{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T05:24:28Z","timestamp":1778304268588,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"France Relance"},{"name":"Delta Dore company"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly focused on recognition through pre-segmented sensor data. In this paper, real-time human activity recognition based on streaming sensors is investigated. The proposed methodology incorporates dynamic event windowing based on spatio-temporal correlation and the knowledge of activity trigger sensor to recognize activities and record new events. The objective is to determine whether the last event that just happened belongs to the current activity, or if it is the sign of the start of a new activity. For this, we consider the correlation between sensors in view of what can be seen in the history of past events. The proposed algorithm contains three steps: verification of sensor correlation (SC), verification of temporal correlation (TC), and determination of the activity triggering the sensor. The proposed approach is applied to a real case study: the \u201cAruba\u201d dataset from the CASAS database. F1 score is used to assess the quality of the segmentation. The results show that the proposed approach segments several activities (sleeping, bed to toilet, meal preparation, eating, housekeeping, working, entering home, and leaving home) with an F1 score of 0.63\u20130.99.<\/jats:p>","DOI":"10.3390\/s22145458","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T22:38:50Z","timestamp":1658443130000},"page":"5458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Dynamic Segmentation of Sensor Events for Real-Time Human Activity Recognition in a Smart Home Context"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5291-7297","authenticated-orcid":false,"given":"Houda","family":"Najeh","sequence":"first","affiliation":[{"name":"IMT Atlantique, Lab-STICC, 29238 Brest, France"},{"name":"Delta Dore Company, 35270 Bonnemain, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0655-2880","authenticated-orcid":false,"given":"Christophe","family":"Lohr","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Lab-STICC, 29238 Brest, France"}]},{"given":"Benoit","family":"Leduc","sequence":"additional","affiliation":[{"name":"Delta Dore Company, 35270 Bonnemain, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1016\/j.rser.2014.09.026","article-title":"Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour","volume":"41","author":"Frederiks","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","first-page":"101212","article-title":"The impacts of occupant behavior on building energy consumption: A review","volume":"45","author":"Chen","year":"2021","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"110714","DOI":"10.1016\/j.rser.2021.110714","article-title":"Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings","volume":"142","author":"Amasyali","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1097\/HRP.0000000000000217","article-title":"Digital technology for building capacity of non-specialist health workers for task-sharing and scaling up mental health care globally","volume":"27","author":"Naslund","year":"2019","journal-title":"Harv. Rev. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.apm.2021.08.032","article-title":"Output feedback control for energy-saving asymmetric hydraulic servo system based on desired compensation approach","volume":"101","author":"Wang","year":"2022","journal-title":"Appl. Math. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"117775","DOI":"10.1016\/j.apenergy.2021.117775","article-title":"Smart fusion of sensor data and human feedback for personalized energy-saving recommendations","volume":"305","author":"Varlamis","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_7","unstructured":"Dore, D. (2011). Building Management Systems Solutions (BMS)."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s11042-021-11410-0","article-title":"State-of-the-art survey on activity recognition and classification using smartphones and wearable sensors","volume":"81","author":"Chaurasia","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lentzas, A., Dalagdi, E., and Vrakas, D. (2022). Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms. Sensors, 22.","DOI":"10.3390\/s22062353"},{"key":"ref_10","unstructured":"Alruban, A., Alobaidi, H., and Li, N.C. (2022). Physical activity recognition by utilising smartphone sensor signals. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jin, X., Saifullah, A., Lu, C., and Zeng, P. (May, January 29). Real-time scheduling for event-triggered and time-triggered flows in industrial wireless sensor-actuator networks. Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737373"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bouchabou, D., Nguyen, S.M., Lohr, C., Leduc, B., and Kanellos, I. (2021). Fully convolutional network bootstrapped by word encoding and embedding for activity recognition in smart homes. International Workshop on Deep Learning for Human Activity Recognition, Springer.","DOI":"10.1007\/978-981-16-0575-8_9"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.pmcj.2012.07.003","article-title":"Activity recognition on streaming sensor data","volume":"10","author":"Krishnan","year":"2014","journal-title":"Pervasive Mob. