{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T00:45:49Z","timestamp":1769215549395,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T00:00:00Z","timestamp":1603238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000738","name":"U.S. Department of Veterans Affairs","doi-asserted-by":"publisher","award":["PH-2018-SAHAT-002"],"award-info":[{"award-number":["PH-2018-SAHAT-002"]}],"id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.<\/jats:p>","DOI":"10.3390\/s20205953","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T20:51:00Z","timestamp":1603399860000},"page":"5953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Pervasive Lying Posture Tracking"],"prefix":"10.3390","volume":"20","author":[{"given":"Parastoo","family":"Alinia","sequence":"first","affiliation":[{"name":"Philips Research North America, Cambridge, MA 02141, USA"},{"name":"School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA"}]},{"given":"Ali","family":"Samadani","sequence":"additional","affiliation":[{"name":"Philips Research North America, Cambridge, MA 02141, USA"}]},{"given":"Mladen","family":"Milosevic","sequence":"additional","affiliation":[{"name":"Philips Research North America, Cambridge, MA 02141, USA"}]},{"given":"Hassan","family":"Ghasemzadeh","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3618-0125","authenticated-orcid":false,"given":"Saman","family":"Parvaneh","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.amjmed.2016.03.032","article-title":"Benefits of early active mobility in the medical intensive care unit: A pilot study","volume":"129","author":"Azuh","year":"2016","journal-title":"Am. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/jhm.2546","article-title":"Promoting mobility and reducing length of stay in hospitalized general medicine patients: A quality-improvement project","volume":"11","author":"Hoyer","year":"2016","journal-title":"J. Hosp. Med."},{"key":"ref_3","first-page":"18","article-title":"Using a national guideline to prevent and manage pressure ulcers","volume":"21","author":"Neilson","year":"2014","journal-title":"Nurs. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1136\/jnnp.67.4.439","article-title":"Sudden unexpected death in epilepsy (SUDEP): A clinical perspective and a search for risk factors","volume":"67","author":"Kloster","year":"1999","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1378\/chest.11-2591","article-title":"Sleep and sleep disorders in the hospital","volume":"141","author":"Venkateshiah","year":"2012","journal-title":"Chest"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1016\/0140-6736(91)92917-Q","article-title":"Prospective cohort study of prone sleeping position and sudden infant death syndrome","volume":"337","author":"Dwyer","year":"1991","journal-title":"Lancet"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1007\/s12603-009-0110-1","article-title":"Sleep disturbance in relation to health-related quality of life in adults: The Fels Longitudinal Study","volume":"13","author":"Lee","year":"2009","journal-title":"J. Nutr. Health Aging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1046\/j.0283-9318.2003.00250.x","article-title":"Immobility\u2014A major risk factor for development of pressure ulcers among adult hospitalized patients: A prospective study","volume":"18","author":"Lindgren","year":"2004","journal-title":"Scand. J. Caring Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2130","DOI":"10.1378\/chest.128.4.2130","article-title":"Prevalence of positional sleep apnea in patients undergoing polysomnography","volume":"128","author":"Mador","year":"2005","journal-title":"Chest"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wrzus, C., Brandmaier, A.M., Von Oertzen, T., M\u00fcller, V., Wagner, G.G., and Riediger, M. (2012). A new approach for assessing sleep duration and postures from ambulatory accelerometry. PLoS ONE, 7.","DOI":"10.2139\/ssrn.2172703"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Yang, G.Z. (2015, January 9\u201312). Monitoring cardio-respiratory and posture movements during sleep: What can be achieved by a single motion sensor. Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA.","DOI":"10.1109\/BSN.2015.7299409"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40001-018-0326-9","article-title":"A lightweight sensing platform for monitoring sleep quality and posture: A simulated validation study","volume":"23","author":"Kwasnicki","year":"2018","journal-title":"Eur. J. Med Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Austin, D., Beattie, Z.T., Riley, T., Adami, A.M., Hagen, C.C., and Hayes, T.L. (September, January 28). Unobtrusive classification of sleep and wakefulness using load cells under the bed. