{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:09:45Z","timestamp":1760148585038,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T00:00:00Z","timestamp":1684540800000},"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>Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals. Therefore, considering the significance of continuous data gathering and transmission sequence for WS, this article introduces a Concerted Sensor Data Transmission Scheme (CSDTS). This scheme endorses aggregation and transmission that aims at generating continuous data sequences. The aggregation is performed considering the overlapping and non-overlapping intervals from the WS sensing process. Such concerted data aggregation generates fewer chances of missing data. In the transmission process, allocated first-come-first-serve-based sequential communication is pursued. In the transmission scheme, a pre-verification of continuous or discrete (missing) transmission sequences is performed using classification tree learning. In the learning process, the accumulation and transmission interval synchronization and sensor data density are matched for preventing pre-transmission losses. The discrete classified sequences are thwarted from the communication sequence and are transmitted post the alternate WS data accumulation. This transmission type prevents sensor data loss and reduces prolonged wait times.<\/jats:p>","DOI":"10.3390\/s23104924","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T02:00:27Z","timestamp":1684720827000},"page":"4924","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Kambatty Bojan","family":"Gurumoorthy","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3531-1904","authenticated-orcid":false,"given":"Arun Sekar","family":"Rajasekaran","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaliraj","family":"Kalirajan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2115-6181","authenticated-orcid":false,"given":"Samydurai","family":"Gopinath","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karpagam Institute of Technology, Coimbatore 641105, Tamilndu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6375-4123","authenticated-orcid":false,"given":"Fadi","family":"Al-Turjman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey"},{"name":"Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manjur","family":"Kolhar","sequence":"additional","affiliation":[{"name":"Department Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al Kharj 11990, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chadi","family":"Altrjman","sequence":"additional","affiliation":[{"name":"Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17610","DOI":"10.1109\/ACCESS.2021.3051899","article-title":"Heterogeneous Sensor Data Fusion for Human Falling Detection","volume":"9","author":"Pan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1109\/TBDATA.2018.2872585","article-title":"Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data","volume":"7","author":"Vhaduri","year":"2018","journal-title":"IEEE Trans. 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