Possibility of Using a VR System as an Action Observation Therapeutical Technique

Abstract

In recent years, 3D virtual reality (VR) systems are increasingly finding their way into biomedical applications. Nevertheless, in most cases a 3D VR is being used as an interactive system (such as Xbox Kinect or Playstation VR). These interactive systems, however effective they may have proven, not only limit use of 3D VR in patients incapable to engage in these systems due to their physical or mental disability, but also put significant requirements on medical institutions for an equipment, medical personal, and therefore institutional budget. In this article, we are proposing a 3D VR as an stand-alone action observation training device, which could limit requirements associated with abovementioned interactive systems due to its capability to stimulate a mirror neuron system of human brain, while adding minimal demands on both patient and medical facility. Research studies that confirm activity in the motor cortex will be described. We focus on the literature that describes theories, models, and experimental studies dealing with the effects of motion observations that are involved in the control and final performance of motor skills.

1. Introduction

3D virtual reality (VR) is currently being used mainly for gaming purposes. However, there is an increasing number of VR implementations in fields of sports training and rehabilitation. The system being used as a VR training system is usually an interactive gaming system (e.g., Playstation VR, Nintendo Wii...). Nevertheless, a simple action observation (AO) using 3D VR system without its interactive component is also an interesting, if not regularly implemented, option to increase a therapy time without an involvement of therapist in the process, due to AO’s capability to activate mirror neuron network. In case the use of VR in such a way is possible, it might open up new ways of therapeutical approaches in many patient cases, where there is a lack of cooperation with patient and the interactive VR system would not be applicable.

2. Mirror neuron

A theory of the specific premotor brain cells, which are activated not only during actual motor execution of a movement, but also during observing the movement, was first formulated in 1992 by si Pellegrino [1]. An activation in the F5 area of the makak’s brain was found, which was the same for particular movement components (such as a grasp) as for an observation of a human grasping an object. This provided a mechanism for explanation of AO’s relation to the motor planning. A consequential research [2] described 532 specific neuron cells in abovementioned cortex area, which were named “mirror neurons” (MNs). Most of the newly found structures were reacting to the movement related to a hand grasp and about 30% of them were involved in specific manipulations and movements. Therefore, the theory about MN being an inherent part of the motor planning was developed.

2.1 A road to human trials

In subsequential studies, other major brain areas containing MN were described. Also different cells were found to have different activation stimulus (e.g., MN activated only during AO, MN activated during AO and motor execution...). However most of the research was still focused on the F5 brain area of monkey’s brain, where the highest number of MN was found, mainly due to inaccurate or unsafe observing methods for human trials.

Nowadays (03/2022) PubMed returns over 200 results to search of “mirror neurons AND human”. Usually, an activity of MN system is measured by functional MRI. A problem is in contrast agents, which are normally used in the monkey trials (most often monocrystalic iron oxide nanoparticles), but are toxic for the human bodies [3].

For differentiation of specific neuron cells via fMRI is theoretically possible solution in natural neuron adaptation on stimulus-neural response decreases with stimulation. For MN, the decrease should be present both via observing and via executing movement. There are studies available [4] that confirm this theory, whereas others did not find any MN adaptation signs in humans [5] or in monkeys [6].

However, there is an evidence for neuron activity in same regions of premotor cortex during action observation for humans and monkeys [7].

In 2012, Molenberghse et al. described on fMRI (n = 125) 14 areas with the corresponding activity to the stimulus as with the monkey brains. These areas mainly contain prefrontal gyrus, ventral and dorsal motor cortex an parietal lobe during action observation; however, MN characteristic activity was also found in the amygdala, insula, and other regions of cortex during emotional and acoustic stimulation. In total there was described activity corresponding to MNs in 34 Brodmann areas of the human brain. Although the authors conclude the paper with the observation that, taking into account the findings from monkey brains, it is unlikely that all the 34 regions directly contain MN cells, there is found the recurrent response of the human brain to sensory (optical, auditory, and emotional) stimuli, as well as downstream activity in cortical areas, corresponding to the presence of MN [8].

