{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T02:55:32Z","timestamp":1785639332019,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry for Education and Science","award":["01EO1401"],"award-info":[{"award-number":["01EO1401"]}]},{"name":"German Federal Ministry for Education and Science","award":["13GW0490B"],"award-info":[{"award-number":["13GW0490B"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting\/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor\u2019s efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear\u2019s efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.<\/jats:p>","DOI":"10.3390\/s24092665","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T05:28:06Z","timestamp":1713850086000},"page":"2665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor"],"prefix":"10.3390","volume":"24","author":[{"given":"Lukas","family":"Boborzi","sequence":"first","affiliation":[{"name":"German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Decker","sequence":"additional","affiliation":[{"name":"German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Razieh","family":"Rezaei","sequence":"additional","affiliation":[{"name":"German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman","family":"Schniepp","sequence":"additional","affiliation":[{"name":"Institute for Emergency Medicine and Medical Management, Ludwig-Maximilians-University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5115-3777","authenticated-orcid":false,"given":"Max","family":"Wuehr","sequence":"additional","affiliation":[{"name":"German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany"},{"name":"Department of Neurology, Ludwig-Maximilians-University of Munich, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Arshad, M.H., Bilal, M., and Gani, A. (2022). Human Activity Recognition: Review, Taxonomy and Open Challenges. Sensors, 22.","DOI":"10.3390\/s22176463"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107561","DOI":"10.1016\/j.patcog.2020.107561","article-title":"Sensor-based and vision-based human activity recognition: A comprehensive survey","volume":"108","author":"Min","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/S1474-4422(19)30397-7","article-title":"Long-term unsupervised mobility assessment in movement disorders","volume":"19","author":"Warmerdam","year":"2020","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1007\/s00415-021-10504-x","article-title":"Fall prediction in neurological gait disorders: Differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring","volume":"268","author":"Schniepp","year":"2021","journal-title":"J. Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e1199","DOI":"10.1212\/WNL.0000000000010176","article-title":"Real-life gait assessment in degenerative cerebellar ataxia: Toward ecologically valid biomarkers","volume":"95","author":"Ilg","year":"2020","journal-title":"Neurology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1002\/ana.25548","article-title":"Gait analysis with wearables predicts conversion to parkinson disease","volume":"86","author":"Elshehabi","year":"2019","journal-title":"Ann. Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rehman, R.Z.U., Zhou, Y., Del Din, S., Alcock, L., Hansen, C., Guan, Y., Hortobagyi, T., Maetzler, W., Rochester, L., and Lamoth, C.J.C. (2020). Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. Sensors, 20.","DOI":"10.3390\/s20236992"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s00415-020-09893-2","article-title":"Independent domains of daily mobility in patients with neurological gait disorders","volume":"267","author":"Wuehr","year":"2020","journal-title":"J. Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s12311-021-01365-1","article-title":"Multimodal Mobility Assessment Predicts Fall Frequency and Severity in Cerebellar Ataxia","volume":"22","author":"Schniepp","year":"2023","journal-title":"Cerebellum"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.parkreldis.2019.01.022","article-title":"Associations between daily-living physical activity and laboratory-based assessments of motor severity in patients with falls and Parkinson\u2019s disease","volume":"62","author":"Galperin","year":"2019","journal-title":"Park. Relat. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1093\/ageing\/afq166","article-title":"Exploring patterns of daily physical and sedentary behaviour in community-dwelling older adults","volume":"40","author":"Lord","year":"2011","journal-title":"Age Ageing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1109\/JBHI.2015.2432454","article-title":"Posture and activity recognition and energy expenditure estimation in a wearable platform","volume":"19","author":"Sazonov","year":"2015","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/JBHI.2017.2734803","article-title":"Automatic Recognition of Activities of Daily Living Utilizing Insole-Based and Wrist-Worn Wearable Sensors","volume":"22","author":"Hegde","year":"2018","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Min, C., Mathur, A., and Kawsar, F. (2018, January 10). Exploring audio and kinetic sensing on earable devices. Proceedings of the 4th ACM Workshop on Wearable Systems and Applications, Munich, Germany.","DOI":"10.1145\/3211960.3211970"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s00421-005-1328-1","article-title":"Coordination of head and trunk accelerations during walking","volume":"94","author":"Kavanagh","year":"2005","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Winters, J.M., and Woo, S.L.Y. (1990). Multiple Muscle Systems: Biomechanics and Movement Organization, Springer.","DOI":"10.1007\/978-1-4613-9030-5"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Seifer, A.-K., Dorschky, E., K\u00fcderle, A., Moradi, H., Hannemann, R., and Eskofier, B.M. (2023). EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors. Sensors, 23.","DOI":"10.3390\/s23146565"},{"key":"ref_19","first-page":"1","article-title":"Sensing with Earables","volume":"6","author":"Clarke","year":"2022","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_21","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_22","first-page":"680","article-title":"How Long Are Various Types of Daily Activities? Statistical Analysis of a Multimodal Wearable Sensor-based Human Activity Dataset","volume":"2022","author":"Liu","year":"2022","journal-title":"Healthinf"},{"key":"ref_23","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2012). Ambient Assisted Living and Home Care, Proceedings of the 4th International Workshop, IWAAL 2012, Vitoria-Gasteiz, Spain, 3\u20135 December 2012, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1109\/TNSRE.2021.3111681","article-title":"Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges","volume":"29","author":"Soltani","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tan, T.-H., Wu, J.-Y., Liu, S.-H., and Gochoo, M. (2022). Human Activity Recognition Using an Ensemble Learning Algorithm with Smartphone Sensor Data. Electronics, 11.","DOI":"10.3390\/electronics11030322"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Capela, N.A., Lemaire, E.D., and Baddour, N. (2015). Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0124414"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ordonez, F.J., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1007\/s12530-022-09480-y","article-title":"A human activity recognition method using wearable sensors based on convtransformer model","volume":"14","author":"Zhang","year":"2023","journal-title":"Evol. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.medengphy.2013.06.005","article-title":"Validity of using tri-axial accelerometers to measure human movement\u2014Part I: Posture and movement detection","volume":"36","author":"Lugade","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ivascu, T., and Negru, V. (2021). Activity-Aware Vital SignMonitoring Based on a Multi-Agent Architecture. Sensors, 21.","DOI":"10.3390\/s21124181"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, Z., and Dong, T. (2017). A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time. Sensors, 17.","DOI":"10.3390\/s17020341"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Starliper, N., Mohammadzadeh, F., Songkakul, T., Hernandez, M., Bozkurt, A., and Lobaton, E. (2019). Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses. Sensors, 19.","DOI":"10.3390\/s19030441"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, K., Chen, E.H., Hao, X., Wirth, F., Vitanova, K., Lange, R., and Burschka, D. (2022, January 23\u201327). Adaptable Action-Aware Vital Models for Personalized Intelligent Patient Monitoring. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812176"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lokare, N., Zhong, B., and Lobaton, E. (2017). Activity-Aware Physiological Response Prediction Using Wearable Sensors. Inventions, 2.","DOI":"10.3390\/inventions2040032"},{"key":"ref_35","unstructured":"Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., and Griss, M. (2012). Mobile Computing, Applications, and Services, Proceedings of the Second International ICST Conference, MobiCASE 2010, Santa Clara, CA, USA, 25\u201328 October 2010, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s41746-019-0149-2","article-title":"Wearable sensors for monitoring the internal and external workload of the athlete","volume":"2","author":"Seshadri","year":"2019","journal-title":"NPJ Digit. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2665\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:32:28Z","timestamp":1760106748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":36,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092665"],"URL":"https:\/\/doi.org\/10.3390\/s24092665","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,23]]}}}