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6347179"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pouyan, M.B., Ostadabbas, S., Farshbaf, M., Yousefi, R., Nourani, M., and Pompeo, M. (2013, January 16\u201318). Continuous eight-posture classification for bed-bound patients. Proceedings of the 2013 6th International Conference on Biomedical Engineering and Informatics, Hangzhou, China.","DOI":"10.1109\/BMEI.2013.6746919"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yousefi, R., Ostadabbas, S., Faezipour, M., Farshbaf, M., Nourani, M., Tamil, L., and Pompeo, M. (September, January 30). Bed posture classification for pressure ulcer prevention. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091813"},{"key":"ref_16","unstructured":"Cary, D., Collinson, R., Sterling, M., and Briffa, K. (2016). Examining the relationship between sleep posture and morning spinal symptoms in the habitual environment using infrared cameras. J. Sleep Disord. Treat. Care."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/JBHI.2013.2252911","article-title":"Estimation of body postures on bed using unconstrained ECG measurements","volume":"17","author":"Lee","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1089\/tmj.2010.0078","article-title":"Wireless portable electrocardiogram and a tri-axis accelerometer implementation and application on sleep activity monitoring","volume":"17","author":"Chang","year":"2011","journal-title":"Telemed. e-Health"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wai, A.A.P., Huang, W., Fook, V.F.S., Biswas, J., Chi-Chun, H., and Koujuch, L. (2010, January 19\u201321). Situation-aware patient monitoring in and around the bed using multimodal sensing intelligence. Proceedings of the 2010 Sixth International Conference on Intelligent Environments, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IE.2010.31"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, W., Wai, A.A.P., Foo, S.F., Biswas, J., Hsia, C.C., and Liou, K. (2010, January 23\u201326). Multimodal sleeping posture classification. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.1054"},{"key":"ref_21","first-page":"875371","article-title":"Sleep monitoring system using kinect sensor","volume":"11","author":"Lee","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"045005","DOI":"10.1088\/2057-1976\/aabef4","article-title":"Atrial fibrillation classification using step-by-step machine learning","volume":"4","author":"Goodfellow","year":"2018","journal-title":"Biomed. Phys. Eng. Express"},{"key":"ref_23","first-page":"8910","article-title":"Sitting posture recognition by body pressure distribution and airbag regulation strategy based on seat comfort evaluation","volume":"2019","author":"Yongxiang","year":"2019","journal-title":"J. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"012014","DOI":"10.1088\/1742-6596\/1437\/1\/012014","article-title":"Human Posture Recognition in Intelligent Healthcare","volume":"1437","author":"Yang","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Otoda, Y., Mizumoto, T., Arakawa, Y., Nakajima, C., Kohana, M., Uenishi, M., and Yasumoto, K. (2018, January 12\u201314). Census: Continuous posture sensing chair for office workers. Proceedings of the 2018 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2018.8326275"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1093\/geront\/gnz068","article-title":"Why older adults and their children disagree about in-home surveillance technology, sensors, and tracking","volume":"60","author":"Berridge","year":"2020","journal-title":"Gerontologist"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/s41746-020-0237-3","article-title":"Modernizing and designing evaluation frameworks for connected sensor technologies in medicine","volume":"3","author":"Coravos","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.5664\/jcsm.6802","article-title":"Improving sleep quality assessment using wearable sensors by including information from postural\/sleep position changes and body acceleration: A comparison of chest-worn sensors, wrist actigraphy, and polysomnography","volume":"13","author":"Razjouyan","year":"2017","journal-title":"J. Clin. Sleep Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1145\/3264908","article-title":"SleepGuard: Capturing rich sleep information using smartwatch sensing data","volume":"2","author":"Chang","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jeng, P.Y., Wang, L.C., Hu, C.J., and Wu, D. (2020, October 20). A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach. Available online: https:\/\/www.preprints.org\/manuscript\/201907.0060\/v1.","DOI":"10.20944\/preprints201907.0060.v1"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Saeedi, R., Schimert, B., and Ghasemzadeh, H. (2014, January 13\u201317). Cost-sensitive feature selection for on-body sensor localization. