{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:30:29Z","timestamp":1778808629595,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,20]],"date-time":"2019-02-20T00:00:00Z","timestamp":1550620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["H2020- PHC-21-2015 - 690140"],"award-info":[{"award-number":["H2020- PHC-21-2015 - 690140"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.<\/jats:p>","DOI":"10.3390\/s19040880","type":"journal-article","created":{"date-parts":[[2019,2,20]],"date-time":"2019-02-20T11:45:39Z","timestamp":1550663139000},"page":"880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0174-5840","authenticated-orcid":false,"given":"Aimilia","family":"Papagiannaki","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8228-0437","authenticated-orcid":false,"given":"Evangelia I.","family":"Zacharaki","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerasimos","family":"Kalouris","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Spyridon","family":"Kalogiannis","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Deltouzos","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Ellul","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasileios","family":"Megalooikonomou","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chiauzzi, E., Rodarte, C., and DasMahapatra, P. (2015). Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med., 13.","DOI":"10.1186\/s12916-015-0319-2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S268","DOI":"10.1093\/geront\/gnw031","article-title":"Updating the evidence for physical activity: Summative reviews of the epidemiological evidence, prevalence, and interventions to promote \u201cactive aging\u201d","volume":"56","author":"Bauman","year":"2016","journal-title":"Gerontologist"},{"key":"ref_3","first-page":"176","article-title":"Is technology present in frailty? Technology a back-up tool for dealing with frailty in the elderly: A systematic review","volume":"8","year":"2017","journal-title":"Aging Dis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Van Velsen, L., Illario, M., Jansen-Kosterink, S., Crola, C., di Somma, C., Colao, A., and Vollenbroek-Hutten, M. (2015). A community-based, technology-supported health service for detecting and preventing frailty among older adults: A participatory design development process. J. Aging Res., 2015.","DOI":"10.1155\/2015\/216084"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kalogiannis, S., Zacharaki, E.I., Deltouzos, K., Kotsani, M., Ellul, J., Benetos, A., and Megalooikonomou, V. (2018, January 3\u20135). Geriatric group analysis by clustering non-linearly embedded multi-sensor data. Proceedings of the 2018 Innovations in Intelligent Systems and Applications (INISTA), Thessaloniki, Greece.","DOI":"10.1109\/INISTA.2018.8466269"},{"key":"ref_6","first-page":"1","article-title":"Information and communicative technology use enhances psychological well-being of older adults: The roles of age, social connectedness, and frailty status","volume":"22","author":"Fang","year":"2017","journal-title":"Aging Ment. Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8651930","DOI":"10.1155\/2018\/8651930","article-title":"Tensor decomposition for multiple instance classification of high-order medical data","volume":"2018","author":"Papastergiou","year":"2018","journal-title":"Complexity"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Papagiannaki, A., Zacharaki, E.I., Deltouzos, K., Orselli, R., Freminet, A., Cela, S., Aristodemou, E., Polycarpou, M., Kotsani, M., and Benetos, A. (2018, January 17\u201320). Meeting challenges of activity recognition for ageing population in real life settings. Proceedings of the 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava, Czech Republic.","DOI":"10.1109\/HealthCom.2018.8531105"},{"key":"ref_9","unstructured":"(2019, February 19). FrailSafe Project. Available online: https:\/\/frailsafe-project.eu\/."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 18\u201323). Activity recognition from user-annotated acceleration data. Proceedings of the International Conference on Pervasive Computing, Linz\/Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"And\u00f2, B., Baglio, S., Lombardo, C.O., Marletta, V., Pergolizzi, E.A., Pistorio, A., and Valastro, A. (2015). ADL Detection for the Active Ageing of Elderly People. Ambient Assisted Living, Springer.","DOI":"10.1007\/978-3-319-18374-9_27"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pippa, E., Mporas, I., and Megalooikonomou, V. (2016, January 21\u201322). Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System. Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016), Rome, Italy.","DOI":"10.5220\/0005912200880095"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sebestyen, G., Stoica, I., and Hangan, A. (2016, January 8\u201310). Human activity recognition and monitoring for elderly people. Proceedings of the 2016 IEEE 12th International Conference on in Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP.2016.7737171"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ranasinghe, D.C., Torres, R.L.S., and Wickramasinghe, A. (2013, January 13\u201314). Automated activity recognition and monitoring of elderly using wireless sensors: Research challenges. Proceedings of the 2013 5th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), Bari, Italy.","DOI":"10.1109\/IWASI.2013.6576067"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.eswa.2012.09.004","article-title":"Elderly activities recognition and classification for applications in assisted living","volume":"40","author":"Chernbumroong","year":"2013","journal-title":"Expert Sys. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, J., Sohn, J., and Kim, S. (2017). Classification of Daily Activities for the Elderly Using Wearable Sensors. J. Healthc. Eng., 2017.","DOI":"10.1155\/2017\/8934816"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.gaitpost.2015.10.016","article-title":"Instrumented shoes for activity classification in the elderly","volume":"44","author":"Major","year":"2016","journal-title":"Gait Posture"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1089\/tmj.2013.0109","article-title":"Monitoring activities of daily living of the elderly and the potential for its use in telecare and telehealth: A review","volume":"19","author":"Gokalp","year":"2013","journal-title":"Telemed. e-Health"},{"key":"ref_19","first-page":"3995","article-title":"Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition","volume":"15","author":"Yang","year":"2015","journal-title":"IJCAI"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., and Zhang, J. (2014, January 6\u20137). Convolutional neural networks for human activity recognition using mobile sensors. Proceedings of the 2014 6th International Conference on Mobile Computing, Applications and Services (MobiCASE), Austin, TX, USA.","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F., and Roggen, D. (2016). Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jiang, W., and Yin, Z. (2015, January 26\u201330). Human activity recognition using wearable sensors by deep convolutional neural networks. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2806333"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"33","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_24","doi-asserted-by":"crossref","unstructured":"Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., F\u00f6rster, K., Tr\u00f6ster, G., Lukowicz, P., Bannach, D., Pirkl, G., and Ferscha, A. (2010, January 15\u201318). Collecting complex activity datasets in highly rich networked sensor environments. Proceedings of the 2010 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sagha, H., Digumarti, S.T., Mill\u00e1n, J.D.R., Chavarriaga, R., Calatroni, A., Roggen, D., and Tr\u00f6ster, G. (2011, January 9\u201312). Benchmarking classification techniques using the Opportunity human activity dataset. Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Anchorage, AK, USA.","DOI":"10.1109\/ICSMC.2011.6083628"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fekri, M., and Shafiq, M.O. (2018, January 17\u201320). Deep Convolutional Neural Network Learning for Activity Recognition using real-life sensor\u2019s data in smart devices. Proceedings of the IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava, Czech Republic.","DOI":"10.1109\/HealthCom.2018.8531150"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., and Pulickal, T.T. (2011, January 21). Design considerations for the WISDM smart phone-based sensor mining architecture. Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, San Diego, CA, USA.","DOI":"10.1145\/2003653.2003656"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_29","unstructured":"Pl\u00f6tz, T., Hammerla, N.Y., and Olivier, P. (2011, January 16\u201322). Feature learning for activity recognition in ubiquitous computing. Proceedings of the International Joint Conference on Artificial Intelligence, Barcelona, Spain."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1764","DOI":"10.1109\/TMC.2018.2789890","article-title":"Autonomous Training of Activity Recognition Algorithms in Mobile Sensors: A Transfer Learning Approach in Context-Invariant Views","volume":"17","author":"Rokni","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Saeedi, R., Norgaard, S., and Gebremedhin, A.H. (2017, January 11\u201314). A closed-loop deep learning architecture for robust activity recognition using wearable sensors. Proceedings of the IEEE International Conference on Big Data, Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8257960"},{"key":"ref_32","unstructured":"Hammerla, N., Halloran, S., and Ploetz, T. (arXiv, 2016). Deep, convolutional, and recurrent models for human activity recognition using wearables, arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ba\u00f1os, O., Damas, M., Pomares, H., Rojas, I., T\u00f3th, M.A., and Amft, O. (2012, January 5\u20138). A benchmark dataset to evaluate sensor displacement in activity recognition. