{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:16:37Z","timestamp":1760242597601,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,16]],"date-time":"2017-12-16T00:00:00Z","timestamp":1513382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kadir Has University","award":["2017-BAP-09"],"award-info":[{"award-number":["2017-BAP-09"]}]},{"name":"Steelcase Education","award":["2015-DK-05"],"award-info":[{"award-number":["2015-DK-05"]}]},{"name":"PolyVision","award":["2015-AP-04"],"award-info":[{"award-number":["2015-AP-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies, we chose Wi-Fi, BLE beacon and ultra-wide band (UWB) sensor technologies for their popularity and investigated their applicability in BM. We tested three technologies for error ranges and found an average error of 1.39 m for Wi-Fi in a 36 m2 test area (6 m \u00d7 6 m), 0.86 m for the BLE beacon in a 37.44 m2 test area (9.6 m \u00d7 3.9 m) and 0.24 m for ultra-wide band sensors in a 36 m2 test area (6 m \u00d7 6 m). We simulated the applicability of these error ranges for real-time locations by using a behavioral dataset collected from an active learning classroom. We used two UWB tags simultaneously by incorporating a custom-designed ceiling system in a new 39.76 m2 test area (7.35 m \u00d7 5.41 m). We considered 26 observation points and collected data for 180 s for each point (total 4680) with an average error of 0.2072 m for 23 points inside the test area. Finally, we demonstrated the use of ultra-wide band sensor technology for BM.<\/jats:p>","DOI":"10.3390\/s17122925","type":"journal-article","created":{"date-parts":[[2017,12,19]],"date-time":"2017-12-19T03:54:32Z","timestamp":1513655672000},"page":"2925","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Early Steps in Automated Behavior Mapping via Indoor Sensors"],"prefix":"10.3390","volume":"17","author":[{"given":"Taner","family":"Arsan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kadir Has University, 34083 Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4430-6178","authenticated-orcid":false,"given":"Orcun","family":"Kepez","sequence":"additional","affiliation":[{"name":"Department of Interior Architecture and Environmental Design, Kadir Has University, 34083 Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,16]]},"reference":[{"key":"ref_1","unstructured":"Barker, R. 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