{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:27:21Z","timestamp":1761294441202,"version":"3.37.3"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["MINECO-TIN2017-84804-R"],"award-info":[{"award-number":["MINECO-TIN2017-84804-R"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Asturias Regional Government","award":["FC-GRUPIN-IDI\/2018\/000226"],"award-info":[{"award-number":["FC-GRUPIN-IDI\/2018\/000226"]}]},{"name":"Spanish Ministry of Economy, Industry and Competitiveness","award":["TIN2017-84804-R\/PID2020-112726RB-I00"],"award-info":[{"award-number":["TIN2017-84804-R\/PID2020-112726RB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>There are many real-world applications like healthcare systems, job monitoring, well-being and personal fitness tracking, monitoring of elderly and frail people, assessment of rehabilitation and follow-up treatments, affording Fall Detection (FD) and ADL (Activity of Daily Living) identification, separately or even at a time. However, the two main drawbacks of these solutions are that most of the times, the devices deployed are obtrusive (devices worn on not quite common parts of the body like neck, waist and ankle) and the poor battery life. Thus, this work proposes a low-power classification algorithm based on an Ensemble of KNN and K-Means algorithms (EKMeans) to identify Falls and High-Intensity ADL events such as running, jogging and climbing up stairs. The input of EKMeans are triaxial accelerometer data gathered from wrist-wearable devices. The proposal will be validated on the Fall&amp;ADL publicly available datasets UMAFall, UCIFall and FallAllD, considering two kinds of activity labelling: Two-Class and Multi-Class. An exhaustive comparative study between our proposal, and the baseline algorithms KNN and a feed-forward Neural Network (NN) is deployed, where EKMeans outperformed clearly the Specificity (ADL classification) of the KNN and NN for the three datasets. Finally, a comparative battery consumption study has been included deploying the analyzed algorithms in a WearOS smartwatch, where EKMeans drains the battery from 100% to 0% in 27.45 hours, saving 5% and 21% concerning KNN and NN, respectively. Keywords: Human Activity Recognition, ADL Identification, Fall Detection TS Clustering, TS Classification, Wearable Devices, Low-Power HAR.<\/jats:p>","DOI":"10.1093\/jigpal\/jzac025","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T13:34:40Z","timestamp":1646400880000},"page":"375-389","source":"Crossref","is-referenced-by-count":4,"title":["A low-power HAR method for fall and high-intensity ADLs identification using wrist-worn accelerometer devices"],"prefix":"10.1093","volume":"31","author":[{"given":"Enrique A","family":"de la Cal","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Geology, University of Oviedo , 33005 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirko","family":"F\u00e1\u00f1ez","sequence":"additional","affiliation":[{"name":"University of Granada , 18014 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Villar","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Oviedo , EPI, 33203 Gij\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose R","family":"Villar","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Geology, University of Oviedo , 33005 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V\u00edctor M","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"University of Granada , 18014 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"2023033115513916400_","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.procs.2017.06.110","article-title":"Umafall: a multisensor dataset for the research on automatic fall detection","volume":"110","author":"Casilari","year":"2017","journal-title":"Procedia Computer Science"},{"key":"2023033115513916400_","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"2023033115513916400_","article-title":"Deep learning for sensor-based human activity recognition: overview, challenges and opportunities","author":"Chen","year":"2020","journal-title":"CoRR"},{"key":"2023033115513916400_","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/978-3-030-61705-9_9","article-title":"A simple classification ensemble for adl and falls","volume-title":"Hybrid Artificial Intelligent Systems","author":"de la Cal","year":"2020"},{"key":"2023033115513916400_","doi-asserted-by":"crossref","first-page":"210816","DOI":"10.1109\/ACCESS.2020.3037715","article-title":"Human activity recognition using inertial, physiological and environmental sensors: a comprehensive survey","volume":"8","author":"Demrozi","year":"2020","journal-title":"IEEE Access"},{"key":"2023033115513916400_","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.engappai.2018.08.014","article-title":"A review of state-of-the-art techniques for abnormal human activity recognition","volume":"77","author":"Dhiman","year":"2019","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"2023033115513916400_","first-page":"1","article-title":"Personalized human activity recognition based on integrated wearable sensor and transfer learning","volume":"21","author":"Zhongzheng","year":"2021","journal-title":"Sensors (Switzerland)"},{"key":"2023033115513916400_","doi-asserted-by":"crossref","DOI":"10.1007\/s11042-020-10435-1","article-title":"A time-efficient convolutional neural network model in human activity recognition","volume":"80","author":"Gholamrezaii","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"2023033115513916400_","doi-asserted-by":"crossref","DOI":"10.3390\/s21062254","article-title":"A feasibility study of the use of smartwatches in wearable fall detection systems","volume":"21","author":"Gonz\u00e1lez-Ca\u00f1ete","year":"2021","journal-title":"Sensors (Basel, Switzerland)"},{"key":"2023033115513916400_","first-page":"100","article-title":"Algorithm as 136: a k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"Journal of the Royal Statistical Society. 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