{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T11:58:15Z","timestamp":1792324695285,"version":"4.4.0"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union\u2019s Horizon Europe research and innovation programme","doi-asserted-by":"publisher","award":["101063008"],"award-info":[{"award-number":["101063008"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality. However, wearable solutions can be intrusive and affect sleep, while non-wearable systems, such as camera-based approaches and pressure sensor arrays, often face challenges related to privacy, cost, and computational complexity. The system in this paper proposes a microcontroller-based approach exploiting the execution of an embedded machine learning (ML) model for posture classification. By locally processing data from a minimal set of pressure sensors, the system avoids the need to transmit raw data to remote units, making it lightweight and suitable for real-time applications. Our results demonstrate that this approach maintains high classification accuracy (i.e., 0.90 and 0.96 for the configurations with 6 and 15 sensors, respectively) while reducing both hardware and computational requirements.<\/jats:p>","DOI":"10.3390\/s25020458","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:28:31Z","timestamp":1736854111000},"page":"458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6311-7332","authenticated-orcid":false,"given":"Giacomo","family":"Peruzzi","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35122 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2416-0220","authenticated-orcid":false,"given":"Alessandra","family":"Galli","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9498-4722","authenticated-orcid":false,"given":"Giada","family":"Giorgi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35122 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3991-8858","authenticated-orcid":false,"given":"Alessandro","family":"Pozzebon","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35122 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1038\/nature04286","article-title":"Sleep-dependent memory consolidation","volume":"437","author":"Stickgold","year":"2005","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1001\/archinte.165.1.25","article-title":"Overweight and obese patients in a primary care population report less sleep than patients with a normal body mass index","volume":"165","author":"Vorona","year":"2005","journal-title":"Arch. Intern. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/01616412.2017.1315864","article-title":"A review of sleep disorders and melatonin","volume":"39","author":"Xie","year":"2017","journal-title":"Neurol. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.sleep.2018.04.017","article-title":"Sleep versus non- sleep-related fatal road accidents","volume":"51","author":"Kalsi","year":"2018","journal-title":"Sleep Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.sleh.2020.02.016","article-title":"Association between work productivity and sleep health: A cross-sectional study in Japan","volume":"6","author":"Ishibashi","year":"2020","journal-title":"Sleep Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/j.1365-2869.2008.00703.x","article-title":"Determining optimal sleep position in patients with positional sleep-disordered breathing using response surface analysis","volume":"18","author":"Lee","year":"2009","journal-title":"J. Sleep Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s11325-019-01897-1","article-title":"Prevalence and characteristics of positional obstructive sleep apnea (POSA) in patients with severe OSA","volume":"24","author":"Oksenberg","year":"2020","journal-title":"Sleep Breath."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1145\/3130969","article-title":"Sleepmonitor: Monitoring respiratory rate and body position during sleep using smartwatch","volume":"1","author":"Sun","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fallmann, S., Van Veen, R., Chen, L., Walker, D., Chen, F., and Pan, C. (2017, January 12\u201315). Wearable accelerometer based extended sleep position recognition. Proceedings of the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China.","DOI":"10.1109\/HealthCom.2017.8210806"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1109\/TNSRE.2020.3048121","article-title":"Transfer learning for clinical sleep pose detection using a single 2D IR camera","volume":"29","author":"Mohammadi","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tam, A.Y.C., So, B.P.H., Chan, T.T.C., Cheung, A.K.Y., Wong, D.W.C., and Cheung, J.C.W. (2021). A blanket accommodative sleep posture classification system using an infrared depth camera: A deep learning approach with synthetic augmentation of blanket conditions. Sensors, 21.","DOI":"10.3390\/s21165553"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/iot2010007","article-title":"CNN-based smart sleep posture recognition system","volume":"2","author":"Tang","year":"2021","journal-title":"IoT"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14466","DOI":"10.1109\/JSEN.2020.3013435","article-title":"Smart sleep monitoring system via passively sensing human vibration signals","volume":"21","author":"Li","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.pmcj.2013.10.008","article-title":"Sleep posture analysis using a dense pressure sensitive bedsheet","volume":"10","author":"Liu","year":"2014","journal-title":"Pervasive Mob. