Wrist-worn devices, such as smartwatches and smart bands, have brought about the unprecedented opportunity to continuously monitor gait during daily routines. However, the use of a single wrist-worn unit for gait analysis is challenging for a variety of reasons. Indeed, the signal collected at the user's wrist is subject to a significant “noise” with respect to other body positions (e.g. waist), mainly due to the arm swing while walking and other unpredictable hand movements. The aim of this paper is to investigate the design and evaluation of a lightweight and reliable gait detection technique for wrist-worn devices. To this end, the proposed method creates a personalized model of the user's gait patterns. The model is created through an automatic training phase, which requires the temporary use of an additional device (smartphone) to gather true gait segments. After, anomaly detection is used to distinguish gait from other activities. Gait data from 20 volunteers have been collected to test and evaluate the proposed technique. Volunteers were asked to walk at different pace, with their normal arm swing or placing the hand inside of a pocket. Results show that the proposed method can reliably distinguish gait from spurious hand movements.

Personalized gait detection using a wrist-worn accelerometer

COLA, GUGLIELMO;AVVENUTI, MARCO;VECCHIO, ALESSIO
2017

Abstract

Wrist-worn devices, such as smartwatches and smart bands, have brought about the unprecedented opportunity to continuously monitor gait during daily routines. However, the use of a single wrist-worn unit for gait analysis is challenging for a variety of reasons. Indeed, the signal collected at the user's wrist is subject to a significant “noise” with respect to other body positions (e.g. waist), mainly due to the arm swing while walking and other unpredictable hand movements. The aim of this paper is to investigate the design and evaluation of a lightweight and reliable gait detection technique for wrist-worn devices. To this end, the proposed method creates a personalized model of the user's gait patterns. The model is created through an automatic training phase, which requires the temporary use of an additional device (smartphone) to gather true gait segments. After, anomaly detection is used to distinguish gait from other activities. Gait data from 20 volunteers have been collected to test and evaluate the proposed technique. Volunteers were asked to walk at different pace, with their normal arm swing or placing the hand inside of a pocket. Results show that the proposed method can reliably distinguish gait from spurious hand movements.
978-1-5090-6244-7
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/863630
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