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370437"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1145\/3130959","article-title":"Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems","volume":"1","author":"Rey","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_35","unstructured":"(2019, February 19). Smartex. Available online: http:\/\/www.smartex.it\/en\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kalogiannis, S., Deltouzos, K., Zacharaki, E.I., Vasilakis, A., Moustakas, K., Ellul, J., and Megalooikonomou, V. (2019). Integrating an openEHR-based personalized virtual model for the ageing population within HBase. BMC Med. Inf. Dec. Mak., 19.","DOI":"10.1186\/s12911-019-0745-8"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2012, January 3\u20135). Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. Proceedings of the International conference on ambient assisted living and Home Care, Vitoria-Gasteiz, Spain.","DOI":"10.1007\/978-3-642-35395-6_30"},{"key":"ref_38","unstructured":"Chakravarty, I.M., Roy, J.D., and Laha, R.G. (1967). Handbook of Methods of Applied Statistics, John Wiley and Sons."},{"key":"ref_39","first-page":"27","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s40708-016-0044-4","article-title":"Spike pattern recognition by supervised classification in low dimensional embedding space","volume":"3","author":"Zacharaki","year":"2016","journal-title":"Brain Inform."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mporas, I., Tsirka, V., Zacharaki, E., Koutroumanidis, M., and Megalooikonomou, V. (2014, January 27\u201330). Evaluation of time and frequency domain features for seizure detection from combined EEG and ECG signals. Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, Rhodes, Greece.","DOI":"10.1145\/2674396.2674412"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pippa, E., Zacharaki, E.I., \u00d6zdemir, A.T., Barshan, B., and Megalooikonomou, V. (2018, January 9\u201312). Global vs local classification models for multi-sensor data fusion. Proceedings of the 10th Hellenic Conference on Artificial Intelligence, Patras, Greece.","DOI":"10.1145\/3200947.3201034"},{"key":"ref_43","unstructured":"Kononenko, I. (1994, January 6\u20138). Estimating attributes: Analysis and extensions of RELIEF. Proceedings of the European Conference on Machine Learning, Catania, Italy."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.neucom.2015.06.071","article-title":"Improving classification of epileptic and non-epileptic EEG events by feature selection","volume":"171","author":"Pippa","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cao, H., Nguyen, M.N., Phua, C., Krishnaswamy, S., and Li, X.L. (2012, January 5\u20138). An integrated framework for human activity classification. Proceedings of the ACM International Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370268"},{"key":"ref_46","unstructured":"Dewancker, I., McCourt, M., Clark, S., Hayes, P., Johnson, A., and Ke, G. (arXiv, 2016). A Stratified Analysis of Bayesian Optimization Methods, arXiv."},{"key":"ref_47","unstructured":"Snoek, J., Larochelle, H., and Adams, R.P. (2012, January 3\u20138). Practical bayesian optimization of machine learning algorithms. Proceedings of the 25th International Conference on Neural Information Processing Systems, Harrahs and Harveys, Lake Tahoe, CA, USA."},{"key":"ref_48","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","volume":"16","author":"Abadi","year":"2016","journal-title":"OSDI"},{"key":"ref_49","unstructured":"(2019, February 19). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"M146","DOI":"10.1093\/gerona\/56.3.M146","article-title":"Frailty in older adults: Evidence for a phenotype","volume":"56","author":"Fried","year":"2001","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med. Sci."},{"key":"ref_51","unstructured":"Zheng, W.-L., and Lu, B.-L. (2016, January 9\u201315). Personalizing EEG-based affective models with transfer learning. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ali, H., Messina, E., and Bisiani, R. (2013, January 20\u201322). Subject-dependent physical activity recognition model framework with a semi-supervised clustering approach. Proceedings of the IEEE European Modelling Symposium (EMS), Manchester, UK.","DOI":"10.1109\/EMS.2013.7"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/880\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:33:29Z","timestamp":1760186009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,20]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19040880"],"URL":"https:\/\/doi.org\/10.3390\/s19040880","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,20]]}}}