-BF system. Specifically, the EMG-BF system may not be fully capable of translating muscle co-contraction. According to Peres et al. [4], the most accurate muscle activation timing could be estimated when the feedback sound’s pitch and loudness were adjusted [4]. In our study, we maintained constant loudness and varied pitches to make it easier for the participants to discern different muscle activities. When questioned about their muscle activity perception during the EMG-BF system adjustment, the participants reported that they could discern individual muscle activities as well as the overlap of multiple muscle activities. Nevertheless, modifying the feedback sound’s pitch and loudness might have been a more suitable approach.
Besides the matters of pitch and loudness, the differing feedback sounds of EMG could potentially result from cognitive overload. In this study, participants identified both the activation and relaxation of agonist-antagonist muscles either as a single beep or a combination of different beeps. Previous research indicates that the simultaneous processing of multiple pieces of biomechanical output information can decrease pedaling endurance performance due to information overload [29]. The auditory feedback from two muscles could indeed lead to the presentation of multiple pieces of information. Nonetheless, it is generally accepted that multimodal stimuli are perceived more accurately and quickly than unimodal stimuli [30, 31]. Sigrist et al. [32] suggested that if the workload in one sensory modality is high, augmented feedback should be provided in another modality or in a multimodal manner. This could prevent cognitive overload, and therefore, might enhance motor learning. Considering this viewpoint, auditory information about the timing of individual muscle activities might be inadequate for modifying muscle co-contraction. For instance, in our study, there was no feedback on the intensity of muscle co-contraction. To enhance the feedback, the visualization of the COI might be a solution. Consequently, it may be necessary to transform the signals of individual agonist and antagonist muscle activation into a co-contraction signal. Moreover, a system that combines visual feedback of COI with sound feedback that adjusts pitch and loudness based on muscle co-contraction signals may be more effective.
2.7 Conclusion
We examined the effectiveness of using beep sounds with EMG-BF to enhance muscle co-contraction. Our results indicated that EMG-BF did not improve muscle co-contraction during pedaling. In order to boost muscle co-contraction via EMG-BF, there might be a need to transform the signal of individual agonist and antagonist muscle activation into a co-contraction signal. Our conclusion is that instant improvement of muscle co-contraction during pedaling is challenging using individual EMG-BF.
Normal walking is typified by kinematic stability, evidenced by the steady patterns of head and lumbar acceleration and the reduction of variability in trunk motion from stride to stride at a regular walking pace [33, 34]. The root mean square (RMS) of upper body acceleration is a frequently employed measure of kinematic stability, with a higher RMS signifying instability in circumstances like walking on uneven surfaces for healthy young adults or associating with an increased risk of falls in elderly individuals [35, 36].
Rhythmic auditory stimulation (RAS), which employs a metronome, has been implemented in physical therapy to correct irregular walking, thus enhancing cadence and stride length, and decreasing stride time variability in patients with Parkinson’s disease [37, 38, 39, 40]. However, RAS with a fixed tempo could potentially cause unstable walking in healthy adults [40, 41], indicating the possible necessity for non-stationary auditory stimulation.
Electromyography biofeedback (EMG-BF) provides another non-stationary stimulation technique, which could affect walking stability due to the direct influence of muscle activity on joint force application [42, 43]. Previous research has shown that EMG-BF enhances walking ability in stroke patients and children with cerebral palsy [15, 17, 44, 45]. The goal of the current study is to build upon these findings by investigating if EMG-BF improves walking stability in healthy adults, positing that it reduces the RMS of the center of mass acceleration (RMS-CoMacc). This study represents the inaugural exploration of the impact of EMG-BF on walking stability in healthy adults.
3.2 Experimental procedure and analysis
The study involved the participation of five women and seven men (women; age: 23 ± 5 years, height: 160 ± 3 cm, body mass: 54 ± 7 kg, men; age: 21 ± 1 years, height: 169 ± 3 cm, body mass: 66 ± 4 kg [mean ± standard deviation]). To gather kinematic data, reflective markers were positioned on 24 body landmarks. These coordinate positions were captured using a three-dimensional motion tracking system equipped with 10 cameras, operating at a frequency of 100 Hz. We recorded EMG data from four muscles in the right lower limb and trunk, specifically, the soleus (SOL), tibialis anterior (TA), vastus lateralis (VL), and semitendinosus (ST).
The participants in this study were assigned two tasks: a maximum voluntary contraction (MVC) task and a walking task. The MVC task involved four main movements: ankle plantarflexion, ankle dorsiflexion, knee extension, and knee flexion. Prior to recording the measurements, participants practiced each MVC task and the RMS of the EMG data was computed around the maximum value for each MVC task. The walking task had participants walk on a treadmill with or without EMG biofeedback. The biofeedback conditions included soleus (SOLBF), tibialis anterior (TABF), semitendinosus (STBF), and a no biofeedback scenario (NBF). Before measurements were collected, participants had a practice session on the treadmill. The EMG-BF system would make a beeping sound if the muscle activity went above a certain threshold based on the MVC. Each EMG-BF had three different thresholds, and the order of the conditions was randomized. During EMG-BF, participants aimed to sustain a consistent beep sound tempo.
