Dynamic time warping approach for optimized locomotor impairment detection using biomedical signal processing

• Published in: Biomedical signal processing and control 2022-02, Vol.72, p.103321, Article 103321
• Main Authors: Adhikary, Subhrangshu, Ghosh, Arindam
• Format: Article
• Language: English
• Subjects: Biomedical signal processing; Disease detection; Gait disorders; Machine learning; Time series
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.103321

[Display omitted] •Tracked motion of 30 human subjects including healthy subjects and abnormal subjectswith help of smartphone embedded sensors.•Used Dynamic Time Warping algorithm to map similarites in pattern of different category of subjects based on locomotor disorders.•Used different machine learning algorithms and tested the performance boost for this approach. The study of walking pattern also known as gait is affected by several underlying musculoskeletal and neurological factors. All humans have contrasting gait pattern however the pattern lies within a predictable range for all human with no underlying health disorders affecting gait. However, deviation from a regular distribution range can indicate underlying health conditions. The gait could be regularly and inexpensively monitored in real-time with help of smartphones embedded sensors such as tri-axial accelerometers and tri-axial gyroscopes as smartphones are widely available. The advancements in computational power and artificial intelligence led to the development of several supervised machine learning algorithms which could be used to detect certain patterns associated with different gait conditions. However, the machine learning has widely been used to study different gait diseases, with this paper we have proposed a more efficient motion tracking approach and a new algorithm utilizing Dynamic Time Warping for an optimized locomotor disorder detection with affordable, practically applicable and globally deployable setup to monitor gait. In this experiment, we have tracked motion of 30 healthy and gait deformed human subjects with the proposed motion tracking approach and utilizing the proposed algorithm, we have achieved 35.95 times faster classification with up to 95.20% detection accuracy.