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, WA, USA.","DOI":"10.1145\/2638728.2641313"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Baskin, I.I., Marcou, G., Horvath, D., and Varnek, A. (2017). Bagging and boosting of classification models. Tutorials Chemoinform., 241\u2013247.","DOI":"10.1002\/9781119161110.ch15"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.engappai.2019.07.011","article-title":"Performance enhancing techniques for deep learning models in time series forecasting","volume":"85","author":"Fang","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lefebvre, G., Berlemont, S., Mamalet, F., and Garcia, C. (2013). BLSTM-RNN based 3D Gesture Classification. Artificial Neural Networks and Machine Learning\u2014ICANN 2013, Proceedings of the International Conference on Artificial Neural Networks, Sofia, Bulgaria, 10\u201313 September 2013, Springer.","DOI":"10.1007\/978-3-642-40728-4_48"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_39","unstructured":"Gamboa, J.C.B. (2017). Deep learning for time-series analysis. arXiv."},{"key":"ref_40","unstructured":"Sun, S., and Xie, Z. (2017, January 8\u201312). Bilstm-based models for metaphor detection. Proceedings of the National CCF Conference on Natural Language Processing and Chinese Computing, Dalian, China."},{"key":"ref_41","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_42","unstructured":"Olgu\u0131n, D.O., and Pentland, A.S. (2006, January 11\u201314). Human activity recognition: Accuracy across common locations for wearable sensors. Proceedings of the 2006 10th IEEE International Symposium on Wearable Computers, Montreux, Switzerland."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1016\/j.patcog.2010.04.019","article-title":"Comparative study on classifying human activities with miniature inertial and magnetic sensors","volume":"43","author":"Altun","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Taborri, J., Palermo, E., Masiello, D., and Rossi, S. (2017, January 22\u201325). Factorization of EMG via muscle synergies in walking task: Evaluation of intra-subject and inter-subject variability. Proceedings of the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Turin, Italy.","DOI":"10.1109\/I2MTC.2017.7969775"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., and Buhmann, J.M. (2010, January 23\u201326). The balanced accuracy and its posterior distribution. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.764"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ronao, C.A., and Cho, S.B. (2014, January 19\u201321). Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models. Proceedings of the 2014 10th International Conference on Natural Computation (ICNC), Xiamen, China.","DOI":"10.1109\/ICNC.2014.6975918"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"267","DOI":"10.2147\/NSS.S145777","article-title":"Sleep positions and nocturnal body movements based on free-living accelerometer recordings: Association with demographics, lifestyle, and insomnia symptoms","volume":"9","author":"Skarpsno","year":"2017","journal-title":"Nat. Sci. Sleep"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"M\u00fcnzner, S., Schmidt, P., Reiss, A., Hanselmann, M., Stiefelhagen, R., and D\u00fcrichen, R. (2017, January 11\u201315). CNN-based sensor fusion techniques for multimodal human activity recognition. Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, HI, USA.","DOI":"10.1145\/3123021.3123046"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TCDS.2018.2800167","article-title":"Multimodal human hand motion sensing and analysis\u2014A review","volume":"11","author":"Xue","year":"2018","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_51","unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.A. (2018). Data augmentation using synthetic data for time series classification with deep residual networks. arXiv."},{"key":"ref_52","unstructured":"DeVries, T., and Taylor, G.W. (2017). Dataset augmentation in feature space. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Gu, Y., Xiao, Y., and Pan, H. (2018, January 8\u201313). SensoryGANs: An Effective Generative Adversarial Framework for Sensor-based Human Activity Recognition. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489106"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5953\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:25:20Z","timestamp":1760178320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,21]]},"references-count":53,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20205953"],"URL":"https:\/\/doi.org\/10.3390\/s20205953","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,21]]}}}