Abstract-We present an approach to wearable sensor-based assessment of motor function in individuals post stroke. We make use of one on-body inertial measurement unit (IMU) to automate the functional ability (FA) scoring of the Wolf Motor Function Test (WMFT). WMFT is an assessment instrument used to determine the functional motor capabilities of individuals post stroke. It is comprised of 17 tasks, 15 of which are rated according to performance time and quality of motion. We present signal processing and machine learning tools to estimate the WMFT FA scores of the 15 tasks using IMU data. We treat this as a classification problem in multidimensional feature space and use a supervised learning approach.
Promoting functional recovery after stroke requires collaborative and innovative approaches to neurorehabilitation research. Task-oriented training (TOT) approaches that include challenging, adaptable, and meaningful activities have led to successful outcomes in several large-scale multisite definitive trials. This, along with recent technological advances of virtual reality and robotics, provides a fertile environment for furthering clinical research in neurorehabilitation. Both virtual reality and robotics make use of multimodal sensory interfaces to affect human behavior. In the therapeutic setting, these systems can be used to quantitatively monitor, manipulate, and augment the users' interaction with their environment, with the goal of promoting functional recovery. This article describes recent advances in virtual reality and robotics and the synergy with best clinical practice. Additionally, we describe the promise shown for automated assessments and in-home activity-based interventions. Finally, we propose a broader approach to ensuring that technology-based assessment and intervention complement evidence-based practice and maintain a patient-centered perspective.
Abstract-We present a study with an autonomous Socially Assistive Robot (SAR) coach that investigates the effect of comparative feedback given by a SAR on the self-efficacy of individuals post-stroke in a seated reaching task. We compare two types of feedback, self-comparative and othercomparative, against a control of no comparative feedback, with 23 participants post-stroke. We find that participants receiving other-comparative feedback have significantly more delay time on the task than participants receiving self or no comparative feedback. In addition, we demonstrate that participants show task performance improvement over time, and provide responses to self-efficacy probes that vary along several dimensions.
Abstract-The advent of new health sensing technologies has presented us with the opportunity to gain richer data from patients undergoing clinical interventions. Such technologies are particularly suited for applications requiring temporal accuracy. The Wolf Motor Function Test (WMFT) is one such application. This assessment is an instrument used to determine functional ability of the paretic and non-paretic limbs in individuals poststroke . It consists of 17 tasks, 15 of which are scored according to both time and a functional ability scale. We propose a technique that uses wearable sensors and performance sensors to estimate the timing of seven of these tasks. We have developed a sensing framework and an algorithm to automatically detect total movement time. We have validated the system's accuracy on the seven selected WMFT tasks. We also suggest how this framework can be adapted to the remaining tasks.
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