Classifying Diverse Physical Activities Using “Smart Garments”

Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-te...

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Main Authors: Mohammad Iman Mokhlespour Esfahani, Maury A. Nussbaum
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/14/3133
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spelling doaj-df6b440c7dba4ee0b84335468faa01382020-11-25T01:55:22ZengMDPI AGSensors1424-82202019-07-011914313310.3390/s19143133s19143133Classifying Diverse Physical Activities Using “Smart Garments”Mohammad Iman Mokhlespour Esfahani0Maury A. Nussbaum1Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48105, USADepartment of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24060, USAPhysical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.https://www.mdpi.com/1424-8220/19/14/3133smart garmentsmart textile systemwearable sensorsmart shirtsmart socksphysical activitiesclassificationhuman health
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Iman Mokhlespour Esfahani
Maury A. Nussbaum
spellingShingle Mohammad Iman Mokhlespour Esfahani
Maury A. Nussbaum
Classifying Diverse Physical Activities Using “Smart Garments”
Sensors
smart garment
smart textile system
wearable sensor
smart shirt
smart socks
physical activities
classification
human health
author_facet Mohammad Iman Mokhlespour Esfahani
Maury A. Nussbaum
author_sort Mohammad Iman Mokhlespour Esfahani
title Classifying Diverse Physical Activities Using “Smart Garments”
title_short Classifying Diverse Physical Activities Using “Smart Garments”
title_full Classifying Diverse Physical Activities Using “Smart Garments”
title_fullStr Classifying Diverse Physical Activities Using “Smart Garments”
title_full_unstemmed Classifying Diverse Physical Activities Using “Smart Garments”
title_sort classifying diverse physical activities using “smart garments”
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2019-07-01
description Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.
topic smart garment
smart textile system
wearable sensor
smart shirt
smart socks
physical activities
classification
human health
url https://www.mdpi.com/1424-8220/19/14/3133
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