{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:26:49Z","timestamp":1760236009608,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21216962","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"6962","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6150-8068","authenticated-orcid":false,"given":"Aiguo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Foshan University, Foshan 528225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan-Chao","family":"Keh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diptendu Sinha","family":"Roy","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Shillong 793003, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439870","article-title":"Indirectly supervised anomaly detection of clinically meaningful health events from smart home data","volume":"12","author":"Dahmen","year":"2021","journal-title":"ACM Trans. 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