Three-dimensional RMS-CoMacc was used to gauge walking stability, computed from the second-order derivative of the center of mass position (anteroposterior [AP], vertical [VT], mediolateral [ML]). Statistical comparison was conducted among the RMS-CoMacc across biofeedback conditions. For each biofeedback condition, only one threshold condition was extracted for comparison. This was because the RMS-CoMacc minimizing threshold condition varied among participants and within the same participant, depending on the direction. Data normality was confirmed with the Shapiro-Wilk test before conducting repeated measure ANOVA. Significance was determined at p < 0.05, with marginally significant results considered at 0.05 ≤ p < 0.10.
3.3 Auditory feedback of soleus reduces RMS-CoMacc in AP and VT direction
We found that the RMS-CoMacc in both the AP and VT directions was significantly lower in the SOLBF condition than in the NBF condition (Figure 3), indicating that the direction in which the RMS-CoMacc decreases depends on the muscle being used for biofeedback. An earlier study showed that the SOL and lateral gastrocnemius muscles are responsible for decelerating the trunk backward during the loading response phase and propelling the trunk forward during the propulsion phase [46]. In addition, the SOL and lateral gastrocnemius muscles are primarily responsible for the vertical acceleration of the trunk [46]. Since the SOL plays a crucial role in creating both AP and VT trunk acceleration, SOLBF might decrease the RMS-CoMacc in both AP and VT directions. Moreover, in terms of comfort, SOLBF seems to be the most suitable biofeedback condition. When asked about the biofeedback condition that was easiest for modulating their muscle activation, most participants chose SOLBF. The SOL has a relatively distinct monophasic waveform, unlike the TA and ST. The waveform’s shape could be related to the ease of modulating muscle activation. The decrease in RMS-CoMacc as a result of EMG-BF could be related to both the biomechanical contributions to trunk acceleration and usability.
Figure 3.
Average RMS-CoMacc in AP, VT, and ML direction.
3.4 Auditory EMG-BF did not reduce RMS-CoMacc in ML direction
We noted that EMG-BF did not decrease the RMS-CoMacc in the ML direction (Figure 3). Previous studies suggest that the link between trunk acceleration in the AP and VT directions is stronger than the connections between the ML and AP directions and between the ML and VT directions [47]. This could suggest that the modulation of trunk acceleration in the ML direction operates somewhat independently. This independent modulation in the ML direction could be connected to the constant RMS-CoMacc in the ML direction during EMG-BF. Another possible explanation for the stable RMS-CoMacc in the ML direction is that we may not have chosen a muscle involved in moving the body in the ML direction for EMG-BF. We chose the ankle plantar flexor, ankle dorsiflexor, and knee flexor for EMG-BF, muscles primarily involved in moving the body in the VT or AP directions. To reduce the RMS-CoMacc in the ML direction, we might need to select hip abductor or adductor muscles for EMG-BF.
3.5 Conclusion
We aimed to find out if EMG-BF improves walking stability in healthy adults. While EMG-BF did have a slight reducing effect on RMS-CoMacc, this effect was minimal. Notably, biofeedback of the ankle plantar flexor managed to decrease both anteroposterior and vertical RMS-CoMacc. We concluded that biofeedback of the ankle plantar flexor can marginally stabilize the anteroposterior and vertical center of mass acceleration during walking.
This chapter deals with an introduction to our previous work and the latest research we are working on. In our previous study, we investigated whether auditory EMG-BF is effective in improving muscle co-contraction. Unfortunately, we found that individual EMG-BF does not immediately improve muscle co-contraction during pedaling. To improve muscle co-contraction by EMG-BF, it may be necessary to convert muscle activation into muscle co-contraction. In our latest study, we investigated whether visual EMG-BF is effective in stabilizing walking. We found that EMG-BF in soleus during normal walking partially stabilizes the center of mass acceleration.
Through two studies, insights have been obtained regarding the requirements for constructing an EMG-BF system capable of enhancing dynamic movements. These insights include:
Converting muscle activity into discernible signals
Selecting the most relevant muscles associated with the desired stabilized movements as feedback targets
Adjusting the threshold individually for auditory feedback
While research on static EMG-BF has been conducted for a long time, there remains ample room for exploration regarding EMG-BF specifically aimed at improving dynamic movements. Consequently, it is necessary to construct a system based on the aforementioned insights and validate its effectiveness in enhancing various dynamic movements other than pedaling and walking.
This work was supported by the TOBE MAKI Scholarship Foundation (Grant number: 19-JC-002), JSPS KAKENHI (Grant number: 21K17627), and CAINZ Digital Innovation Foundation.
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Written By
Benio Kibushi
Submitted: 12 June 2023Reviewed: 13 June 2023Published: 09 July 2023
Open access peer-reviewed chapter
