Gait quantification and visualization for digital healthcare

Anwary, Arif Reza; Yu, Hongnian; Vassallo, Michael

Abstract

Gait abnormalities are common in clinical practice and there is a global imperative to improve technologies that facilitate their detection, evaluation, monitoring and management. Real time evaluation using digital technology supports the development of digital healthcare. Currently gait assessment relies on visual observation of structured clinical tests such as the “Timed Get up and Go Test.” Gold standard methods such as “Qualisys Motion Capture System” require sophisticated equipment in gait laboratories. These are not widely available due to expense, analysis time and requirement of trained technicians. Developing low cost, portable, easy to use digital technology is important to enable sophisticated assessment of gait at home or in clinics. Common measures for quantification of gait include symmetry angle, ratio and index. These measurements may be difficult to interpret by users as stand-alone values. To facilitate the evaluation and interpretation of locomotive information, a tool to visualize gait in real-time is proposed. The proposed tool consists of five approaches (1: Real-time dial visualization, 2: Visualization of individual leg time variation, 3: Visualization of both legs asymmetry, 4: Boxplot visualization, and 5: Evaluation considering all features). Results show that wearable Inertial Measurement Unit (IMU) can be used for extraction of objective gait features. This system opens possibilities for home-based assessment of gait without the requirement and expense of an elaborate laboratory setup and supports the development of digital healthcare.

Citation

Anwary, A. R., Yu, H., & Vassallo, M. (2020). Gait quantification and visualization for digital healthcare. Health Policy and Technology, 9(2), 204-212. https://doi.org/10.1016/j.hlpt.2019.12.004

• Journal Article Type: Article
• Acceptance Date: Dec 30, 2019
• Online Publication Date: Jun 13, 2020
• Publication Date: 2020-06
• Deposit Date: Jun 17, 2022
• Journal: Health Policy and Technology
• Print ISSN: 2211-8837
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 2
• Pages: 204-212
• DOI: https://doi.org/10.1016/j.hlpt.2019.12.004
• Keywords: Gait visualization; Gait asymmetry; Gait feature extraction; Inertial Measurement Unit; Gait Analysis
• Public URL: http://researchrepository.napier.ac.uk/Output/2879975