Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults

• Published in: Sensors (Basel, Switzerland) Switzerland), 2019-10, Vol.19 (20), p.4537
• Main Authors: O'Brien, Megan K, Hidalgo-Araya, Marco D, Mummidisetty, Chaithanya K, Vallery, Heike, Ghaffari, Roozbeh, Rogers, John A, Lieber, Richard, Jayaraman, Arun
• Format: Article
• Language: English
• Subjects: Acceleration; Adult; Age; Aged; Algorithms; Angular velocity; Balance; berg balance scale; Clinical outcomes; fall risk; Female; Gait; gait events; gait impairment; Gait recognition; Humans; Inertial platforms; Kinematics; Male; Middle Aged; Outcome Assessment, Health Care; Postural Balance; postural sway; Posture; Quality of life; Recovery (Medical); rehabilitation; Sensors; Signal processing; Stroke; Stroke - physiopathology; Stroke Rehabilitation; ten-meter walk test; timed up and go; Velocity; Walking; Wearable computers; Wearable Electronic Devices; wearable sensors; Wearable technology; Young Adult
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19204537

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20-70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.