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Sport-Related Human Activity Detection and Recognition Using a Smartwatch

As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion...

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Detalles Bibliográficos
Autores principales: Zhuang, Zhendong, Xue, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891622/
https://www.ncbi.nlm.nih.gov/pubmed/31744127
http://dx.doi.org/10.3390/s19225001
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author Zhuang, Zhendong
Xue, Yang
author_facet Zhuang, Zhendong
Xue, Yang
author_sort Zhuang, Zhendong
collection PubMed
description As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.
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spelling pubmed-68916222019-12-12 Sport-Related Human Activity Detection and Recognition Using a Smartwatch Zhuang, Zhendong Xue, Yang Sensors (Basel) Article As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method. MDPI 2019-11-16 /pmc/articles/PMC6891622/ /pubmed/31744127 http://dx.doi.org/10.3390/s19225001 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhuang, Zhendong
Xue, Yang
Sport-Related Human Activity Detection and Recognition Using a Smartwatch
title Sport-Related Human Activity Detection and Recognition Using a Smartwatch
title_full Sport-Related Human Activity Detection and Recognition Using a Smartwatch
title_fullStr Sport-Related Human Activity Detection and Recognition Using a Smartwatch
title_full_unstemmed Sport-Related Human Activity Detection and Recognition Using a Smartwatch
title_short Sport-Related Human Activity Detection and Recognition Using a Smartwatch
title_sort sport-related human activity detection and recognition using a smartwatch
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891622/
https://www.ncbi.nlm.nih.gov/pubmed/31744127
http://dx.doi.org/10.3390/s19225001
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