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/TKDE.2011.51","article-title":"A knowledge-driven approach to activity recognition in smart homes","volume":"24","author":"Chen","year":"2011","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.pmcj.2012.11.004","article-title":"Dynamic sensor data segmentation for real-time knowledge-driven activity recognition","volume":"10","author":"Okeyo","year":"2014","journal-title":"Pervasive Mob. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sfar, H., and Bouzeghoub, A. (2019, January 8\u201312). DataSeg: Dynamic streaming sensor data segmentation for activity recognition. Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, Limassol, Cyprus.","DOI":"10.1145\/3297280.3297332"},{"key":"ref_17","first-page":"1245","article-title":"A comparative analysis of windowing approaches in dense sensing environments","volume":"2","author":"Quigley","year":"2018","journal-title":"Multidiscip. Digit. Publ. Inst. Proc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bouchabou, D., Nguyen, S.M., Lohr, C., LeDuc, B., and Kanellos, I. (2021). A survey of human activity recognition in smart homes based on IoT sensors algorithms: Taxonomies, challenges, and opportunities with deep learning. Sensors, 21.","DOI":"10.3390\/s21186037"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/98.626982","article-title":"A new location technique for the active office","volume":"4","author":"Ward","year":"1997","journal-title":"IEEE Pers. Commun."},{"key":"ref_20","unstructured":"Liao, L., Fox, D., and Kautz, H. (2005, January 5\u20138). Location-based activity recognition. Proceedings of the Advances in Neural Information Processing Systems 18 (NIPS 2005), Vancouver, BC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.pmcj.2010.11.004","article-title":"Motion-and location-based online human daily activity recognition","volume":"7","author":"Zhu","year":"2011","journal-title":"Pervasive Mob. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","article-title":"Deep-learning-enhanced human activity recognition for Internet of healthcare things","volume":"7","author":"Zhou","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tapia, E.M., Intille, S.S., and Larson, K. (2004). Activity recognition in the home using simple and ubiquitous sensors. International Conference on Pervasive Computing, Springer.","DOI":"10.1007\/978-3-540-24646-6_10"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"506","DOI":"10.2197\/ipsjdc.3.506","article-title":"Applying ontology and probabilistic model to human activity recognition from surrounding things","volume":"3","author":"Yamada","year":"2007","journal-title":"IPSJ Digit. Cour."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Silva, C.A.S., Amayri, M., and Basu, K. (2021). Characterization of Energy Demand and Energy Services Using Model-Based and Data-Driven Approaches. Towards Energy Smart Homes, Springer.","DOI":"10.1007\/978-3-030-76477-7_7"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chui, K.T., Gupta, B.B., Liu, R.W., and Vasant, P. (2021). Handling data heterogeneity in electricity load disaggregation via optimized complete ensemble empirical mode decomposition and wavelet packet transform. Sensors, 21.","DOI":"10.3390\/s21093133"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.enbuild.2017.06.042","article-title":"Comprehensive feature selection for appliance classification in NILM","volume":"151","author":"Sadeghianpourhamami","year":"2017","journal-title":"Energy Build."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tra, V., Amayri, M., and Bouguila, N. (2022). Outlier Detection Via Multiclass Deep Autoencoding Gaussian Mixture Model for Building Chiller Diagnosis. Energy Build., 111893.","DOI":"10.1016\/j.enbuild.2022.111893"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wang, G., and Guo, X. (2022). Online Activity Recognition Combining Dynamic Segmentation and Emergent Modeling. Sensors, 22.","DOI":"10.3390\/s22062250"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/TETC.2018.2870047","article-title":"Time-bounded activity recognition for ambient assisted living","volume":"9","author":"Wan","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Al Machot, F., Ranasinghe, S., Plattner, J., and Jnoub, N. (2018, January 18\u201323). Human activity recognition based on real life scenarios. Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece.","DOI":"10.1109\/PERCOMW.2018.8480138"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MC.2012.328","article-title":"CASAS: A smart home in a box","volume":"46","author":"Cook","year":"2012","journal-title":"Computer"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s00779-014-0824-x","article-title":"Dynamic sensor event segmentation for real-time activity recognition in a smart home context","volume":"19","author":"Wan","year":"2015","journal-title":"Pers. Ubiquitous Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5458\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:47Z","timestamp":1760140547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,21]]},"references-count":33,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145458"],"URL":"https:\/\/doi.org\/10.3390\/s22145458","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,21]]}}}