Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition

Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have s...

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Autores principales:	Aiguo Wang, Shenghui Zhao, Huan-Chao Keh, Guilin Chen, Diptendu Sinha Roy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wearable computing activity recognition clustering guided Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/94005bbe08c8412da2d245ad5794c8ec
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spelling	oai:doaj.org-article:94005bbe08c8412da2d245ad5794c8ec2021-11-11T19:00:42ZTowards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition10.3390/s212169621424-8220https://doaj.org/article/94005bbe08c8412da2d245ad5794c8ec2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6962https://doaj.org/toc/1424-8220Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability.Aiguo WangShenghui ZhaoHuan-Chao KehGuilin ChenDiptendu Sinha RoyMDPI AGarticlewearable computingactivity recognitionclustering guidedChemical technologyTP1-1185ENSensors, Vol 21, Iss 6962, p 6962 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wearable computing activity recognition clustering guided Chemical technology TP1-1185
spellingShingle	wearable computing activity recognition clustering guided Chemical technology TP1-1185 Aiguo Wang Shenghui Zhao Huan-Chao Keh Guilin Chen Diptendu Sinha Roy Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
description	Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability.
format	article
author	Aiguo Wang Shenghui Zhao Huan-Chao Keh Guilin Chen Diptendu Sinha Roy
author_facet	Aiguo Wang Shenghui Zhao Huan-Chao Keh Guilin Chen Diptendu Sinha Roy
author_sort	Aiguo Wang
title	Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
title_short	Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
title_full	Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
title_fullStr	Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
title_full_unstemmed	Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
title_sort	towards a clustering guided hierarchical framework for sensor-based activity recognition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/94005bbe08c8412da2d245ad5794c8ec
work_keys_str_mv	AT aiguowang towardsaclusteringguidedhierarchicalframeworkforsensorbasedactivityrecognition AT shenghuizhao towardsaclusteringguidedhierarchicalframeworkforsensorbasedactivityrecognition AT huanchaokeh towardsaclusteringguidedhierarchicalframeworkforsensorbasedactivityrecognition AT guilinchen towardsaclusteringguidedhierarchicalframeworkforsensorbasedactivityrecognition AT diptendusinharoy towardsaclusteringguidedhierarchicalframeworkforsensorbasedactivityrecognition
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Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition

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