Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor
Open accessPublished version of a preprint
Abstract
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’s 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’s 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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 1× · see citing works
“The DeepConvLSTM architecture combines a convolutional neural network with a long short-term memory recurrent network (LSTM) and has been widely applied in the past [27].”
MethodSensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730 · see citing works
Window Size Impact in Human Activity Recognition
Baños, O., Gálvez, J. M., Damas, M., Pomares, H., & Rojas, I. (2014) · Sensors · doi:10.3390/s140406474 · Cited by 656 · see citing works
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Zhang, M., & Sawchuk, A. A. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing · doi:10.1145/2370216.2370438 · Cited by 554 · see citing works
Winters, J. M., & Woo, S. L. (1990) · Springer eBooks · doi:10.1007/978-1-4613-9030-5 · Cited by 492 · see citing works
Wearable sensors for monitoring the internal and external workload of the athlete
Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0149-2 · Cited by 378 · see citing works
Wang, Z., Yang, Z., & Dong, T. (2017) · Sensors · doi:10.3390/s17020341 · Cited by 357 · see citing works
Sensor Positioning for Activity Recognition Using Wearable Accelerometers
Atallah, L., Lo, B., King, R., & Yang, G.-Z. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2160540 · Cited by 346 · see citing works
Long-term unsupervised mobility assessment in movement disorders
Warmerdam, E., Hausdorff, J. M., Atrsaei, A., Zhou, Y., Mirelman, A., Aminian, K., Espay, A. J., Hansen, C., Evers, L. J. W., Keller, A., et al. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30397-7 · Cited by 306 · see citing works
Gait analysis with wearables predicts conversion to Parkinson disease
Din, S. D., Elshehabi, M., Galna, B., Hobert, M. A., Warmerdam, E., Suenkel, U., Brockmann, K., Metzger, F. G., Hansen, C., Berg, D., et al. (2019) · Annals of Neurology · doi:10.1002/ana.25548 · Cited by 211 · see citing works
Capela, N. A., Lemaire, E. D., & Baddour, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124414 · Cited by 197 · see citing works
Human Activity Recognition: Review, Taxonomy and Open Challenges
Arshad, M. H., Bilal, M., & Gani, A. (2022) · Sensors · doi:10.3390/s22176463 · Cited by 162 · see citing works
Exploring patterns of daily physical and sedentary behaviour in community-dwelling older adults
Lord, S., CHASTIN, S. F. M., McInnes, L., Little, L., Briggs, P., & Rochester, L. (2011) · Age and Ageing · doi:10.1093/ageing/afq166 · Cited by 146 · see citing works
Lugade, V. A., Fortune, E., Morrow, M. M. B., & Kaufman, K. R. (2013) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2013.06.005 · Cited by 143 · see citing works
Activity-Aware Mental Stress Detection Using Physiological Sensors
Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., & Griss, M. (2012) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-29336-8_16 · Cited by 134 · see citing works
Coordination of head and trunk accelerations during walking
Kavanagh, J. J., Morrison, S., & Barrett, R. (2005) · European Journal of Applied Physiology · doi:10.1007/s00421-005-1328-1 · Cited by 132 · see citing works
Hegde, N., Bries, M., Swibas, T., Melanson, E., & Sazonov, E. E. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2734803 · Cited by 108 · see citing works
Galperin, I., Hillel, I., Din, S. D., Bekkers, E. M. J., Nieuwboer, A., Abbruzzese, G., Avanzino, L., Nieuwhof, F., Bloem, B. R., Rochester, L., et al. (2019) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2019.01.022 · Cited by 96 · see citing works
Human Activity Recognition Using an Ensemble Learning Algorithm with Smartphone Sensor Data
Tan, T.-H., Wu, J., Liu, S.-H., & Gochoo, M. (2022) · Electronics · doi:10.3390/electronics11030322 · Cited by 94 · see citing works
Real-life gait assessment in degenerative cerebellar ataxia
Ilg, W., Seemann, J., Giese, M. A., Traschütz, A., Schöls, L. J., Timmann, D., & Synofzik, M. (2020) · Neurology · doi:10.1212/wnl.0000000000010176 · Cited by 91 · see citing works
Control of Balance of Upper Body During Gait
Winter, D. A., Ruder, G. K., & MacKinnon, C. D. (1990) · Springer eBooks · doi:10.1007/978-1-4613-9030-5_33 · Cited by 66 · see citing works
Rehman, R. Z. U., Zhou, Y., Din, S. D., Alcock, L., Hansen, C., Guan, Y., Hortobágyi, T., Maetzler, W., Rochester, L., & Lamoth, C. J. C. (2020) · Sensors · doi:10.3390/s20236992 · Cited by 61 · see citing works
Schniepp, R., Huppert, A., Decker, J., Schenkel, F., Schlick, C., Rasoul, A., Dieterich, M., Brandt, T., Jahn, K., & Wuehr, M. (2021) · Journal of Neurology · doi:10.1007/s00415-021-10504-x · Cited by 61 · see citing works
Self-citationPosture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform
Sazonov, E. E., Hegde, N., Browning, R. C., Melanson, E. L., & Sazonova, N. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2432454 · Cited by 58 · see citing works
Exploring audio and kinetic sensing on earable devices
Min, C., Mathur, A., & Kawsar, F. (2018) · doi:10.1145/3211960.3211970 · Cited by 56 · see citing works
Soltani, A., Aminian, K., Mazzà, C., Cereatti, A., Palmerini, L., Bonci, T., & Paraschiv-Ionescu, A. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3111681 · Cited by 48 · see citing works
