Balance Assessment Using a Smartwatch Inertial Measurement Unit with Principal Component Analysis for Anatomical Calibration

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 10; p. 4585
• Main Authors: Presley, Benjamin M, Sklar, Jeffrey C, Hazelwood, Scott J, Berg-Johansen, Britta, Klisch, Stephen M
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09-05-2023; MDPI
• Subjects: Acceleration; Aged; Algorithms; Analysis; Balance; Benchmarking; biomechanics; Bone Plates; Brain; Calibration; coordinate system discovery; Humans; Inertial coordinates; inertial measurement unit; Inertial platforms; Injuries; Measurement; posturography; Principal Component Analysis; Principal components analysis; Sensors; Smartwatches; Technology assessment; Traumatic brain injury; Velocity; Wearable computers; inertial measurement unit; smartwatch; balance; coordinate system discovery; posturography; wearables; biomechanics; calibration; principal component analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23104585

Balance assessment, or posturography, tracks and prevents health complications for a variety of groups with balance impairment, including the elderly population and patients with traumatic brain injury. Wearables can revolutionize state-of-the-art posturography methods, which have recently shifted focus to clinical validation of strictly positioned inertial measurement units (IMUs) as replacements for force-plate systems. Yet, modern anatomical calibration (i.e., sensor-to-segment alignment) methods have not been utilized in inertial-based posturography studies. Functional calibration methods can replace the need for strict placement of inertial measurement units, which may be tedious or confusing for certain users. In this study, balance-related metrics from a smartwatch IMU were tested against a strictly placed IMU after using a functional calibration method. The smartwatch and strictly placed IMUs were strongly correlated in clinically relevant posturography scores (r = 0.861-0.970,
< 0.001). Additionally, the smartwatch was able to detect significant variance (
< 0.001) between pose-type scores from the mediolateral (ML) acceleration data and anterior-posterior (AP) rotation data. With this calibration method, a large problem with inertial-based posturography has been addressed, and wearable, "at-home" balance-assessment technology is within possibility.