{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:49:19Z","timestamp":1792806559138,"version":"4.5.0"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,12]],"date-time":"2023-02-12T00:00:00Z","timestamp":1676160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for Digital Innovations, Lower Saxony","award":["ZN3491"],"award-info":[{"award-number":["ZN3491"]}]},{"name":"Open Access Publication Funds of Technische Universit\u00e4t Braunschweig","award":["ZN3491"],"award-info":[{"award-number":["ZN3491"]}],"id":[{"id":"https:\/\/ror.org\/010nsgg66","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer\u2019s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.<\/jats:p>","DOI":"10.3390\/s23042066","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T02:14:11Z","timestamp":1676254451000},"page":"2066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8966-0176","authenticated-orcid":false,"given":"Mostafa","family":"Haghi","sequence":"first","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 38106 Braunschweig, Lower Saxony, Germany"},{"name":"Ubiquitous Computing Lab, Department of Computer Science, Konstanz University of Applied Sciences, 78462 Konstanz, Baden-W\u00fcrttemberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9441-1422","authenticated-orcid":false,"given":"Arman","family":"Ershadi","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 38106 Braunschweig, Lower Saxony, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3492-4407","authenticated-orcid":false,"given":"Thomas M.","family":"Deserno","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 38106 Braunschweig, Lower Saxony, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"key":"ref_1","unstructured":"(2022, February 11). The Top 10 Causes of Death. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"The WHOQOL Group (1995). The World Health Organization quality of life assessment (WHOQOL): Position paper from the World Health Organization. Soc. Sci. Med., 41, 1403\u20131409.","DOI":"10.1016\/0277-9536(95)00112-K"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s00702-019-01979-0","article-title":"Association between physical activity and dementia\u2019s risk factors in patients with Parkinson\u2019s disease","volume":"126","author":"Alwardat","year":"2019","journal-title":"J. Neural Transm."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1590\/1980-57642020dn14-020001","article-title":"Daily functioning and dementia","volume":"14","author":"Cipriani","year":"2020","journal-title":"Dement. Neuropsychol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.math.2011.09.006","article-title":"Towards monitoring lumbo-pelvic posture in real-life situations: Concurrent validity of a novel posture monitor and a traditional laboratory-based motion analysis system","volume":"17","author":"Campbell","year":"2012","journal-title":"Man. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jbiomech.2017.09.037","article-title":"Wearable technology for spine movement assessment: A systematic review","volume":"64","author":"Papi","year":"2017","journal-title":"J. Biomech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1093\/pm\/pny062","article-title":"Pain intensity, disability, and quality of life in patients with chronic low Back pain: Does age matter?","volume":"20","author":"Wettstein","year":"2019","journal-title":"Pain Med."},{"key":"ref_8","unstructured":"(2022, February 11). Ageing and Health. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ageing-and-health#:~:text=At%20this%20time%20the%20share,2050%20to%20reach%20426%20million."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Susnea, I., Dumitriu, L., Talmaciu, M., Pecheanu, E., and Munteanu, D. (2019). Unobtrusive monitoring the daily activity routine of elderly people living alone, with low-cost binary sensors. Sensors, 19.","DOI":"10.3390\/s19102264"},{"key":"ref_10","unstructured":"Vinoth Kumar, P., Gunapriya, B., Sivaranjani, S., Gomathi, P., Rajesh, T., Sujitha, S., and Deebanchakkarawarthi, G. (2022). Mobile Computing and Sustainable Informatics, Springer."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deserno, T.M. (2020, January 17\u201319). Transforming smart vehicles and smart homes into private diagnostic spaces. Proceedings of the 2020 2nd Asia Pacific Information Technology Conference, Bali Island, Indonesia.","DOI":"10.1145\/3379310.3379325"},{"key":"ref_12","unstructured":"Haghi, M., Spicher, N., Wang, J., and Deserno, T.M. (2022). Accident and Emergency Informatics, IOS Press."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, J., Spicher, N., Warnecke, J.M., Haghi, M., Schwartze, J., and Deserno, T.M. (2021). Unobtrusive health monitoring in private spaces: The smart home. Sensors, 21.","DOI":"10.3390\/s21030864"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-015-0050-4","article-title":"Recognition of activities of daily living in healthy subjects using two ad hoc classifiers","volume":"14","author":"Urwyler","year":"2015","journal-title":"Biomed. Eng. Online"},{"key":"ref_15","unstructured":"Shuvo, M.M.H., Ahmed, N., Nouduri, K., and Palaniappan, K. (2020, January 13\u201315). A Hybrid approach for human activity recognition with support vector machine and 1D convolutional neural network. Proceedings of the 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Majumder, S., Mondal, T., and Deen, M.J. (2017). Wearable sensors for remote health monitoring. Sensors, 17.","DOI":"10.3390\/s17010130"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bartalesi, R., Lorussi, F., De Rossi, D., Tesconi, M., and Tognetti, A. (September, January 31). Wearable monitoring of lumbar spine curvature by inertial and e-textile sensory fusion. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina.","DOI":"10.1109\/IEMBS.2010.5627294"},{"key":"ref_18","unstructured":"Anzai, Y. (2012). Pattern Recognition and Machine Learning, Elsevier."