{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T11:58:14Z","timestamp":1792324694312,"version":"4.4.0"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012505","name":"Royal Perth Hospital Research Foundation","doi-asserted-by":"publisher","award":["SB-Plus 200\/2022"],"award-info":[{"award-number":["SB-Plus 200\/2022"]}],"id":[{"id":"10.13039\/100012505","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when further assessment is necessary. While inertial measurement units (IMUs) have been used in health research, their use inside hospitals has been limited. This study explores the use of IMUs with machine learning to continuously capture, classify and visualise patient positions in hospital beds. The participants attended a data collection session in a simulated hospital bedspace and were asked to adopt nine positions. Movement data were captured using five IMU Xsens DOTs attached to the forehead, wrists and ankles. Support Vector Machine (SVM) and K-Nearest Neighbours classifiers were trained using five different combinations of sensors (e.g., right wrist only, right and left wrist) to determine body positions. Data from 30 participants were analysed. The highest accuracy (87.7%) was achieved by SVM using forehead and wrist sensors. Adding data from ankle sensors reduced the accuracy. To preserve patient privacy in a hospital setting, a 3D visualisation was developed in Unity, offering a non-identifiable representation of patient positions. This system could help clinicians monitor changes in position which may signal clinical deterioration.<\/jats:p>","DOI":"10.3390\/s25020499","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T06:46:10Z","timestamp":1737009970000},"page":"499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Using Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8817-3533","authenticated-orcid":false,"given":"Eliza","family":"Becker","sequence":"first","affiliation":[{"name":"Curtin School of Allied Health, Curtin University, Perth 6102, Australia"},{"name":"Virtual Care and Community Care, East Metropolitan Health Service, Perth 6000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1944-1418","authenticated-orcid":false,"given":"Siavash","family":"Khaksar","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harry","family":"Booker","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6082-6352","authenticated-orcid":false,"given":"Kylie","family":"Hill","sequence":"additional","affiliation":[{"name":"Curtin School of Allied Health, Curtin University, Perth 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3195-3480","authenticated-orcid":false,"given":"Tele","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carol","family":"Watson","sequence":"additional","affiliation":[{"name":"Physiotherapy Department, Royal Perth Hospital, Perth 6000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2286-2963","authenticated-orcid":false,"given":"Ethan","family":"Wordsworth","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meg","family":"Harrold","sequence":"additional","affiliation":[{"name":"Curtin School of Allied Health, Curtin University, Perth 6102, Australia"},{"name":"Physiotherapy Department, Royal Perth Hospital, Perth 6000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s00134-018-5384-5","article-title":"The rise of ward monitoring: Opportunities and challenges for critical care specialists","volume":"45","author":"Michard","year":"2019","journal-title":"Intensive Care Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/nhs.12778","article-title":"Clinical cues used by nurses to recognize changes in patients\u2019 clinical states: A systematic review","volume":"23","author":"Burdeu","year":"2021","journal-title":"Nurs. Health Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1056\/NEJMsa2001090","article-title":"Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration","volume":"383","author":"Escobar","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1111\/jan.14435","article-title":"Clinical deterioration and hospital-acquired complications in adult patients with isolation precautions for infection control: A systematic review","volume":"76","author":"Berry","year":"2020","journal-title":"J. Adv. Nurs."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3990","DOI":"10.1111\/jocn.13839","article-title":"Recognising and responding to in-hospital clinical deterioration: An integrative review of interprofessional practice issues","volume":"26","author":"Allen","year":"2017","journal-title":"J. Clin. Nurs."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1186\/s13054-021-03766-4","article-title":"The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: A systematic review and meta-analysis","volume":"25","author":"Areia","year":"2021","journal-title":"Crit. Care"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.resuscitation.2021.07.022","article-title":"Treatments costs associated with inpatient clinical deterioration","volume":"166","author":"Curtis","year":"2021","journal-title":"Resuscitation"},{"key":"ref_8","unstructured":"Australian Commission on Safety and Quality in Health Care (2022, September 19). The State of Patient Safety and Quality in Australian Hospitals 2019, Available online: https:\/\/www.safetyandquality.gov.au\/sites\/default\/files\/2019-07\/the-state-of-patient-safety-and-quality-in-australian-hospitals-2019.pdf."