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A wearable sensor and framework for accurate remote monitoring of human motion

  • Remote monitoring and evaluation of human motion during daily life require accurate extraction of kinematic quantities of body segments. Current approaches use inertial sensors that require numerical time differentiation to access the angular acceleration vector, a mathematical operation that greatly increases noise in the acceleration value. Here we introduce a wearable sensor that utilises aRemote monitoring and evaluation of human motion during daily life require accurate extraction of kinematic quantities of body segments. Current approaches use inertial sensors that require numerical time differentiation to access the angular acceleration vector, a mathematical operation that greatly increases noise in the acceleration value. Here we introduce a wearable sensor that utilises a spatially defined cluster of inertial measurement units on a rigid base for directly measuring the angular acceleration vector. For this reason, we used computational modelling and experimental data to demonstrate that our new sensor configuration improves the accuracy of tracking angular acceleration vectors. We confirmed the feasibility of tracking human movement by automatic assessment of experimental fall initiation and balance recovery responses. The sensor therefore presents an opportunity to pioneer reliable assessment of human movement and balance in daily life.show moreshow less

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Metadaten
Document Type:Article (reviewed)
Zitierlink: https://opus.hs-offenburg.de/8727
Bibliografische Angaben
Title (English):A wearable sensor and framework for accurate remote monitoring of human motion
Author:Maximilian GießlerStaff MemberORCiDGND, Julian Werth, Bernd WaltersbergerStaff MemberGND, Kiros Karamanidis
Year of Publication:2024
Date of first Publication:2024/01/30
Publisher:Springer Nature
First Page:1
Last Page:15
Article Number:20
Parent Title (English):Communications Engineering
Volume:3
ISSN:2731-3395
DOI:https://doi.org/10.1038/s44172-024-00168-6
URL:https://www.nature.com/articles/s44172-024-00168-6
Language:English
Inhaltliche Informationen
Institutes:Fakultät Maschinenbau und Verfahrenstechnik (M+V)
Institutes:Bibliografie
Tag:Near Falls
Formale Angaben
Relevance:Wiss. Zeitschriftenartikel reviewed: Sonstiger Nachweis des Review-Verfahrens
Open Access: Open Access 
 Gold 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International