setting up camera coupled-emg sensors and vr headset to assess motion and muscle activity

Intégration de la RV et de biocapteurs pour l’évaluation quantitative de l’activité musculaire et du mouvement

Movement is how we interact with the world. While everyday activities such as picking up a glass of water or walking to work may seem simple, even these movements rely on complex signaling between the central nervous system, muscles, and limbs1. As such, personal independence and quality of life are highly correlated to the level of an individual&#39;s limb function2,3. Neurological damage, as in spinal cord injury (SCI) or peripheral nerve injury, can result in permanent motor deficits, thereby diminishing one&#39;s ability to execute even simple activities of daily living4,5. According to the National Institute of Neurological Disorders and Stroke, over 100 million people in the United States experience motor deficits, with stroke as one of the leading causes6,7,8. Due to the nature of these injuries, patients often require prolonged care in which quantitative motor assessment and remote treatment may be beneficial.
Current practices for treating movement disorders often require both initial and ongoing clinical assessment of function through observation by trained experts such as physical or occupational therapists. Standard validated clinical tests often require trained professionals to administer them, with specific time constraints and subjective scoring of predefined movements or functional tasks. However, even in healthy individuals, identical movements can be accomplished with varying combinations of joint angles. This concept is termed musculoskeletal redundancy.
Functional clinical tests often do not account for the individual redundancy underlying inter-subject variability. For clinicians and researchers alike, distinguishing between normal variability caused by redundancy and pathological changes in movement remains a challenge. Standardized clinical assessments performed by well-trained raters utilize low-resolution scoring systems to reduce inter-rater variability and improve test validity. However, this introduces ceiling effects, thus lowering the sensitivity and predictive validity for subjects who may have mild movement deficits9,10. Furthermore, these clinical tests cannot differentiate if deficits are caused by passive body mechanics or active muscle coordination, which may be of importance during initial diagnosis and when designing a patient-specific rehabilitation plan. Randomized clinical trials have revealed inconsistent efficacy of treatment plans formulated based on evidence provided by these clinical tests11,12,13. Several studies have emphasized the need for quantitative, user-friendly clinical metrics that may be used to guide the design of future interventions14,15.
In previous studies, we demonstrated the implementation of automated movement assessment using readily available consumer motion capture devices in post-stroke arm impairment, as well as the evaluation of shoulder function after chest surgery in breast cancer patients16,17. Additionally, we have shown that using active joint moments to estimate muscle moments of specific active movements is a more sensitive measure of motor deficits after stroke compared to joint angles18. Motion capture and surface electromyography (EMG) may therefore be of critical importance in the assessment of patients who are diagnosed as asymptomatic by standard clinical tests, but who may still be experiencing movement difficulties, fatigue, or pain. This paper describes a system that may enable detailed and quantitative characterization of movement during standard clinical tests for the future development of methods for at-home evaluation and rehabilitation in movement-impaired patient populations.
Virtual reality (VR) can be used to construct an immersive user experience while modeling everyday tasks. Typically, VR systems track the hand movements of the user to allow for simulated interactions with the virtual environment. The protocol we describe here uses consumer VR products for motion capture to quantify the assessment of motor deficits, similar to other studies demonstrating the use of off-the-shelf video game controllers in quantitative evaluation of impairment after stroke or shoulder surgery16,17. In addition, EMG is a non-invasive measure of the neural activity underlying muscular contraction19. As such, EMG may be used to indirectly evaluate the quality of the neural control of movement and provide a detailed assessment of motor function. Muscle and nerve damage may be detected by EMG, and disorders such as muscular dystrophy and cerebral palsy are commonly monitored using this technique20,21. Furthermore, EMG may be used to track changes in muscle strength or spasticity, which may not be evident in kinematic assessments22,23, as well as fatigue and muscle coactivation. Metrics such as these are critical in considering rehabilitation progress23,24,25.
The experimental paradigm described here seeks to leverage a combination of VR and EMG to address the limitations of traditional clinical assessment tools. Here, participants were asked to perform a modified Box and Block task (BBT)26 using real objects and in VR. The standard BBT is a clinical tool used in the general assessment of gross upper extremity function, in which subjects are asked to move as many 2.5 cm blocks as possible from one compartment, over a partition, to an adjoining compartment within one minute. While often used to reliably assess deficits in patients with stroke or other neuromuscular conditions (e.g., upper extremity paresis, spastic hemiplegia), normative data have also been reported for healthy children and adults, ages 6-8926. A virtual movement assessment is used to simulate functional aspects of the validated clinical test performed in real life. VR is used here to decrease required hardware while allowing for the provision of standardized instructions and programmed, automated scoring. As such, constant supervision by trained professionals would no longer be necessary.
The BBT in this study has been simplified to focus on capturing the reaching and grasping of one block at a time that appears in the same location. This maximized the reproducibility of the movements and minimized the inter-subject variability in recorded data. Lastly, virtual reality headsets can be purchased for as little as $300 and have the potential to house multiple assessments. Once programmed, this would significantly decrease the cost associated with typical professional evaluation and allow for increased accessibility of these standard, validated clinical tests in both clinical and remote/at-home settings.

