Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision)
Johanna Spangler, Marc Mitjans, Ashley Collimore, Aysha Gomes-Pires, David M Levine, Roberto Tron, Lou Awad- Physical Therapy, Sports Therapy and Rehabilitation
Abstract
Objective
Functional movement assessments are routinely used to evaluate and track changes in mobility. The objective of this study was to evaluate a multimodal movement monitoring system developed for autonomous, home-based, functional movement assessment.
Methods
Fifty frail and pre-frail adults were recruited from the Brigham and Women’s Hospital at Home program to evaluate the feasibility and accuracy of applying the multimodal movement monitoring system to autonomously recognize and score functional activities collected in the home. Study subjects completed sit-to-stand, standing balance (romberg, semi-tandem, and tandem), and walking test activities in likeness to the Short Physical Performance Battery. Test activities were identified and scored manually, and by multimodal activity recognition and scoring algorithms trained on lab-based biomechanical data to integrate wearable inertial measurement unit (IMU) and external red-blue-green-depth vision data. Feasibility was quantified as the proportion of completed tests that were analyzable. Accuracy was quantified as agreement between actual and system-identified activities. In an exploratory analysis of a subset of functional activity test data, accuracy of a preliminary activity-scoring algorithm was also evaluated.
Results
Activity recognition by the IMU-vision system had good feasibility and high accuracy. Of 271 test activities collected in the home, 217 (80%) were analyzable by the activity-recognition algorithm, which overall correctly identified 206 (95%) of the analyzable activities: 100% of walking, 97% of balance, and 82% of sit-to-stand activities (χ2 (2) = 19.9). In the subset of 152 tests suitable for activity scoring, automatic and manual scores showed substantial agreement (Kw = 0.76 [0.69,0.83]).
Conclusions
Autonomous recognition and scoring of home-based functional activities is enabled by a multimodal movement monitoring system that integrates IMU and vision data. Further algorithm training with ecologically-valid data, and a kitted system that is independently usable by patients, are needed before fully autonomous, functional movement assessment is realizable.
Impact
Functional movement assessments that can be administered in the home without a clinician present have potential to democratize these evaluations and improve care access.