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1109\/JSEN.2016.2515101","article-title":"The lower limbs kinematics analysis by wearable sensor shoes","volume":"16","author":"Li","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"661676","DOI":"10.3389\/fcomp.2021.661676","article-title":"Wearable devices for gait analysis in intelligent healthcare","volume":"3","author":"Liu","year":"2021","journal-title":"Front. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"D\u00edaz, S., Stephenson, J.B., and Labrador, M.A. (2019). Use of wearable sensor technology in gait, balance, and range of motion analysis. Appl. Sci., 10.","DOI":"10.3390\/app10010234"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101227","DOI":"10.1016\/j.smrv.2019.101227","article-title":"A systematic review of the accuracy of sleep wearable devices for estimating sleep onset","volume":"49","author":"Scott","year":"2020","journal-title":"Sleep Med. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12891-019-2430-6","article-title":"The role of wearables in spinal posture analysis: A systematic review","volume":"20","author":"Simpson","year":"2019","journal-title":"BMC Musculoskelet. Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/JSEN.2015.2423562","article-title":"A high reliability wearable device for elderly fall detection","volume":"15","author":"Pierleoni","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"180","DOI":"10.15446\/dyna.v84n201.59054","article-title":"Wearable low-cost inertial sensor-based electrogoniometer for measuring joint range of motion","volume":"84","year":"2017","journal-title":"Dyna"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.3390\/s16071067","article-title":"A novel wearable device for food intake and physical activity recognition","volume":"16","author":"Farooq","year":"2016","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6441","DOI":"10.3390\/s150306441","article-title":"MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: A review on research activities in Italy","volume":"15","author":"Ciuti","year":"2015","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Farooq, M., and Sazonov, E. (2017, January 4\u20136). Real time monitoring and recognition of eating and physical activity with a wearable device connected to the eyeglass. Proceedings of the 2017 Eleventh International Conference on Sensing Technology (ICST), Sydney, Australia.","DOI":"10.1109\/ICSensT.2017.8304420"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7772","DOI":"10.3390\/s100807772","article-title":"A review of accelerometry-based wearable motion detectors for physical activity monitoring","volume":"10","author":"Yang","year":"2010","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Crema, C., Depari, A., Flammini, A., Sisinni, E., Haslwanter, T., and Salzmann, S. (2017, January 13\u201315). IMU-based solution for automatic detection and classification of exercises in the fitness scenario. Proceedings of the 2017 IEEE Sensors Applications Symposium (SAS), Glassboro, NJ, USA.","DOI":"10.1109\/SAS.2017.7894068"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Spinsante, S., and Scalise, L. (2018, January 11\u201313). Measurement of elderly daily physical activity by unobtrusive instrumented shoes. Proceedings of the 2018 IEEE international symposium on medical measurements and applications (MeMeA), Rome, Italy.","DOI":"10.1109\/MeMeA.2018.8438675"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/JSEN.2016.2514606","article-title":"A novel infrared motion sensing system for compressive classification of physical activity","volume":"16","author":"Guan","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jang, Y., Shin, S., Lee, J.W., and Kim, S. (2007, January 22\u201326). A preliminary study for portable walking distance measurement system using ultrasonic sensors. Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353535"},{"key":"ref_34","unstructured":"Zhang, H., Li, L., Jia, W., Fernstrom, J.D., Sclabassi, R.J., and Sun, M. (September, January 31). Recognizing physical activity from ego-motion of a camera. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1111\/obr.12021","article-title":"Daily physical activity assessment with accelerometers: New insights and validation studies","volume":"14","author":"Plasqui","year":"2013","journal-title":"Obes. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/S0268-0033(97)00084-3","article-title":"Effects of a lumbar support on spine posture and motion assessed by electrogoniometer and recording","volume":"13","author":"Thoumie","year":"1998","journal-title":"Clin. Biomech."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1007\/s00586-012-2312-1","article-title":"Comparative evaluation of a novel measurement tool to assess lumbar spine posture and range of motion","volume":"21","author":"Rohlmann","year":"2012","journal-title":"Eur. Spine J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s00586-008-0586-0","article-title":"Trunk posture monitoring with inertial sensors","volume":"17","author":"Wong","year":"2008","journal-title":"Eur. Spine J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Joshi, S., and Abdelfattah, E. (2021, January 27\u201330). Deep Neural Networks for Time Series Classification in Human Activity Recognition. Proceedings of the 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON53756.2021.9623228"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Perez-Gamboa, S., Sun, Q., and Zhang, Y. (2021, January 22\u201325). Improved sensor based human activity recognition via hybrid convolutional and recurrent neural networks. Proceedings of the 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Kailua-Kona, HI, USA.","DOI":"10.1109\/INERTIAL51137.2021.9430460"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2066\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:32:09Z","timestamp":1760121129000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,12]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23042066"],"URL":"https:\/\/doi.org\/10.3390\/s23042066","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,12]]}}}