{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T16:16:18Z","timestamp":1777133778498,"version":"3.51.4"},"reference-count":40,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p>The advent of newer and better technologies has made Human Activity Recognition (HAR) highly essential in our daily lives. HAR is a classification problem where the activity of humans is classified by analyzing the data collected from various sources like sensors, cameras etc. for a period of time. In this work, we have proposed a model for activity recognition which will provide a substructure for the assisted living environment. We used a genetic search based feature selection for the management of the voluminous data generated from various embedded sensors such as accelerometer, gyroscope, etc. We evaluated the proposed model on a sensor-based dataset - Human Activities and Postural Transitions Recognition (HAPT) which is publically available. The proposed model yields an accuracy of 97.04% and is better as compared to the other existing classification algorithms on the basis of several considered evaluation metrics. In this paper, we have also presented a cloud based edge computing architecture for the deployment of the proposed model which will ensure faster and uninterrupted assisted living environment.<\/jats:p>","DOI":"10.2298\/csis230622003b","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T08:45:19Z","timestamp":1705913119000},"page":"95-116","source":"Crossref","is-referenced-by-count":3,"title":["Activity recognition for elderly care using genetic search"],"prefix":"10.2298","volume":"21","author":[{"given":"Ankita","family":"Biswal","sequence":"first","affiliation":[{"name":"Dept. of Computer Science & Engineering, CUTM, Bhubaneswar, India"}]},{"suffix":"Rani","given":"Chhabi","family":"Panigrahi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rama Devi Women\u2019s University, Bhubaneswar, India"}]},{"given":"Anukampa","family":"Behera","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, S\u2019O\u2019A Deemed to be University, Bhubaneswar, India"}]},{"given":"Sarmistha","family":"Nanda","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Gandhi Engineering College, Bhubaneswar, India"}]},{"given":"Tien-Hsiung","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan"}]},{"given":"Bibudhendu","family":"Pati","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rama Devi Women\u2019s University, Bhubaneswar, India"}]},{"given":"Chandan","family":"Malu","sequence":"additional","affiliation":[{"name":"iCETS, Infosys, Bhubaneswar, India"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Chernbumroong, S., Cang, S., Atkins, A., & Yu, H. 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