Wearable Inertial Gait Algorithms: Impact of Wear Location and Environment in Healthy and Parkinson’s Populations

Celik, Yunus, Stuart, Sam, Woo, Wai Lok and Godfrey, Alan (2021) Wearable Inertial Gait Algorithms: Impact of Wear Location and Environment in Healthy and Parkinson’s Populations. Sensors, 21 (19). p. 6476. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s21196476


Wearable inertial measurement units (IMUs) are used in gait analysis due to their discrete wearable attachment and long data recording possibilities within indoor and outdoor environments. Previously, lower back and shin/shank-based IMU algorithms detecting initial and final contact events (ICs-FCs) were developed and validated on a limited number of healthy young adults (YA), reporting that both IMU wear locations are suitable to use during indoor and outdoor gait analysis. However, the impact of age (e.g., older adults, OA), pathology (e.g., Parkinson′s Disease, PD) and/or environment (e.g., indoor vs. outdoor) on algorithm accuracy have not been fully investigated. Here, we examined IMU gait data from 128 participants (72-YA, 20-OA, and 36-PD) to thoroughly investigate the suitability of ICs-FCs detection algorithms (1 × lower back and 1 × shin/shank-based) for quantifying temporal gait characteristics depending on IMU wear location and walking environment. The level of agreement between algorithms was investigated for different cohorts and walking environments. Although mean temporal characteristics from both algorithms were significantly correlated for all groups and environments, subtle but characteristically nuanced differences were observed between cohorts and environments. The lowest absolute agreement level was observed in PD (ICC2,1 = 0.979, 0.806, 0.730, 0.980) whereas highest in YA (ICC2,1 = 0.987, 0.936, 0.909, 0.989) for mean stride, stance, swing, and step times, respectively. Absolute agreement during treadmill walking (ICC2,1 = 0.975, 0.914, 0.684, 0.945), indoor walking (ICC2,1 = 0.987, 0.936, 0.909, 0.989) and outdoor walking (ICC2,1 = 0.998, 0.940, 0.856, 0.998) was found for mean stride, stance, swing, and step times, respectively. Findings of this study suggest that agreements between algorithms are sensitive to the target cohort and environment. Therefore, researchers/clinicians should be cautious while interpreting temporal parameters that are extracted from inertial sensors-based algorithms especially for those with a neurological condition.

Item Type: Article
Additional Information: Funding information: Yunus Celik is supported in his PhD study at Northumbria University by the Turkish Ministry of National Education. This work was supported, in part, by a Parkinson’s Foundation Post-Doctoral Fellowship for Basic Scientists (PF-FBS-1898-18-21) and a Clinical Research Award (PF-CRA-2073) to Dr Samuel Stuart.
Uncontrolled Keywords: gait analysis; wearable electronic devices; computing methodologies; patient outcome assessment
Subjects: B800 Medical Technology
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Health and Life Sciences > Sport, Exercise and Rehabilitation
Depositing User: Elena Carlaw
Date Deposited: 28 Sep 2021 13:36
Last Modified: 28 Sep 2021 13:36
URI: http://nrl.northumbria.ac.uk/id/eprint/47382

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