MIT Uses IoT Tech for Unsupervised Physical TherapyMIT Uses IoT Tech for Unsupervised Physical Therapy
The new system allows patients to conduct rehabilitation exercises without supervision
October 19, 2022
The digitization of health care has proven revolutionary for patient care, with advancements in wearable devices, biomedical robots and sensor technologies bringing previously unseen insights into patient biometrics and allowing for unique tailoring of therapies as a result.
Researchers are continuing to develop these devices to hone their accuracy and efficiency. The latest advancement comes from a team at MIT’s Computer Science and Artificial Intelligence Laboratory. The team worked in collaboration with Massachusetts General Hospital to create a new kind of unsupervised physical rehabilitation system, dubbed MuscleRehab.
The novel system was designed to offer patients physical relief without requiring a personal trainer, combining motion tracking with muscle-monitoring imaging and virtual reality (VR) to give patients direct insight into how their body is moving, and how to correctly perform exercises.
“We built the MuscleRehab to investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises, and helps post-rehabilitation evaluation for physical therapists,” said Junyi Zhu, lead author of the MuscleRehab study. “This contributes to improving the tele-rehabilitation efficiency for patients, and removes burden from the therapists to allow them to focus on more essential tasks and treat more people.
“I imagine this tech can be deployed in the tele-rehabilitation industry, as well as in sports for muscle injury recovery and prevention.”
To use MuscleRehab, the patient dons a VR headset and tracking suit, used to capture movement data and measure muscle activity while the wearer performs different exercises. The team analyzed the exercise accuracy seen when the patient used either simply the motion tracking data, or this data in combination with their muscle activity, and reported a 15% increase in accuracy when the patient could see both.
“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,” said Junyi Zhu, lead author of the study.
Currently, the tech focuses on the upper thigh area of the body and its associated muscle groups, though the team is working on expanding to focus on other areas of the body.
With ongoing pressures on a medical industry still reeling from the impacts of the pandemic, digital tools have become a crucial means of putting more capabilities in the hands of patients, easing operational pressures as the less crucial appointments can be conducted remotely.
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