{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:50:03Z","timestamp":1777683003721,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JHS"],"published-print":{"date-parts":[[2024,10,15]]},"abstract":"<jats:p>This paper proposes a low power consuming system for monitoring elderly people\u2019s activities and their health conditions. The proposed system has two activity recognition modules: smartphone sensor-based wearable module; infrared grid sensor-based remote module. The two activity recognition modules work in a coordinated way. The fraction of the time the person is detected by the infrared sensor, the smartphone remains idle. As a result, energy consumption in the smartphone is reduced significantly, and hence the battery lifetime is increased. In the smartphone, a Feed-forward Neural Network (FNN) based activity recognition algorithm is implemented using fixed-point computation to further reduce energy consumption. A Convolutional Neural Network is used in the infrared sensor-based activity recognition module. The proposed system also has real-time health monitoring capability, which is based on ECG signal classification. A FNN leveraging fixed-point operation is used for ECG signal classification on an embedded ARM processor. Proposed fixed-point implementations of the FNNs are faster than floating-point implementation and require 50% less memory to store the neural network model parameters without loss of classification accuracy.<\/jats:p>","DOI":"10.3233\/jhs-240001","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T11:18:55Z","timestamp":1718363935000},"page":"607-618","source":"Crossref","is-referenced-by-count":2,"title":["Optimized neural network models for low power elderly monitoring system in Internet of things"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4139-9056","authenticated-orcid":false,"given":"Raqibul","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Faculty of Engineering, Halic University, Istanbul, 34060, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8314-9051","authenticated-orcid":false,"given":"Alireza","family":"Souri","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Faculty of Engineering, Halic University, Istanbul, 34060, Turkey"},{"name":"Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600 077, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"9","key":"10.3233\/JHS-240001_ref3","first-page":"1295","article-title":"Energy efficient smartphone-based activity recognition using fixed-point arithmetic","volume":"19","author":"Anguita","year":"2013","journal-title":"Journal of universal computer science"},{"key":"10.3233\/JHS-240001_ref4","doi-asserted-by":"crossref","unstructured":"B.\u00a0Barrois and O.\u00a0Sentieys, Customizing fixed-point and floating-point 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