2.2 Stimulation of MN as a way to train motor skills

The abovementioned findings open up, among other things, the possibility of using the MN stimulation as a training or therapeutic tool. Due to the activation of some MN during the execution and monitoring of the movement, and provided that these MN are involved in movement imagery and planning, their targeted involvement can be as a tool for training motor functions. In practice, the targeted MN can be set into two types: the aforementioned action observation and a motor imagery (MI).

3. Action observation and a clinical use

The use of AO in the treatment of motor deficits is a relatively well-documented phenomenon. Some authors refer to AO directly as to a mechanism activating MN that mediates sensory and emotional learning, as well as learning from an observation of movement, and thus represents the potential for the use of AO and its action through MN as a passive rehabilitation and learning technique for both cognitive-behavioral and motor function [9].

The effect of independent AO on postural stability and movement coordination has been described. For example, Son & Kang [10] observed another person performing the same test between two measurements and found a significant increase in stability during Y-balance test compared with the control group. Given that observation of another person performing the test was the only difference in the test protocol between the test and control groups, the authors conclude that AO, even without the use of an additional training technique, has the potential to affect stability.

Similarly, Gatti et al. [11], in their comparison of the AO and balance effect training, focus on the effect of AO on the stability. The authors measured the changes in center of pressure (COP) in stance modifications. The sample of probands (n = 79) was divided into three groups with different training protocols (AO, AO with a movement imitation, balance training) and one control group. The balance training consisted of a series of coordination and stability demanding movements (walking on the balance beam, standing on a trampoline on one leg, standing on a roller, etc.). The video used showed the same movements performed by a professional athlete. According to the results of this work, the AO together with the imitation of the movements even appears to be an equivalent training tool to increase postural control as a stand-alone balance training (effect size ES [0,7]) compared with the control group. AO alone then has a lower effect size (ES [0.3]), but still it is significant.

There has also been described an increase in muscle strength in the hand movement after AO therapy. For example, Porro et al. [12] found that of the 82 participants in the experiments, for the physical training, group muscle strength was 50% higher than baseline values were, but even in the AO-only intervention group, this change was significant (+33%) compared with the baseline values. There was no significant change in the control group.

The findings open the question of using AO for motor stability learning with temporarily immobile patients or in specific motor deficits.

A positive effect of AO intervention for post-stroke patients has also been described. Nevertheless, AO appears to be the most effective as an addition to execution of an observed activity (overall balance index 2.3 ± 2.0 before and 1.2 ± 0.8 post-test; mEFAP 102.2 ± 45.5 pre and 54.2 ± 41.4 post-test) [13].

For children and adolescents with Down syndrome, the use of “therapeutic virtual reality” represents an opportunity to significantly improve coordination and stability of movement and, last but not least, the fun in exercise (46.86 + 7.98 before, 53.57 + 1.99 after therapy in the KTK motor test) [14]. Lohse et al. [15] in a systematic review and meta-analysis of 26 papers describing the effect of VR in the rehabilitation of stroke patients found that using VR therapy results in significant effect compared with conventional therapeutic methods in standard motor tests results (ES [0.48]), as well as in functional tests (“ADL” tests; ES [0.58]). VR games (XBox 360 Kinect) were also used to successfully influence the coordination skills of children with central coordination disorder as measured by the DCDQ test (p = 0.003 for α = 0,05) [16]. And in addition to the aforementioned collection of work dedicated to MN activity on fMRI, there are also five studies measuring EEG from which the result is that both AO and mirror therapies increase the possibility of motor unit inhibition in spasticity [17].

Hebert [18] showed that AO has a positive effect on coordination, the ability to plan and to learn, even for complex activities and movements such as a “speed stacking” (building pyramids from the cups). In three training rounds of VR, the probands in the first round were slower than in the second round (Cohen’s d = 0.64) and in the second round they were slower than in the third round (Cohen’s d = 0.56).

