MIT and MGH create virtual reality system to advance home physical therapy

MIT and MGH create virtual reality system to advance home physical therapy

Researchers from MIT and Massachusetts General Hospital have created a movement and muscle engagement monitoring system for unsupervised physical rehabilitation that could help with injuries and improve mobility in older adults and athletes, say- they.


Disability conditions benefit from physical rehabilitation, but there are not enough physiotherapists for everyone.

To better enable data-driven, unsupervised rehabilitation for recovering athletes, patients currently undergoing physical therapy, or those with limiting physical illnesses, researchers at MIT Computer Science and Artificial Intelligence Laboratory and MGH have created a wearable device based on sensors and a virtual reality platform, MuscleRehab.

The system calculates muscle engagement and visualizes it on a virtual muscle skeleton avatar. It uses an imaging technique called electrical impedance tomography that measures how muscles engage with a VR headset and tracking suit that allows patients to watch themselves perform alongside a physical therapist.

The researchers, who are preparing to present their work for the first time, say studies of the system show that monitoring and visualizing muscle engagement during unsupervised physical rehabilitation can improve therapy accuracy and post-rehabilitation assessments. , and possibly prevent further injury.

“By actively measuring deep muscle engagement, we can observe whether the data is abnormal compared to a patient’s baseline, to provide insight into potential muscle trajectory,” said MIT PhD student Junyi Zhu. CSAIL and lead author of an article on MuscleRehab in today’s review. announcement.

The system includes a training regimen with pre-recorded baseline standards for the regimen and streams the avatar with real-time muscle engagement.

Patients wear the tracking suit and VR to capture their 3D motion data, then perform various exercises, such as lunges, knee bends, deadlifts, leg raises, knee extensions, squats, hydrants and bridges, which measure quadriceps, sartorius, hamstrings. and adductor activity.

The EIT sensor board comes with two straps filled with electrodes that are slipped over the user’s upper thigh to capture 3D volumetric data. Using a motion capture system, EIT sensing data displays actively triggered muscles on the screen, where the muscles darken with more engagement.

The team compared exercise accuracy with and without the EIT wearable. In both cases, their avatar performs alongside a physiotherapist.

A professional physiotherapist explained which muscle groups were supposed to be worked during each of the exercises. They compared the two results – with just the motion tracking data overlaid on their patient avatar and adding the EIT sensing straps which provide feedback and visualization of movement and muscle engagement.

By visualizing both muscle engagement and movement data during these unsupervised exercises instead of movement alone, overall exercise accuracy improved by 15% among test subjects.

The researchers also compared how long during exercises the correct muscle group was triggered with and without the wearable.

By monitoring and recording the most engagement data, physiotherapists reported a much better understanding of the patient’s exercise quality, and it helped to better assess their current regimen and exercise based on these statistics.

Zhu wanted to find a better way than the electromyography used by some wearable devices to detect the engagement (blood flow, stretch and contraction) of different muscle layers, and was inspired by EIT, which measures the electrical conductivity of muscles. , i.e. typically used to monitor lung function, detect chest tumors, and diagnose pulmonary embolisms.

Currently, MuscleRehab focuses on the upper thigh and major muscle groups within, but may extend to the glutes.

Co-authors of the paper include scientist Hamid Ghaednia, instructor in the Department of Orthopedic Surgery at Harvard Medical School and co-director of the Center for Physical Artificial Intelligence at Mass General Hospital, and Dr. Joseph Schwab, chief of the Orthopedic Spine Center, director of Spine Oncology and Co-Director of the Stephan L. Harris Chordoma Center and Associate Professor of Orthopedic Surgery at Harvard Medical School.


There is a growing trend to use technologies such as remote patient monitoring to care for patients and ease the burden on hospitals and providers.

Innovation with clinicians and patients open and ready to embrace new solutions is in its infancy, said Dr. Waqaas Al-Siddiq, President, CEO and Founder of Biotricity. Health Informatics News in March.

“We can advance RPM by looking at the diagnostic devices that currently exist for each condition, find which sensors can be integrated into wireless devices, and create ongoing, clinically relevant solutions,” he said. .

Just as RPM can significantly reduce hospital readmissions and emergency room visits, new sensor-based technologies can encourage home health care approaches and have the potential to improve outcomes and reduce visits. in person.


“This work advances EIT, a sensing approach traditionally used in clinical settings, with an ingenious and unique combination with virtual reality,” said Yang Zhang, assistant professor of electrical and computer engineering at UCLA Samueli School of Science. Engineering, in the ad.

“The enabled app that facilitates rehabilitation has the potential to have a broad impact in society to help patients perform physical rehabilitation safely and effectively at home.

Andrea Fox is the editor of Healthcare IT News.

Healthcare IT News is a HIMSS publication.

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