MIT System “Sees” The Inner Structure of Body During Physical Rehab

MIT System “Sees” The Inner Structure of Body During Physical RehabMIT System “Sees” The Inner Structure of Body During Physical Rehab

What You Should Know:

– Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital have created an unsupervised physical rehabilitation system called MuscleRehab.

– Motion tracking captures motion activity, while Electrical Impedance Tomography measures “what the muscles are up to”, whereas a virtual reality headset and tracking suit lets one watch themselves perform alongside a physical therapist.

Virtual Reality Driven Physical Rehabilitation

Around three-quarters of years lived with disability are conditions that could benefit from physical rehabilitation – but there aren’t enough physical therapists (PT) to go around. The growing need is racing alongside population growth, and aging as well as higher rates of severe ailments, even within the same age group, are contributing to the problem. 

An upsurge in sensor-based techniques, such as on-body motion sensors, has provided some autonomy and precision for patients. Still, the minimalist watches and rings largely rely on motion data, therefore lacking the more holistic picture a PT pieces together, including muscle engagement and tension, in addition to movement.  This muscle-motion language barrier, so to speak, prompted the creation of an unsupervised physical rehabilitation system, MuscleRehab by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH).

For patients, they simply put on the sleek black tracking suit and VR that captures 3-D movement data, and then perform various exercises such as lunges, knee bends, deadlifts, leg raises, knee extensions, squats, fire hydrants, and bridges that measure activity of quadriceps, sartorius, hamstrings, and adductors. In the virtual environment, patients were given two conditions. In both cases their avatar performs alongside a physical therapist. In the first situation,  just the motion tracking data is overlaid onto their patient avatar. In the second situation, the patient puts on the EIT sensing straps, and then they have all the information of the motion and the muscle engagement. 

With these two conditions, the team compared the exercise accuracy and handed the results to a professional therapist, who explained which muscle groups were supposed to be engaged during each of the exercises. By visualizing both muscle engagement and motion data during these unsupervised exercises instead of just motion alone, the overall accuracy of exercises improved by fifteen percent. 

The team then did a cross-comparison of how much time during the exercises the correct muscle group got triggered between the two conditions. In the condition where we show the muscle engagement data in real-time, that’s the feedback. By monitoring and recording the most engagement data, the PT’s reported a much better understanding of the quality of the patient’s exercise, and that it helped to better evaluate their current regime and exercise based on those stats.

With MuscleRehab, the EIT sensing board serves as the “brains” behind the system. It’s accompanied by two straps filled with electrodes that are slipped onto a user’s upper thigh to capture 3D volumetric data. The motion capturing – they used “OptiTrack” – uses 39 markers and a bunch of cameras that sense super high frame rates per second. The EIT sensing data showed actively triggered muscles highlighted on the display, and a given muscle would become darker with more engagement. 

Currently, MuscleRehab focuses on the upper thigh and the major muscle groups inside, but down the line they’d like to expand to the glutes. 

“This work advances EIT, a sensing approach conventionally used in clinical settings, with an ingenious and unique combination with virtual reality,” says Yang Zhang, Assistant Professor in Electrical & Computer Engineering, UCLA Samueli School of Engineering. “The enabled application that facilitates rehabilitation potentially has a wide impact across society to help patients conduct physical rehabilitation safely and effectively at home. Such tools to eliminate the need for clinical resources and personnel have long been needed for the lack of workforce in healthcare.”