MuscleRehab: A Sensor-Based Suit For Autonomous Physical Rehabilitation

Physical therapy and musculoskeletal rehabilitation are important treatment methods that help people recover from injury or illness and rebuild strength and mobility in the muscles and joints. Generally, this kind of treatment is prescribed to those recovering from an accident, injury, or disease that either deteriorates or impedes the movement of muscles or joints.

Image Credit: MIT

Each day, there are people who are waiting for or would benefit from access to physical rehabilitation. However, one of the major challenges when it comes to helping these individuals is the ever-growing waiting list due to the fact there are not enough physical therapists (PTs) available. This problem is exacerbated by a growing, aging population as well as the impact that the COVID-19 pandemic has had on healthcare in general.

Now, a group of researchers based at MIT have developed a motion monitoring system called MuscleRehab, that uses sensors for autonomous physical rehabilitation. MuscleRehab incorporates optical motion tracking and electrical impedance tomography to visualize a patient’s muscle engagement and motion data, which can be performed during an unsupervised physical rehabilitation session.

Body Motion Sensors

In recent years, unsupervised monitoring technology to aid PTs and help them focus on essential treatment has become a more popular approach. However, one of the challenges this presents is that it relies heavily on the patient’s subjective assessment of their recovery routines and progress.

With it becoming more popular and easier to access, sensor-based monitoring gives patients a level of autonomy without compromising the accuracy of the data required for recovery.

Smart devices and wearables such as watches and rings are currently the most widely used technologies in rehabilitation scenarios. However, these are limited as they do not provide a holistic overview of a patient’s progress.

We wanted our sensing scenario to not be limited to a clinical setting, to better enable data-driven unsupervised rehabilitation for athletes in injury recovery, patients currently in physical therapy, or those with physical limiting ailments, to ultimately see if we can assist with not only recovery, but perhaps prevention.

Junyi Zhu, Lead Author, and Ph.D. Student in Electrical Engineering and Computer Science, MIT

The MuscleRehab system is comprised of three main features; electrical impedance tomography (EIT), a body suit with body motion sensors, and a virtual reality (VR) headset. The system visualizes muscle engagement and motion data during unsupervised exercise, which collects a comprehensive overview of the patient’s physical movement and range of motion, which the PT can then analyze.

By actively measuring deep muscle engagement, we can observe if the data is abnormal compared to a patient’s baseline, to provide insight into the potential muscle trajectory.

Junyi Zhu, Lead Author, and Ph.D. Student in Electrical Engineering and Computer Science, MIT

Improving Accessibility to Physical Therapy

Using the VR headset, patients can follow the instructions of a PT in a virtual environment with an avatar performing the actions and exercises alongside the therapist. The EIT optical sensing system is not only able to gather all the relevant data on both motion and muscle engagement, but it can also check if the right muscle groups are being engaged during the exercises.

The sensing data that EIT is able to transmit highlights the actively triggered muscles as specific muscles, and they change color and become darker with more engagement. The team saw a 15% improvement in the accuracy of the exercises and data using the system with EIT.

PTs were able to analyze the overall quality of the patient’s exercise when using the system, allowing them to better understand their own working methods and exercise routines based on the data and statistics provided by the MuscleRehab system.

To hone the capabilities of the MuscleRehab system, the MIT team has been focusing on the upper thigh and nearby muscle groups but hopes to be able to incorporate the glutes into the system’s capabilities soon.

The team will also evaluate other application methods of the technology and examine the prospect of using EIT radiotherapy. MuscleRehab could facilitate more accurate ‘at home’ rehabilitation, which would significantly impact physical therapy as it relieves the pressure on PTs and improves patient accessibility.

References and Further Reading

Zhu, J., et al., (2022) MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement. [online] Hcie.csail.mit.edu. Available at: https://hcie.csail.mit.edu/research/musclerehab/musclerehab.html

Gordon, R., (2022) MIT system “sees” the inner structure of the body during physical rehab. [online] MIT News | Massachusetts Institute of Technology. Available at: https://news.mit.edu/2022/mit-system-sees-inner-structure-body-during-physical-rehab-1011

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David J. Cross

Written by

David J. Cross

David is an academic researcher and interdisciplinary artist. David's current research explores how science and technology, particularly the internet and artificial intelligence, can be put into practice to influence a new shift towards utopianism and the reemergent theory of the commons.

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