{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:37:13Z","timestamp":1771706233007,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"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>This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit sensors installed as wearable devices. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based and distance-based strategies, are employed to achieve the desired classification performance. The proposed system is shown to be highly accurate and computationally efficient for different algorithmic parameters, which is relevant in the context of limited computing resources on typical wearable devices. Extensive experimentation and simulation study have been conducted on multi-user human activity data from the public domain in order to analyze the trade-off between classification accuracy and computation complexity of the proposed learning paradigm with different algorithmic hyper-parameters. With 4.17 h of training time for 8000 activity episodes, the proposed SSL approach consumes at most 20 KB of CPU memory space, while providing a maximum accuracy of 90% and 100% classification rates.<\/jats:p>","DOI":"10.3390\/s24144444","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T09:23:08Z","timestamp":1720603388000},"page":"4444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7424-3070","authenticated-orcid":false,"given":"Avirup","family":"Roy","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4436-1054","authenticated-orcid":false,"given":"Hrishikesh","family":"Dutta","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit Kumar","family":"Bhuyan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subir","family":"Biswas","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e37354","DOI":"10.2196\/37354","article-title":"The Apple Watch for Monitoring Mental Health\u2013Related Physiological Symptoms: Literature Review","volume":"9","author":"Lui","year":"2022","journal-title":"JMIR Ment. Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.70252\/NJQX2719","article-title":"Current State of Commercial Wearable Technology in Physical Activity Monitoring 2015\u20132017","volume":"11","author":"Bunn","year":"2018","journal-title":"Int. J. Exerc. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bianchini, M., Maggini, M., and Jain, L.C. (2013). Semi-supervised Learning. Handbook on Neural Information Processing, Springer. Intelligent Systems Reference Library.","DOI":"10.1007\/978-3-642-36657-4"},{"key":"ref_4","unstructured":"Rasekh, A., Chen, C.-A., and Lu, Y. (2014). Human Activity Recognition using Smartphone. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TBCAS.2011.2160540","article-title":"Sensor Positioning for Activity Recognition Using Wearable Accelerometers","volume":"5","author":"Atallah","year":"2011","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2499621","article-title":"A tutorial on human activity recognition using body-worn inertial sensors","volume":"46","author":"Bulling","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.eswa.2019.04.057","article-title":"A survey on wearable sensor modality centred human activity recognition in health care","volume":"137","author":"Wang","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., and Craddock, I. (2018). A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics, 5.","DOI":"10.20944\/preprints201803.0147.v1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.eswa.2018.03.056","article-title":"Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges","volume":"105","author":"Nweke","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1088\/0967-3334\/35\/11\/2269","article-title":"A comparison of activity classification in younger and older cohorts using a smartphone","volume":"35","author":"Wang","year":"2014","journal-title":"Physiol. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Albert, M.V., Toledo, S., Shapiro, M., and Kording, K. (2012). Using Mobile Phones for Activity Recognition in Parkinson\u2019s Patients. Front. Neurol., 3.","DOI":"10.3389\/fneur.2012.00158"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","article-title":"Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living","volume":"7","author":"Weiss","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","unstructured":"Weiss, G.M., and Lockhart, J.W. (2012, January 22\u201326). The Impact of Personalization on Smartphone-Based Activity Recognition. Proceedings of the Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, ON, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"32066","DOI":"10.1109\/ACCESS.2020.2973425","article-title":"On the Personalization of Classification Models for Human Activity Recognition","volume":"8","author":"Ferrari","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Weiss, G.M., Timko, J.L., Gallagher, C.M., Yoneda, K., and Schreiber, A.J. (2016, January 24\u201327). Smartwatch-based activity recognition: A machine learning approach. Proceedings of the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2016.7455925"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Berchtold, M., Budde, M., Gordon, D., Schmidtke, H.R., and Beigl, M. (2010, January 10\u201313). ActiServ: Activity Recognition Service for mobile phones. Proceedings of the International Symposium on Wearable Computers (ISWC) 2010, Seoul, Republic of Korea.","DOI":"10.1109\/ISWC.2010.5665868"},{"key":"ref_17","unstructured":"Siirtola, P., Koskim\u00e4ki, H., and R\u00f6ning, J. (2024, May 09). From User-Independent to Personal Human Activity Recognition Models Using Smartphone Sensors. Available online: https:\/\/oulurepo.oulu.fi\/handle\/10024\/22078."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s12652-011-0064-0","article-title":"The adARC pattern analysis architecture for adaptive human activity recognition systems","volume":"4","author":"Roggen","year":"2013","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s10115-013-0665-3","article-title":"Transfer learning for activity recognition: A survey","volume":"36","author":"Cook","year":"2013","journal-title":"Knowl. Inf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2836","DOI":"10.1109\/TMM.2018.2814339","article-title":"Personalized Classifier for Food Image Recognition","volume":"20","author":"Horiguchi","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.cmpb.2018.04.003","article-title":"Enhancement of gesture recognition for contactless interface using a personalized classifier in the operating room","volume":"161","author":"Cho","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Roy, A., Dutta, H., Griffith, H., and Biswas, S. (2022). An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation. Sensors, 22.","DOI":"10.3390\/s22072514"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/TCE.2020.3036277","article-title":"A Personalized Classifier for Human Motion Activities With Semi-Supervised Learning","volume":"66","author":"Singh","year":"2020","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Roy, A., Dutta, H., Bhuyan, A.K., and Biswas, S.K. (2023, January 15\u201317). Semi-Supervised Learning Using Sparsely Labelled Sip Events for Online Hydration Tracking Systems. Proceedings of the 2023 International Conference on Machine Learning and Applications (ICMLA), Jacksonville, FL, USA.","DOI":"10.1109\/ICMLA58977.2023.00273"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Oh, S., Ashiquzzaman, A., Lee, D., Kim, Y., and Kim, J. (2021). Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning. Sensors, 21.","DOI":"10.3390\/s21082760"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1109\/TMC.2018.2793913","article-title":"Bi-View Semi-Supervised Learning Based Semantic Human Activity Recognition Using Accelerometers","volume":"17","author":"Lv","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_27","unstructured":"Weiss, G. (2023, September 04). WISDM Smartphone and Smartwatch Activity and Biometrics Dataset. Available online: https:\/\/archive.ics.uci.edu\/dataset\/507\/wisdm+smartphone+and+smartwatch+activity+and+biometrics+dataset."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, M., Meng, Z., and King, I. (2020, January 17\u201320). FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00082"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1038\/sj.ijo.0801049","article-title":"Physical inactivity, sedentary lifestyle and obesity in the European Union","volume":"23","author":"Hu","year":"1999","journal-title":"Int. J. Obes."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ka\u0144toch, E. (2018). Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk. Sensors, 18.","DOI":"10.3390\/s18103219"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"298","DOI":"10.3934\/publichealth.2016.2.298","article-title":"Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior","volume":"3","author":"Montoye","year":"2016","journal-title":"AIMS Public Health"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s43586-022-00184-w","article-title":"Principal component analysis","volume":"2","author":"Greenacre","year":"2022","journal-title":"Nat. Rev. Methods Primer"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","article-title":"Unsupervised K-Means Clustering Algorithm","volume":"8","author":"Sinaga","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","first-page":"1","article-title":"Gaussian Mixture Model Clustering with Incomplete Data","volume":"17","author":"Zhang","year":"2021","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4444\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:12:27Z","timestamp":1760109147000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4444"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,9]]},"references-count":34,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24144444"],"URL":"https:\/\/doi.org\/10.3390\/s24144444","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,9]]}}}