LoJacono et al. then showed that training in a virtual environment can change the movement strategy in a real environment. Between two “cross obstacle” tests in the real environment, probands (n = 40) underwent training of the same movement on a treadmill with an obstacle projected using VR. It was confirmed that the training effect is already observable in the virtual environment (F[3,36] = 5.10, p = .01, η2 = 0.30), and even that there is a change in movement strategy in the real environment and that the movement after the training is performed more safely (F[4,34] = 4.42, p = .01, η2 = .34) [19].

4. Motor imagery and its clinical use

From the available evidence, MN system activity is present not only at the abovementioned observation, but also when imagining movement [20, 21, 22, 23]. At first glance, thus the MI appears to be a substitutable mechanism for AO and VR systems.

However, specific studies dedicated to this possibility seem to suggest otherwise. According to data from Gonzalez-Rosa et al. from EEG imaging during the implementation of complex movements of all four limbs during the training, there is an increase in activity for the both training mechanisms (F(4, 52) = 4.18, p = 0.02), but with a significantly higher MN activation during AO in both the parietal lobe (p = 0.03) and frontal lobe (p = 0.79) compared with MI training. Then, during the execution of the trained movement, the observable effect during kinematic analysis in movement speed (F(5,130) = 6.58, p < 0.01), as well as a group effect [F(2, 26) =3.73, p = 0.03] with a significant difference between the AO and MI groups (p = 0.03) occurred. The results, among others, were interpreted by the authors as showing lower MN activation and thus lower clinical effect of MI, probably due to the imagery of unspecified movement difficult for probands to imagine specifically. The authors open a discussion on the imaginative abilities of the probands [24].

Similarly, Neuper et al. describe MN activation in MI, but lower than in AO [25]. And Bakker et al. [26], measuring brain excitability, found an effect of imagining a simple dorsiflexion leg movement, but for a complex movement (walking), this effect was only evident for probands with above-average excitability for the simple movement.

Thus, although MI appears to be a suitable alternative to therapies using AO, the resulting activation can be highly variable. In its use, reliance must be placed on the interindividual psychomotor abilities of the patients and during the course of therapy there is no option beyond the relatively complicated EEG measurement or fMRI to check for correct implementation of the therapy. In contrast, in AO, the therapy can act on relatively complex and specific motor functions (grip, dorsiflexion of the leg, etc.) in a specific way, which is easily repeatable in the case of video-assisted AO.

Based on the different brain activity and the different effects of each type of training in the above works, we hypothesize that using AO visual feedback contributes positively to the activation of the MN system and thus potentially leading to a higher training effects. The aforementioned findings pointing to a lower effectiveness of MI training compared with AO, as well as to interindividual differences in the effect of MI on brain activity (especially in more complex movements), represent potential limitations to the use of MI in clinical practice.

5. Mechanisms in AO and MI

Differences between AO and MI are not only in clinical effects but also in mechanisms of their effects on brain activity—AO has a higher proportion of visual feedback, thus providing a concrete representation of the trained movement. Following a different principle of MN activation, different activity of individual brain regions was found.

During AO, activity was observed mainly in the brain regions ventral and dorsal premotor cortex (VPC, DPC), in the upper and inferior parietal lobe, in the sulcus temporalis superior, and in the dorsolateral prefrontal cortex. Combination of these areas is called an “action observation network” (AON). In some of these areas, particularly in the VPC, in the inferior temporal lobe and in the sulcus temporalis superior, the presence of MN has been described [27]. AON activity is also associated with an increase in motor-evoked potentials, i.e., with an increase in corticospinal excitability.

During MI, activity is observable in the supplementary motor area, in the primary motor area, the premotor area, the basal ganglia area, and the cerebellum, areas similar to those involved in real movement; however, the activity in these areas is in comparison to the actual execution of the movement different—in MI, the activity of the premotor cortex in particular is significantly lower [28]. At the same time, the activated regions differ according to the type of motor imagery. For example, the parietal lobe is activated similarly in both kinesthetic and visual imagery, but there are observable variations in motor and visual centers [29, 30]. It is therefore necessary to differentiate and correctly instruct the different modalities of MI depending on the intended activation.

