{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:51:37Z","timestamp":1767117097056,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T00:00:00Z","timestamp":1571616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Saitama Medical University, faculty of Health and Medical Care","award":["28-024 and 30-004"],"award-info":[{"award-number":["28-024 and 30-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the aging of society, the number of fall accidents has increased in hospitals and care facilities, and some accidents have happened around beds. To help prevent accidents, mats and clip sensors have been used in these facilities but they can be invasive, and their purpose may be misinterpreted. In recent years, research has been conducted using an infrared-image depth sensor as a bed-monitoring system for detecting a patient getting up, exiting the bed, and\/or falling; however, some manual calibration was required initially to set up the sensor in each instance. We propose a bed-monitoring system that retains the infrared-image depth sensors but uses semi-automatic rather than manual calibration in each situation where it is applied. Our automated methods robustly calculate the bed region, surrounding floor, sensor location, and attitude, and can recognize the spatial position of the patient even when the sensor is attached but unconstrained. Also, we propose a means to reconfigure the spatial position considering occlusion by parts of the bed and also accounting for the gravity center of the patient\u2019s body. Experimental results of multi-view calibration and motion simulation showed that our methods were effective for recognition of the spatial position of the patient.<\/jats:p>","DOI":"10.3390\/s19204581","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T11:37:55Z","timestamp":1571657875000},"page":"4581","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semi-Automatic Calibration Method for a Bed-Monitoring System Using Infrared Image Depth Sensors"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2622-6887","authenticated-orcid":false,"given":"Hideki","family":"Komagata","sequence":"first","affiliation":[{"name":"Faculty of Health and Medical Care, Saitama Medical University, 1397-1 Yamane, Hidaka, Saitama 350-1241, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erika","family":"Kakinuma","sequence":"additional","affiliation":[{"name":"Shin-Oyama City Hospital, 2251-1 Hitotonoya, Oyama, Tochigi 323-0827, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Ishikawa","sequence":"additional","affiliation":[{"name":"Faculty of Health and Medical Care, Saitama Medical University, 1397-1 Yamane, Hidaka, Saitama 350-1241, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2675-3011","authenticated-orcid":false,"given":"Kazuma","family":"Shinoda","sequence":"additional","affiliation":[{"name":"School of Engineering, Utsunomiya University, 7-1-2 Yoto, Utsunomiya, Tochigi 321-8585, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"Faculty of Health and Medical Care, Saitama Medical University, 1397-1 Yamane, Hidaka, Saitama 350-1241, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,21]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2007). 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