{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:33:11Z","timestamp":1768807991674,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["HANC ANR-15-CE36-0005"],"award-info":[{"award-number":["HANC ANR-15-CE36-0005"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004795","name":"Institut Universitaire de France","doi-asserted-by":"publisher","award":["2019"],"award-info":[{"award-number":["2019"]}],"id":[{"id":"10.13039\/501100004795","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004794","name":"Centre National de la Recherche Scientifique","doi-asserted-by":"publisher","award":["D\u00e9fi S2C3"],"award-info":[{"award-number":["D\u00e9fi S2C3"]}],"id":[{"id":"10.13039\/501100004794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013413","name":"Conseil R\u00e9gional d'Alsace","doi-asserted-by":"publisher","award":["2016"],"award-info":[{"award-number":["2016"]}],"id":[{"id":"10.13039\/501100013413","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Here we propose a new machine learning algorithm for classification of human activities by means of accelerometer and gyroscope signals. Based on a novel hierarchical system of logistic regression classifiers and a relatively small set of features extracted from the filtered signals, the proposed algorithm outperformed previous work on the DaLiAc (Daily Life Activity) and mHealth datasets. The algorithm also represents a significant improvement in terms of computational costs and requires no feature selection and hyper-parameter tuning. The algorithm still showed a robust performance with only two (ankle and wrist) out of the four devices (chest, wrist, hip and ankle) placed on the body (96.8% vs. 97.3% mean accuracy for the DaLiAc dataset). The present work shows that low-complexity models can compete with heavy, inefficient models in classification of advanced activities when designed with a careful upstream inspection of the data.<\/jats:p>","DOI":"10.3390\/s20113090","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"3090","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors"],"prefix":"10.3390","volume":"20","author":[{"given":"Isaac","family":"Debache","sequence":"first","affiliation":[{"name":"Institut Pluridisciplinaire Hubert Curien (IPHC) UMR 7178 Centre National de la Recherche Scientifique (CNRS), Universit\u00e9 de Strasbourg, 67000 Strasbourg, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7317-3154","authenticated-orcid":false,"given":"Lor\u00e8ne","family":"Jeantet","sequence":"additional","affiliation":[{"name":"Institut Pluridisciplinaire Hubert Curien (IPHC) UMR 7178 Centre National de la Recherche Scientifique (CNRS), Universit\u00e9 de Strasbourg, 67000 Strasbourg, France"}]},{"given":"Damien","family":"Chevallier","sequence":"additional","affiliation":[{"name":"Institut Pluridisciplinaire Hubert Curien (IPHC) UMR 7178 Centre National de la Recherche Scientifique (CNRS), Universit\u00e9 de Strasbourg, 67000 Strasbourg, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1266-5144","authenticated-orcid":false,"given":"Audrey","family":"Bergouignan","sequence":"additional","affiliation":[{"name":"Institut Pluridisciplinaire Hubert Curien (IPHC) UMR 7178 Centre National de la Recherche Scientifique (CNRS), Universit\u00e9 de Strasbourg, 67000 Strasbourg, France"},{"name":"Division of Endocrinology, Metabolism, and Diabetes and Anschutz Health and Wellness Center, University of Colorado, School of Medicine, Aurora, CO 80045, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8206-2739","authenticated-orcid":false,"given":"C\u00e9dric","family":"Sueur","sequence":"additional","affiliation":[{"name":"Institut Pluridisciplinaire Hubert Curien (IPHC) UMR 7178 Centre National de la Recherche Scientifique (CNRS), Universit\u00e9 de Strasbourg, 67000 Strasbourg, France"},{"name":"Institut Universitaire de France, Saint-Michel 103, 75005 Paris, France"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1093\/eurheartj\/ehq451","article-title":"Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003\u201306","volume":"32","author":"Healy","year":"2011","journal-title":"Eur. Heart J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.gaitpost.2008.01.003","article-title":"Comparison of low-complexity fall detection algorithms for body attached accelerometers","volume":"28","author":"Kangas","year":"2008","journal-title":"Gait Posture"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"17037","DOI":"10.3390\/s140917037","article-title":"Gait Characteristic Analysis and Identification Based on the iPhone\u2019s Accelerometer and Gyrometer","volume":"14","author":"Sun","year":"2014","journal-title":"Sensors"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1152\/japplphysiol.00556.2017","article-title":"Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry","volume":"124","author":"Garnotel","year":"2017","journal-title":"J. Appl. Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Awais, M., Mellone, S., and Chiari, L. (2015, January 25\u201329). Physical activity classification meets daily life: Review on existing methodologies and open challenges. