Undergraduate Research and Innovation Scholar
Using EIT to Model Lower Limb Muscle Movements in Virtual Reality.
- Human–Computer Interaction
- Medical Devices
Unsupervised physical rehabilitation traditionally has used motion tracking to determine correct exercise execution. However, motion tracking is not representative of the assessment of physical therapists, which focus on muscle engagement. In this paper, we investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises by showing users whether they target the right muscle groups. To accomplish this, we use wearable electrical impedance tomography (EIT) to monitor the muscle engagement and visualize the current state on a virtual muscle-skeleton avatar in VR. We use additional optical motion tracking to also monitor the user’ s movement. We run a user study with 10 participants that compares exercise execution while seeing muscle+motion data vs. motion data only, and also present the recorded data to a group of physical therapists for post-rehabilitation analysis. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy for users during rehabilitation, and post-rehabilitation evaluation for physical therapists.
Aashini is a fourth-year student at MIT double majoring in Mechanical and Electrical Engineering with a specific focus on Medical Devices. After graduation, she will go on to get her Ph.D. in Medical Engineering and Medical Physics as an HST student at MIT. In her free time, she loves making chai, learning guitar, and studying American Sign Language.