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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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 |
work_keys_str_mv |
AT mohammadimanmokhlespouresfahani classifyingdiversephysicalactivitiesusingsmartgarments AT mauryanussbaum classifyingdiversephysicalactivitiesusingsmartgarments |
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