2022
DOI: 10.1109/jsen.2022.3216459
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Classifying Gait Alterations Using an Instrumented Smart Sock and Deep Learning

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 comp… Show more

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Cited by 3 publications
(1 citation statement)
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“…5 Most importantly, studies have integrated vibration, motion, and accelerometer sensors to produce e-yarns to detect fall or near fall, monitor vibration in the index finger and palm to classify gait alterations, respectively. [6][7][8] Other notable e-yarns are the development of light-emitting diode (LED) e-yarns, which are embedded into clothing for applications in health care, art, fashion, entertainment, and road safety (to make pedestrians visible). 9 LED e-yarns are usually fabricated by embedding LEDs into thin wires or conductive yarns to produce light when supplied with an external source of power.…”
Section: Introductionmentioning
confidence: 99%
“…5 Most importantly, studies have integrated vibration, motion, and accelerometer sensors to produce e-yarns to detect fall or near fall, monitor vibration in the index finger and palm to classify gait alterations, respectively. [6][7][8] Other notable e-yarns are the development of light-emitting diode (LED) e-yarns, which are embedded into clothing for applications in health care, art, fashion, entertainment, and road safety (to make pedestrians visible). 9 LED e-yarns are usually fabricated by embedding LEDs into thin wires or conductive yarns to produce light when supplied with an external source of power.…”
Section: Introductionmentioning
confidence: 99%