A human activity recognition method using wearable sensors based on convtransformer model
Zhang, Z., Wang, W., An, A., Qin, Y., & Yang, F. (2023) · Evolving Systems · doi:10.1007/s12530-022-09480-y · Cited by 44 · see citing works
Liu, H., & Schultz, T. (2022) · International Conference on Health Informatics · doi:10.5220/0010896400003123 · Cited by 33 · see citing works
Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses
Starliper, N., Mohammadzadeh, F., Songkakul, T., Hernandez, M. L., Bozkurt, A. Y., & Lobaton, E. J. (2019) · Sensors · doi:10.3390/s19030441 · Cited by 26 · see citing works
EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors
Seifer, A.-K., Dorschky, E., Küderle, A., Moradi, H., Hannemann, R., & Eskofier, B. M. (2023) · Sensors · doi:10.3390/s23146565 · Cited by 24 · see citing works
Independent domains of daily mobility in patients with neurological gait disorders
Wuehr, M., Huppert, A., Schenkel, F., Decker, J., Jahn, K., & Schniepp, R. (2020) · Journal of Neurology · doi:10.1007/s00415-020-09893-2 · Cited by 18 · see citing works
Self-citationMultimodal Mobility Assessment Predicts Fall Frequency and Severity in Cerebellar Ataxia
Schniepp, R., Huppert, A., Decker, J., Schenkel, F., Dieterich, M., Brandt, T., & Wuehr, M. (2022) · The Cerebellum · doi:10.1007/s12311-021-01365-1 · Cited by 11 · see citing works
Self-citationActivity-Aware Vital Sign Monitoring Based on a Multi-Agent Architecture
Ivaşcu, T., & Negru, V. (2021) · Sensors · doi:10.3390/s21124181 · Cited by 10 · see citing works
Activity-Aware Physiological Response Prediction Using Wearable Sensors
Lokare, N., Zhong, B., & Lobaton, E. J. (2017) · Inventions · doi:10.3390/inventions2040032 · Cited by 9 · see citing works
Adaptable Action-Aware Vital Models for Personalized Intelligent Patient Monitoring
Wu, K., Chen, E. H., Xing, H., Wirth, F., Vitanova, K., Lange, R., & Burschka, D. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812176 · Cited by 6 · see citing works
Proceedings of the 6th ACM Workshop on Wearable Systems and Applications
Tran, V., MobiSys '20: The 18th Annual International Conference on Mobile Systems, A., Association for Computing Machinery-Digital Library, ACM Special Interest Group on Mobility of Systems, U., ACM Special Interest Group on Operating Systems, & SIGs, A. (2020) · doi:10.1145/3396870 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
NeuroHAR: A Neuroevolutionary Method for Human Activity Recognition (HAR) for Health Monitoring
Alam, F., Pławiak, P., Almaghthawi, A., Qazani, M. R. C., Mohanty, S., & Alizadehsani, R. (2024) · IEEE Access · doi:10.1109/access.2024.3441108 · Cited by 15 · see citing works
Machine learning models for wearable-based human activity recognition: A comparative study
Ciortuz, G., Pour, H. H., Irshad, M. T., Nisar, M. A., Huang, X., & Fudickar, S. J. F. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130911 · Cited by 14 · see citing works
Mobile Spatiotemporal Gait Segmentation Using an Ear-Worn Motion Sensor and Deep Learning
Decker, J., Boborzi, L., Schniepp, R., Jahn, K., & Wuehr, M. (2024) · Sensors · doi:10.3390/s24196442 · Cited by 11 · see citing works
Self-citationKollmansperger, S., Decker, J., Berkes, S., Jahn, K., & Wuehr, M. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1502204 · Cited by 3 · see citing works
Self-citationDjaja-Jośko, V., Kołakowski, M., Cichocki, J., & Kołakowski, J. (2025) · Sensors · doi:10.3390/s25041151 · Cited by 2 · see citing works
Human Motion Posture Recognition based on Spatiotemporal Graph CNN
Li, Y., & Jing, W. (2025) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis65355.2025.11070350 · Cited by 1 · see citing works
Harnessing Quantum Attention: A Hybrid Deep Network for Wearable Sensor-based Activity Recognition
Endla, P., Asha, S., Sallaram, S., Thatipudi, J. G., Tejasri, V., & S.Gopi (2025) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc65190.2025.11140878 · Cited by 1 · see citing works
An Efficient Model for Identifying Human Behaviour using Machine Learning
Bansal, N., Bansal, A., & Gupta, M. (2024) · International Conference on Communication, Control and Intelligent Systems (CCIS) · doi:10.1109/ccis63231.2024.10932002
Seifer, A.-K., Jahnel, L., Küderle, A., Hannemann, R., & Eskofier, B. M. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254014
Human Activity Recognition using Hearing Aids
Sloan, W. (2026) · doi:10.22215/etd/2026-17117
- Authors
- Boborzi, L., Decker, J., Rezaei, R., Schniepp, R., & Wuehr, M.
- Affiliations
- Deutsches Schwindel- und Gleichgewichtszentrum (DE)
- Institut für Notfallmedizin und Medizinmanagement (DE)
- Corresponding author
- Wuehr, Max
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2024-04-23
- Volume
- 24
- Issue
- 9
- Pages
- 2665
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC11085719
- OpenAlex
- W4395048752
- Cited by
- 9
- References
- 37
- Citations by year
- 2025: 5 · 2024: 4
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- German Federal Ministry for Education and Science (01EO1401, 13GW0490B)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- human activity recognition, wearable sensors, stair climbing, running, remote patient monitoring, health monitoring
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Deep Learning, Activities of Daily Living, Adult, Algorithms, Ear, Female, Humans, Human Activities, Male, Middle Aged, Monitoring, Physiologic, Motion, Walking, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
