![]() ![]() The network was incapable of correctly differentiating foot slap (21.2%) and steppage gait (4.8%). ![]() 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%). When only a single accelerometer was utilised for classification, the ankle accelerometer generated the most accurate results in comparison to the other two. 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%. Four Neural Networks and an SVM were tested to ascertain the most effective method of automatic data classification. 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. Human trials were conducted on 12 able-bodied participants, an instrumented sock was worn on each foot. Seamless instrumented socks were fabricated using three accelerometer embedded yarns, positioned at the toe (hallux), above the heel and on the lateral malleolus. 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. ![]()
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