{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T09:12:20Z","timestamp":1773393140518,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2014,4,29]],"date-time":"2014-04-29T00:00:00Z","timestamp":1398729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.<\/jats:p>","DOI":"10.3390\/s140507831","type":"journal-article","created":{"date-parts":[[2014,4,30]],"date-time":"2014-04-30T04:40:45Z","timestamp":1398832845000},"page":"7831-7856","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5986-3247","authenticated-orcid":false,"given":"Piotr","family":"Augustyniak","sequence":"first","affiliation":[{"name":"AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Krak\u00f3w, Poland"}]},{"given":"Magdalena","family":"Smole\u0144","sequence":"additional","affiliation":[{"name":"AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Krak\u00f3w, Poland"}]},{"given":"Zbigniew","family":"Mikrut","sequence":"additional","affiliation":[{"name":"AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Krak\u00f3w, Poland"}]},{"given":"Eliasz","family":"Ka\u0144toch","sequence":"additional","affiliation":[{"name":"AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Krak\u00f3w, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2014,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.2337\/diacare.26.5.1626","article-title":"Diabetes and telemedicine\u2013is the technology sound, effective, cost-effective, and practical?","volume":"26","author":"Klonoff","year":"2003","journal-title":"Diabet. 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