With the aging of the US population, there is an increasing need for effective and accessible rehabilitation services for debilitating illnesses and injuries such as stroke and arthritis. Intensive long-term rehabilitation is challenging to administer in an accessible and affordable way as it requires frequent trips to the clinic (usually supported by a caregiver), and significant one-on-one time with rehabilitation experts. Telemedicine and telehealth are gaining prominence as cost effective ways to deliver home-based health and wellness to wider populations. However, automated tele-rehabilitation is not currently feasible as the expert functions of the therapist cannot yet be fully automated and replicated in the home. In addition, there are significant technical, behavioral, and clinical challenges to scaling technology assisted home-based rehabilitation. This project aims to address these challenges through the development of a system for Semi-Automated Rehabilitation At Home (SARAH). The system is defined as semi-automated because it relies on the remote participation of the therapist for developing and adapting the therapy program. The SARAH system uses the remote therapists? instructions to guide the patient through daily intensive therapy sessions at the home. Using inexpensive sensing technologies that are non-intrusive and mindful of the patient?s privacy, the system records and analyzes the daily therapy sessions as well as the general activities of the patient in the home. The SARAH system then provides feedback to the patient based on their therapy activities and general movements around the home. The system also provides summaries of patient progress to the remote therapist so that they can adapt the program for subsequent therapy sessions. The first version of the SARAH system focuses on upper extremity stroke rehabilitation at the home as the team of researchers has significant experience in this space. Additional outputs from this project, including the development of a generalized system and relevant methodology, are designed to support a wide variety of home-based rehabilitation contexts.
The technical goals of the project are the development of movement assessment algorithms fusing knowledge based and data driven approaches. This fused approach produces automated patient assessment feedback during home-based therapy, and summaries of patient therapy and daily activities to assist the therapist with remote decision making. The project utilizes a Hierarchical Bayesian Model (HBM) approximating the therapist decision process as a common framework for the development of integrative cyber-human movement assessment algorithms. Therapy sessions are captured using two video cameras and four wearable Inertial Measurement Units (IMUs), while daily activity is only be tracked through the IMUs to estimate the wearer's 3D kinematics. The project fuses clinician?s expert knowledge of therapy tasks and segments with video and IMU data to implement automated segmentation and rating of therapy at the home. The fused cyber-human assessment of therapy data is used to inform the translation of low-level IMU feature tracking during daily life activities into daily movement summaries assisting remote therapy assessment and customization. The automated summaries include: therapy adherence, quality of therapy performance, quantity of patient daily activity and movement in the house, use of impaired limb, tasks detected during daily activity, and confidence of identification. The fusion of knowledge based and data driven approaches for computational movement analysis, as well as the cyber-human design process itself, will yield higher-level generalizable insights extending to many more applications of machine learning and deep learning in data-constrained scenarios. The low-cost sensor networks and wearable sensor solutions produced by the project will provide practical ways to monitor kinematics in real-world environments such as improved control systems for prosthetics and exoskeletons, prevention of workplace injuries through biofeedback, and enhancements in human-robot collaboration.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