6. VR parameters to optimize results

With the assumption that VR can be an effective tool to influence MN and thus motor function, the question arises on what parameters the VR used should meet.

Perez-Marcos [31] talks in his overview of VR technology about four qualities of VR environments: immersion, interaction, sensorimotor capabilities, and illusion. Immersion is fundamental potential advantage of 3D VR systems. However, it is not just about the type of projection, but about the overall way in which the virtual environment is mediated. Immersivity can also be supported by other sensory perceptions (e.g., audio, tactile...).

Above all, the possibility of interaction increases the fidelity of the experience. Nevertheless it seems that the immersiveness induced by the use of appropriate inputs can, over time, “burn out” if there is no opportunity to interact with the virtual environment. The simplest form of the interaction is said to be the response of the image to the movement of the head—by looking around in the virtual environment. Notwithstanding it can be extended to include for example image response to an observer activity or, in the case of robotic systems, mechanisms using biofeedback.

Sensorimotor interactions allow for another “layer” of interaction with the virtual environment. One of the top forms is the mentioned biofeedback (e.g., EMG-driven exoskeleton), or the mediation of specific tactile sensations related to our activity in the projected image. In practical applications, it is usually a vibrating game system controller. More advanced sensorimotor response can be provided by some robotic systems.

In the context of VR, illusion is the last “stage” in the quality of the environment, including all the previous ones. An ideal VR system would be able to provide such a high immersiveness, such a high level of interaction and such faithful sensorimotor information, that it would be possible to fully feel and fully “trust” in one’s presence in a virtual environment.

Wenk et al. elaborated, in their comparison of the different VR principles, the difference between immersive (3D) virtual reality (IVR) and 2D VR (screen) as to an impact on the movement quality as assessed by the motor and cognitive tests. The study participants placed objects on the marks in the virtual environment while counting them. During the test, task duration, straightness of trajectory of movement, speed of movement, and cognitive load were measured. In the IVR environment, compared with the 2D VR, the tests were performed faster (ES [0.7519]) and with a more direct trajectory (ES [0.7194]) [32]. And Panek et al. described lower brain activity during 2D VR observation compared with observing therapist performing the movement. These findings seem to suggest that the higher the immersion, the higher the activity [33].

7. Suggestion of VR for motor training via AO

As a consequence of the abovementioned findings, we argue it is possible to suggest principles for VR implementation to the clinical practice. First of all, the chosen system should provide the highest possible level of immersion possible. The use of 3D VR systems over 2D VR is therefore preferable. The video chosen for the intervention should be made in such a way that the 3D environment presented provides the most “real life experience” possible - “first person” video format is preferable over third-person game animations. The movements chosen for the AO should be presented specifically and simply enough so that observers can relate to their execution—e.g., grabbing a ball in the video is preferable over the video of painting a picture on canvas.

With abovementioned principles, it is theoretically possible to start using VR as stand-alone system for AO, without the use of interactive system. Although the implementation of interactive component is arguably beneficial (at least to the brain activity), the use of VR for AO poses possibility to intervene in patient cases, where an active cooperation is (for whatever reason) not possible.

8. Conclusion

In this article, we aimed to summarize the principles involved in action observation via VR system used as a stand-alone action observation training device. Although the published studies indicate that in many cases (e.g., balance tests, coordination skills, etc.) the implementation of interactive component is arguably beneficial (at least to the brain activity), the use of VR for AO poses possibility to intervene in patient cases, where an active cooperation is (for whatever reason) not possible. With the information gained from our literature research, we are suggesting the parameters for VR video suitable for AO training. Although more research and thorough clinical trials for this principle are definitely needed, the use of 3D VR without interactive component involved seems to pose an interesting opportunity for implementing relatively new and attractive technology to increase efficiency of standard therapeutical approaches.

Acknowledgments

Written as part of project: SVV 260599 2020 -2022, PROGRES, Cooperation 2022 - 2026.

Conflict of interest

The authors declare no conflict of interest.