Literature DB >> 24485500

Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring.

F Massé1, A K Bourke1, J Chardonnens1, A Paraschiv-Ionescu1, K Aminian2.   

Abstract

Despite its medical relevance, accurate recognition of sedentary (sitting and lying) and dynamic activities (e.g. standing and walking) remains challenging using a single wearable device. Currently, trunk-worn wearable systems can differentiate sitting from standing with moderate success, as activity classifiers often rely on inertial signals at the transition period (e.g. from sitting to standing) which contains limited information. Discriminating sitting from standing thus requires additional sources of information such as elevation change. The aim of this study is to demonstrate the suitability of barometric pressure, providing an absolute estimate of elevation, for evaluating sitting and standing periods during daily activities. Three sensors were evaluated in both calm laboratory conditions and a pilot study involving seven healthy subjects performing 322 sitting and standing transitions, both indoor and outdoor, in real-world conditions. The MS5611-BA01 barometric pressure sensor (Measurement Specialties, USA) demonstrated superior performance to counterparts. It discriminates actual sitting and standing transitions from stationary postures with 99.5% accuracy and is also capable to completely dissociate Sit-to-Stand from Stand-to-Sit transitions.
Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Keywords:  ADL; Activity recongnition; Atmospheric pressure; Barometric pressure; Daily activity; Postural transitions; Wearable monitoring

Mesh:

Year:  2014        PMID: 24485500     DOI: 10.1016/j.medengphy.2014.01.001

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  4 in total

1.  Using multiple barometers to detect the floor location of smart phones with built-in barometric sensors for indoor positioning.

Authors:  Hao Xia; Xiaogang Wang; Yanyou Qiao; Jun Jian; Yuanfei Chang
Journal:  Sensors (Basel)       Date:  2015-03-31       Impact factor: 3.576

2.  A sensor fusion method for tracking vertical velocity and height based on inertial and barometric altimeter measurements.

Authors:  Angelo Maria Sabatini; Vincenzo Genovese
Journal:  Sensors (Basel)       Date:  2014-07-24       Impact factor: 3.576

3.  A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson's Disease Patients.

Authors:  Daniel Rodríguez-Martín; Carlos Pérez-López; Albert Samà; Andreu Català; Joan Manuel Moreno Arostegui; Joan Cabestany; Berta Mestre; Sheila Alcaine; Anna Prats; María de la Cruz Crespo; Àngels Bayés
Journal:  Sensors (Basel)       Date:  2017-04-11       Impact factor: 3.576

4.  Validation of a Lower Back "Wearable"-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment.

Authors:  Minh H Pham; Elke Warmerdam; Morad Elshehabi; Christian Schlenstedt; Lu-Marie Bergeest; Maren Heller; Linda Haertner; Joaquim J Ferreira; Daniela Berg; Gerhard Schmidt; Clint Hansen; Walter Maetzler
Journal:  Front Neurol       Date:  2018-08-10       Impact factor: 4.003

  4 in total

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