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319526"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00779-010-0293-9","article-title":"Preprocessing Techniques for Context Recognition from Accelerometer Data","volume":"14","author":"Figo","year":"2010","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s40279-017-0716-0","article-title":"Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations","volume":"47","author":"Migueles","year":"2017","journal-title":"Sports Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bai, J., Di, C., Xiao, L., Evenson, K.R., LaCroix, A.Z., Crainiceanu, C.M., and Buchner, D.M. (2016). An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0160644"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"S490","DOI":"10.1249\/01.mss.0000185571.49104.82","article-title":"The Technology of Accelerometry-Based Activity Monitors: Current and Future","volume":"37","author":"Chen","year":"2005","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ferscha, A., and Mattern, F. (2004). Activity Recognition from User-Annotated Acceleration Data. Pervasive Computing, Springer.","DOI":"10.1007\/b96922"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","unstructured":"Hammerla, N.Y., Halloran, S., and Pl\u00f6tz, T. (2016). Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hur, T., Bang, J., Huynh-The, T., Lee, J., Kim, J.-I., and Lee, S. (2018). Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition. Sensors, 18.","DOI":"10.3390\/s18113910"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huynh-The, T., Hua, C.-H., and Kim, D.-S. (2019, January 23\u201327). Visualizing Inertial Data For Wearable Sensor Based Daily Life Activity Recognition Using Convolutional Neural Network *. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857366"},{"key":"ref_17","unstructured":"Chollet, F. (2017). Deep Learning with Python, Manning Publications. [1st ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5262","DOI":"10.1109\/ACCESS.2017.2684913","article-title":"Improving Activity Recognition Accuracy in Ambient Assisted Living Systems by Automated Feature Engineering","volume":"5","author":"Zdravevski","year":"2017","journal-title":"IEEE Access"},{"key":"ref_19","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Leutheuser, H., Schuldhaus, D., and Eskofier, B.M. (2013). Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0075196"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s00500-012-0896-3","article-title":"Human activity recognition based on a sensor weighting hierarchical classifier","volume":"17","author":"Banos","year":"2013","journal-title":"Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, S., Mccullagh, P., Nugent, C., and Zheng, H. (2010, January 19\u201321). Activity Monitoring Using a Smart Phone\u2019s Accelerometer with Hierarchical Classification. Proceedings of the 2010 Sixth International Conference on Intelligent Environments, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IE.2010.36"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pecchia, L., Chen, L.L., Nugent, C., and Bravo, J. (2014). mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications. Ambient Assisted Living and Daily Activities, Springer International Publishing.","DOI":"10.1007\/978-3-319-13105-4"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Van Hees, V.T., Gorzelniak, L., Le\u00f3n, E.C.D., Eder, M., Pias, M., Taherian, S., Ekelund, U., Renstr\u00f6m, F., Franks, P.W., and Horsch, A. (2013). Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0061691"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window Size Impact in Human Activity Recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_26","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, Y., Guo, M., and Wang, Z. (2016, January 14\u201316). An improved algorithm for human activity recognition using wearable sensors. Proceedings of the 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), Chiang Mai, Thailand.","DOI":"10.1109\/ICACI.2016.7449833"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/JBHI.2015.2458274","article-title":"Human Activity Recognition by Combining a Small Number of Classifiers","volume":"20","author":"Ghahramani","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jurca, R., Cioara, T., Anghel, I., Antal, M., Pop, C., and Moldovan, D. (2018, January 6\u20138). Activities of Daily Living Classification using Recurrent Neural Networks. Proceedings of the 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet), Cluj-Napoca, Romania.","DOI":"10.1109\/ROEDUNET.2018.8514124"},{"key":"ref_31","first-page":"1387","article-title":"Novel approaches to human activity recognition based on accelerometer data","volume":"12","author":"Jordao","year":"2018","journal-title":"SIVP"},{"key":"ref_32","first-page":"2079","article-title":"On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Martindale, C.F., Sprager, S., and Eskofier, B.M. (2019). Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables. Sensors, 19.","DOI":"10.3390\/s19081820"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Konstan, J.A., Conejo, R., Marzo, J.L., and Oliver, N. (2011). A Dynamic Sliding Window Approach for Activity Recognition. User Modeling, Adaption and Personalization, Springer.","DOI":"10.1007\/978-3-642-22362-4"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3090\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:54Z","timestamp":1760175234000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,29]]},"references-count":34,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20113090"],"URL":"https:\/\/doi.org\/10.3390\/s20113090","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,29]]}}}