},{"key":"ref_9","unstructured":"Thewlis, D., Bishop, C., Daniell, N., and Paul, G. (2011, January 3\u20137). A comparison of two commercially available motion capture systems for gait analysis: High-end vs. low-cost. Proceedings of the Congress of the International Society of Biomechanics, Brussels, Belgium."},{"key":"ref_10","unstructured":"(2024, December 27). Fiber Optics: Understanding the Basics. Available online: https:\/\/www.photonics.com\/Articles\/Fiber_Optics_Understanding_the_Basics\/a25151."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pupic, N., Gabison, S., Evans, G., Fernie, G., Dolatabadi, E., and Dutta, T. (2024). Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management. Sensors, 24.","DOI":"10.3390\/s24196483"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e29769","DOI":"10.2196\/29769","article-title":"Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial","volume":"8","author":"Khaksar","year":"2021","journal-title":"JMIR Rehabil. Assist. Technol."},{"key":"ref_13","unstructured":"Rincy, T., and Gupta, R. (2020, January 22\u201323). A survey on machine learning approaches and its techniques. Proceedings of the IEEE International Students\u2019 Conference on Electrical, Electronics and Computer Science, Bhopal, India."},{"key":"ref_14","unstructured":"Rincy, T., and Gupta, R. (2020, January 22\u201323). Feature selection techniques and its importance in machine learning: A survey. Proceedings of the IEEE International Students\u2019 Conference on Electrical, Electronics and Computer Science, Bhopal, India."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chan, H.-L., Ouyang, Y., Chen, R.-S., Lai, Y.-H., Kuo, C.-C., Liao, G.-S., Hsu, W.-Y., and Chang, Y.-J. (2023). Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing. Sensors, 23.","DOI":"10.3390\/s23010495"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3878","DOI":"10.1109\/TNSRE.2024.3487216","article-title":"A Novel Multi-Feature Fusion Network with Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition","volume":"32","author":"Hu","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","unstructured":"Patel, Y. (2022). Classification of Static Poses in a Medical Bed Through Machine Learning Using IMU Sensors. [Bachelor\u2019s Thesis, Curtin University]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Al-azzawi, S.S., Khaksar, S., Hadi, E.K., Agrawal, H., and Murray, I. (2021). HeadUp: A Low-Cost Solution for Tracking Head Movement of Children with Cerebral Palsy Using IMU. Sensors, 21.","DOI":"10.3390\/s21238148"},{"key":"ref_19","unstructured":"(2024, December 27). Movella DOT. Available online: https:\/\/www.movella.com\/products\/wearables\/movella-dot."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khaksar, S., Pieters, S., Borazjani, B., Hyde, J., Booker, H., Khokhar, A., Murray, I., and Campbell, A. (2022). Posture Monitoring and Correction Exercises for Workers in Hostile Environments Utilizing Non-Invasive Sensors: Algorithm Development and Validation. Sensors, 22.","DOI":"10.3390\/s22249618"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hendry, D., Leadbetter, R., McKee, K., Hopper, L., Wild, C., O\u2019Sullivan, P., Straker, L., and Campbell, A. (2020). An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data. Sensors, 20.","DOI":"10.3390\/s20030740"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kroll, L., B\u00f6hning, N., M\u00fc\u00dfigbrodt, H., Stahl, M., Halkin, P., Liehr, B., Grunow, C., Kujumdshieva-B\u00f6hning, B., Freise, C., and Hopfenm\u00fcller, W. (2020). Non-contact monitoring of agitation and use of a sheltering device in patients with dementia in emergency departments: A feasibility study. BMC Psychiatry, 20.","DOI":"10.1186\/s12888-020-02573-5"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s11548-018-1895-3","article-title":"Patient 3D body pose estimation from pressure imaging","volume":"14","author":"Casas","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101431","DOI":"10.1016\/j.accpm.2024.101431","article-title":"Is artificial intelligence prepared for the 24-h shifts in the ICU?","volume":"43","author":"Gonzalez","year":"2024","journal-title":"Anaesth. Crit. Care Pain Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/499\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:29:57Z","timestamp":1759919397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,16]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["s25020499"],"URL":"https:\/\/doi.org\/10.3390\/s25020499","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,16]]}}}