Pleins feux sur l’auteur : Améliorer la réadaptation à distance grâce à la réalité virtuelle et à l’électromyographie

Configuration pour l’évaluation quantitative du mouvement et de l’activité musculaire lors d’un test virtuel modifié en boîte et en bloc

Here in the Neuroengineering and Rehabilitation Laboratory at West Virginia University, we study functional mechanisms of sensory motor control, and develop objective assessment tools for the assessment of motor skills and movement impairment. Remote or virtual care has become a promising means to address healthcare disparities. However, there's a critical need to quantitatively characterize motor skills for use in these applications.Our protocol seeks to enable clinicians and researchers to obtain high resolution data on complex movements in order to better assess movement deficits remotely. This protocol aims to simplify complex and underused rehabilitation technologies by automating at home movement assessment. Its key advantage lies in integrating virtual reality, motion capture, and electromyography.For example, a VR headset with hand tracking captures movements for first analysis, while an EMG bracelet estimate muscle performance during virtual testing.

Configuration de capteurs EMG couplés à une caméra et d’un casque VR pour évaluer les mouvements et l’activité musculaire

Pour commencer, procurez-vous tous les composants du système requis pour le test de mouvement et d’activité musculaire. Allumez la station de base d’électromyographie et connectez-la à un ordinateur dédié. Pour configurer le serveur de capture de mouvement, connectez-le d’abord à un routeur réseau de liaison informatique, puis connectez-le à un moniteur et à des caméras pour la visualisation.Allumez le troisième ordinateur dédié connecté au casque de réalité virtuelle et chargez-y l’environnement de tâche prévu. À l’aide d’une fonction logicielle personnalisée, synchronisez les systèmes à temps. Pour préparer la zone de test, retirez tous les obstacles et placez une chaise robuste sans accoudoirs au centre.

setting-up-camera-coupled-emg-sensors-vr-headset-to-assess-motion

Research

JoVE Journal

Engineering

Setting Up Camera Coupled-EMG Sensors and VR Headset to Assess Motion and Muscle Activity

63 vues.  West Virginia University. Pour commencer, procurez-vous tous les composants du système requis pour le test de mouvement et d’activité musculaire. Allumez la station de base d’électromyographie et connectez-la à un ordinateur dédié. Pour configurer le serveur de capture de mouvement, connectez-le d’abord à un routeur réseau de liaison informatique, puis connectez-le à un moniteur et à des caméras pour la visualisation.Allumez le troisième ordinateur dédié connecté au casque de réalité virt...

Vidéo: Configuration de capteurs EMG couplés à une caméra et d’un casque VR pour évaluer les mouvements et l’activité musculaire

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

The ability to move allows us to interact with the world. When this ability is impaired, it can significantly reduce one's quality of life and independence and may lead to complications. The importance of remote patient evaluation and rehabilitation has recently grown due to limited access to in-person services. For example, the COVID-19 pandemic unexpectedly resulted in strict regulations, reducing access to non-emergent healthcare services. Additionally, remote care offers an opportunity to address healthcare disparities in rural, underserved, and low-income areas where access to services remains limited. 
Improving accessibility through remote care options would limit the number of hospital or specialist visits and render routine care more affordable. Finally, the use of readily available commercial consumer electronics for at-home care can enhance patient outcomes due to improved quantitative observation of symptoms, treatment efficacy, and therapy dosage. While remote care is a promising means to address these issues, there is a crucial need to quantitatively characterize motor impairment for such applications. The following protocol seeks to address this knowledge gap to enable clinicians and researchers to obtain high-resolution data on complex movement and underlying muscle activity. The ultimate goal is to develop a protocol for remote administration of functional clinical tests. 
Here, participants were instructed to perform a medically-inspired Box and Block task (BBT), which is frequently used to assess hand function. This task requires subjects to transport standardized cubes between two compartments separated by a barrier. We implemented a modified BBT in virtual reality to demonstrate the potential of developing remote assessment protocols. Muscle activation was captured for each subject using surface electromyography. This protocol allowed for the acquisition of high-quality data to better characterize movement impairment in a detailed and quantitative manner. Ultimately, these data have the potential to be used to develop protocols for virtual rehabilitation and remote patient monitoring.

The protocol described here aims to enhance the quantitative evaluation of upper limb deficits, with the goal of developing additional technology for remote assessment both in the clinic and at home. Virtual reality and biosensor technologies are combined with standard clinical techniques to provide insights into the functioning of the neuromuscular system.