Classifying gait alterations using an instrumented smart sock and deep learning

Lugoda, Pasindu; Hayes, Stephen Clive; Hughes-Riley, Theodore; Turner, Alexander; Martins, Mariana V.; Cook, Ashley; Raval, Kaivalya; Oliveira, Carlos; Breedon, Philip; Dias, Tilak

Abstract

This paper presents a non-invasive method of classifying gait patterns associated with various movement disorders and/or neurological conditions, utilising unobtrusive, instrumented socks and a deep learning network. Seamless instrumented socks were fabricated using three accelerometer embedded yarns, positioned at the toe (hallux), above the heel and on the lateral malleolus. Human trials were conducted on 12 able-bodied participants, an instrumented sock was worn on each foot. Participants were asked to complete seven trials consisting of their typical gait and six different gait types that mimicked the typical movement patterns associated with various movement disorders and neurological conditions. Four Neural Networks and an SVM were tested to ascertain the most effective method of automatic data classification. The Bi-LSTM generated the most accurate results and illustrates that the use of three accelerometers per foot increased classification accuracy compared to a single accelerometer per foot by 11.4%. When only a single accelerometer was utilised for classification, the ankle accelerometer generated the most accurate results in comparison to the other two. The network was able to correctly classify five different gait types: stomp (100%), shuffle (66.8%), diplegic (66.6%), hemiplegic (66.6%) and “normal walking” (58.0%). The network was incapable of correctly differentiating foot slap (21.2%) and steppage gait (4.8%). This work demonstrates that instrumented textile socks incorporating three accelerometer yarns were capable of generating sufficient data to allow a neural network to distinguish between specific gait patterns. This may enable clinicians and therapists to remotely classify gait alterations and observe changes in gait during rehabilitation.

Citation

Lugoda, P., Hayes, S. C., Hughes-Riley, T., Turner, A., Martins, M. V., Cook, A., Raval, K., Oliveira, C., Breedon, P., & Dias, T. (2022). Classifying gait alterations using an instrumented smart sock and deep learning. IEEE sensors journal, 22(23), 23232-23242. https://doi.org/10.1109/JSEN.2022.3216459

• Journal Article Type: Article
• Acceptance Date: Aug 31, 2022
• Online Publication Date: Oct 27, 2022
• Publication Date: Dec 1, 2022
• Deposit Date: May 24, 2023
• Publicly Available Date: May 25, 2023
• Journal: IEEE Sensors Journal
• Print ISSN: 1530-437X
• Electronic ISSN: 1558-1748
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 22
• Issue: 23
• Pages: 23232-23242
• DOI: https://doi.org/10.1109/JSEN.2022.3216459
• Keywords: Biomedical equipment; Electronic textiles (E-textiles); Gait monitoring; Long short-term memory (LSTM); Machine learning; Sensors; Smart textiles; Wearable sensors
• Public URL: https://hull-repository.worktribe.com/output/4132774