Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis

Abstract We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently...

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Autores principales:	Adrian Derungs, Oliver Amft
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ca5c4f25e12b463190f470f24354f167
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spelling	oai:doaj.org-article:ca5c4f25e12b463190f470f24354f1672021-12-02T15:39:58ZEstimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis10.1038/s41598-020-68225-62045-2322https://doaj.org/article/ca5c4f25e12b463190f470f24354f1672020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-68225-6https://doaj.org/toc/2045-2322Abstract We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.Adrian DerungsOliver AmftNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Adrian Derungs Oliver Amft Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
description	Abstract We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.
format	article
author	Adrian Derungs Oliver Amft
author_facet	Adrian Derungs Oliver Amft
author_sort	Adrian Derungs
title	Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
title_short	Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
title_full	Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
title_fullStr	Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
title_full_unstemmed	Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
title_sort	estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ca5c4f25e12b463190f470f24354f167
work_keys_str_mv	AT adrianderungs estimatingwearablemotionsensorperformancefrompersonalbiomechanicalmodelsandsensordatasynthesis AT oliveramft estimatingwearablemotionsensorperformancefrompersonalbiomechanicalmodelsandsensordatasynthesis
_version_	1718385899950047232

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis

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