{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:15:06Z","timestamp":1774642506463,"version":"3.50.1"},"reference-count":10,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"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>Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period. Identification of non-wear time is usually performed as a pre-processing step using data recorded by the accelerometer, which is the most common sensor used for PA analysis algorithms. The main issue is the correct differentiation between non-wear time, sleep time, and sedentary wake time, especially in frail older adults or patient groups. Based on the current state of the art, the objectives of this study were to (1) develop robust non-wearing detection algorithms based on data recorded with a wearable device that integrates acceleration and temperature sensors; (2) validate the algorithms using real-world data recorded according to an appropriate measurement protocol. A comparative evaluation of the implemented algorithms indicated better performances (99%, 97%, 99%, and 98% for sensitivity, specificity, accuracy, and negative predictive value, respectively) for an event-based detection algorithm, where the temperature sensor signal was appropriately processed to identify the timing of device removal\/non-wear.<\/jats:p>","DOI":"10.3390\/s22031117","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T22:16:18Z","timestamp":1643753778000},"page":"1117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9703-380X","authenticated-orcid":false,"given":"Sara","family":"Pagnamenta","sequence":"first","affiliation":[{"name":"Ecole Polytechnique Federale de Lausanne (EPFL), Laboratory of Movement Analysis and Measurement (LMAM), CH-1015 Lausanne, Switzerland"}]},{"given":"Karoline Blix","family":"Gr\u00f8nvik","sequence":"additional","affiliation":[{"name":"Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6582-5375","authenticated-orcid":false,"given":"Kamiar","family":"Aminian","sequence":"additional","affiliation":[{"name":"Ecole Polytechnique Federale de Lausanne (EPFL), Laboratory of Movement Analysis and Measurement (LMAM), CH-1015 Lausanne, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2231-8138","authenticated-orcid":false,"given":"Beatrix","family":"Vereijken","sequence":"additional","affiliation":[{"name":"Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway"}]},{"given":"Anisoara","family":"Paraschiv-Ionescu","sequence":"additional","affiliation":[{"name":"Ecole Polytechnique Federale de Lausanne (EPFL), Laboratory of Movement Analysis and Measurement (LMAM), CH-1015 Lausanne, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Doherty, A., Jackson, D., Hammerla, N., Pl\u00f6tz, T., Olivier, P., Granat, M.H., and Wareham, N.J. (2017). Large scale population assessment of physical activity using wrist worn accelerometers: The UK Biobank Study. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169649"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1249\/MSS.0b013e318258cb36","article-title":"Assessment of wear\/nonwear time classification algorithms for triaxial accelerometer","volume":"44","author":"Choi","year":"2012","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e007447","DOI":"10.1136\/bmjopen-2014-007447","article-title":"Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity","volume":"5","author":"Zhou","year":"2015","journal-title":"BMJ Open"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Knaier, R., H\u00f6chsmann, C., Infanger, D., Hinrichs, T., and Schmidt-Trucks\u00e4ss, A. (2019). Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X+. BMC Public Health, 19.","DOI":"10.1186\/s12889-019-6568-9"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/02640414.2019.1703301","article-title":"Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data","volume":"38","author":"Ahmadi","year":"2020","journal-title":"J. Sports Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1097\/mss.0b013e318150d42e","article-title":"Large-scale applications of accelerometers: New frontiers and new questions","volume":"39","author":"Troiano","year":"2007","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e8284","DOI":"10.7717\/peerj.8284","article-title":"Ambulatory sleep scoring using accelerometers\u2014Distinguishing between nonwear and sleep\/wake states","volume":"8","author":"Barouni","year":"2020","journal-title":"PeerJ"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Duncan, S., Stewart, T., Mackay, L., Neville, J., Narayanan, A., Walker, C., and Morton, S. (2018). Wear-time compliance with a dual-accelerometer system for capturing 24-h behavioural profiles in children and adults. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15071296"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.gaitpost.2003.07.005","article-title":"Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation","volume":"20","author":"Buchser","year":"2004","journal-title":"Gait Posture"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8832","DOI":"10.1038\/s41598-021-87757-z","article-title":"A novel algorithm to detect non-wear time from raw accelerometer data using convolutional neural networks","volume":"11","author":"Syed","year":"2021","journal-title":"Sci. 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