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"33217","DOI":"10.1109\/JSEN.2024.3452940","article-title":"Sleep State Monitoring System Based on Flexible Pressure sensor Arrays for Evaluating Sleep Quality","volume":"24","author":"Li","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/JBHI.2019.2899070","article-title":"Artificial neural network for in-bed posture classification using bed-sheet pressure sensors","volume":"24","author":"Matar","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pouyan, M.B., Birjandtalab, J., Heydarzadeh, M., Nourani, M., and Ostadabbas, S. (2017, January 16\u201319). A pressure map dataset for posture and subject analytics. Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA.","DOI":"10.1109\/BHI.2017.7897206"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ne\u0161kovi\u0107, \u00d0.D., and Miljkovi\u0107, N. (2023, January 21\u201322). Classification of Lying Postures. Proceedings of the 2023 31st Telecommunications Forum (TELFOR), Belgrade, Serbia.","DOI":"10.1109\/TELFOR59449.2023.10372623"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Davoodnia, V., and Etemad, A. (2019, January 6\u20139). Identity and posture recognition in smart beds with deep multitask learning. Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.","DOI":"10.1109\/SMC.2019.8914459"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Stern, L., and Roshan Fekr, A. (2023). In-bed posture classification using deep neural network. Sensors, 23.","DOI":"10.3390\/s23052430"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"93907","DOI":"10.1109\/ACCESS.2020.2994593","article-title":"Self-supervised learning from multi-sensor data for sleep recognition","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Elsharif, E., Drawil, N., and Kanoun, S. (2021, January 25\u201327). Automatic posture and limb detection for pressure ulcer risk assessment. Proceedings of the 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, Tripoli, Libya.","DOI":"10.1109\/MI-STA52233.2021.9464360"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Dang, B.L., Dam, H.P., Dang, Q.H., Nguyen, D.M., and Vo, V.A. (2022, January 20\u201322). A novel implementation of sleeping posture classification using RANC ecosystem. Proceedings of the 2022 International Conference on Advanced Technologies for Communications (ATC), Ha Noi, Vietnam.","DOI":"10.1109\/ATC55345.2022.9942964"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Doan, N.P., Pham, N.D.A., Pham, H.M., Nguyen, H.T., Nguyen, T.A., and Nguyen, H.H. (2021, January 15\u201316). Real-time sleeping posture recognition for smart hospital beds. Proceedings of the 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), Hanoi, Vietnam.","DOI":"10.1109\/MAPR53640.2021.9585289"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dam, H.P., Pham, N.D.A., Pham, H.M., Doan, N.P., Nguyen, D.M., and Nguyen, H.H. (2021, January 21\u201322). In-bed posture classification using pressure sensor data and spiking neural network. Proceedings of the 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam.","DOI":"10.1109\/NICS54270.2021.9701531"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Licciardo, G.D., Russo, A., Naddeo, A., Cappetti, N., Di Benedetto, L., Rubino, A., and Liguori, R. (2021). A resource constrained neural network for the design of embedded human posture recognition systems. Appl. Sci., 11.","DOI":"10.3390\/app11114752"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Peruzzi, G., Galli, A., and Pozzebon, A. (2022, January 18\u201320). A Novel Methodology to Remotely and Early Diagnose Sleep Bruxism by Leveraging on Audio Signals and Embedded Machine Learning. Proceedings of the 2022 IEEE International Symposium on Measurements & Networking (M&N), Padua, Italy.","DOI":"10.1109\/MN55117.2022.9887782"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/458\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:28:48Z","timestamp":1759919328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,14]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["s25020458"],"URL":"https:\/\/doi.org\/10.3390\/s25020458","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,14]]}}}