Sun, R.; Aldunate, R.G.; Sosnoff, J.J. The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment. Sensors2019, 19, 2183.
Sun, R.; Aldunate, R.G.; Sosnoff, J.J. The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment. Sensors 2019, 19, 2183.
Sun, R.; Aldunate, R.G.; Sosnoff, J.J. The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment. Sensors2019, 19, 2183.
Sun, R.; Aldunate, R.G.; Sosnoff, J.J. The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment. Sensors 2019, 19, 2183.
Abstract
Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in the aging population. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLensTM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older, 12 young) participated in this study. An automated functional mobility assessment app was developed based on the HoloLens platform. Mobility performance was recorded with the headset built-in sensor and validated with reference inertial sensor (Opal, APDM) taped on the headset and lower back. Results indicate vertical kinematic measures by HoloLens was in good agreement with the reference sensor (Normalized RMSE ~ 10%). Additionally, the HoloLens-based test completion time was in perfect agreement with clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users to complete common mobility tests with good measurement accuracy, thus it has great potential to provide objective and efficient sensor-based mobility assessment.
Keywords
mixed reality headset; mobility assessment; wearable sensor; fall risk; aging
Subject
Medicine and Pharmacology, Orthopedics and Sports Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
