{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T21:15:33Z","timestamp":1789161333071,"version":"build-2803163510"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/X036146\/1"],"award-info":[{"award-number":["EP\/X036146\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovative Medicines Initiative 2 Joint Undertaking","award":["853981"],"award-info":[{"award-number":["853981"]}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/X031012\/1"],"award-info":[{"award-number":["EP\/X031012\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition (HAR) lies at the core of digital healthcare applications that monitor different types of physical activity. Traditional HAR methods often struggle to adapt to variable-length, real-world activity data and to generalise across cohorts (e.g., from young to old cohorts). Thus, the aim of this study was to investigate HAR using wearable sensor data, with a particular focus on cross-cohort evaluation. Each dataset included two accelerometers (right thigh and lower back) sampling at 50 Hz, capturing a range of daily-life activities that were annotated using video recordings from chest-mounted cameras synchronised with the accelerometers. Neural networks were trained on young cohorts\u2019 data and tested on old cohorts\u2019 data. The effects of network architecture, sampling frequency and sensor location on classification performance were investigated. Network performance was evaluated using accuracy, recall, precision, F1-score and confusion matrices. The gated recurrent unit architecture achieved the best performance when trained solely on young cohorts\u2019 data, with weighted F1-score of 0.95 \u00b1 0.05 and 0.93 \u00b1 0.05 for young and old cohorts, respectively, resulting in a highly generalizable method. Classification performance across multiple sampling frequencies was comparable. The thigh-mounted sensor consistently achieved higher performance than the lower back sensor across activities except lying. Furthermore, combining datasets significantly improved performance on the old cohort (weighted F1-score: 0.97 \u00b1 0.02) due to increased variability in the training data. This study highlights the importance of network architecture and dataset composition in HAR and demonstrates the potential of neural networks for robust, real-world activity recognition across age-defined cohorts, specifically between young and old cohorts.<\/jats:p>","DOI":"10.3390\/s26041320","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T15:28:18Z","timestamp":1771428498000},"page":"1320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neural Network-Based Granular Activity Recognition from Accelerometers: Assessing Generalizability Across Diverse Mobility Profiles"],"prefix":"10.3390","volume":"26","author":[{"given":"Metin","family":"Bicer","sequence":"first","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"},{"name":"NIHR Newcastle Biomedical Research Centre, Newcastle University and the Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE4 5PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Pope","sequence":"additional","affiliation":[{"name":"School of Engineering Mathematics and Technology, University of Bristol, Bristol BS8 1QU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lynn","family":"Rochester","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"},{"name":"NIHR Newcastle Biomedical Research Centre, Newcastle University and the Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE4 5PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1154-4751","authenticated-orcid":false,"given":"Silvia","family":"Del Din","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"},{"name":"NIHR Newcastle Biomedical Research Centre, Newcastle University and the Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE4 5PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8364-9803","authenticated-orcid":false,"given":"Lisa","family":"Alcock","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"},{"name":"NIHR Newcastle Biomedical Research Centre, Newcastle University and the Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE4 5PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tapia, E.M., Intille, S.S., and Larson, K. (2004, January 21\u201323). Activity recognition in the home using simple and ubiquitous sensors. Proceedings of the International Conference on Pervasive Computing, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_10"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tezcan, \u00d6., Akcay, C., Sari, M., and Cavus, M. (2025). Sensor-Based Automatic Recognition of Construction Worker Activities Using Deep Learning Network. Sensors, 25.","DOI":"10.3390\/s25133988"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1159\/000512513","article-title":"A roadmap to inform development, validation and approval of digital mobility outcomes: The Mobilise-D approach","volume":"4","author":"Rochester","year":"2020","journal-title":"Digit. Biomark."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Viceconti, M., Tome, M., Dartee, W., Knezevic, I., Hernandez Penna, S., Mazz\u00e0, C., Caulfield, B., Garcia-Aymerich, J., Becker, C., and Maetzler, W. (2022). On the use of wearable sensors as mobility biomarkers in the marketing authorization of new drugs: A regulatory perspective. Front. Med., 9.","DOI":"10.3389\/fmed.2022.996903"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s12984-023-01198-5","article-title":"Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium","volume":"20","author":"Bonci","year":"2023","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_6","first-page":"1","article-title":"Locomotion and cadence detection using a single trunk-fixed accelerometer: Validity for children with cerebral palsy in daily life-like conditions","volume":"16","author":"Newman","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abdul Jabbar, K., Sarvestan, J., Zia Ur Rehman, R., Lord, S., Kerse, N., Teh, R., and Del Din, S. (2023). Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults. Sensors, 23.","DOI":"10.3390\/s23104605"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.medengphy.2018.04.008","article-title":"Detection of daily postures and walking modalities using a single chest-mounted tri-axial accelerometer","volume":"57","author":"Nazarahari","year":"2018","journal-title":"Med. Eng. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.gaitpost.2016.07.328","article-title":"A multi-resolution investigation for postural transition detection and quantification using a single wearable","volume":"49","author":"Hickey","year":"2016","journal-title":"Gait Posture"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.medengphy.2011.05.002","article-title":"Activity classification using a single chest mounted tri-axial accelerometer","volume":"33","author":"Godfrey","year":"2011","journal-title":"Med. Eng. Phys."},{"key":"ref_11","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_12","first-page":"1","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"Chen","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s10618-023-00948-2","article-title":"Improving position encoding of transformers for multivariate time series classification","volume":"38","author":"Foumani","year":"2023","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, 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_15","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1109\/JIOT.2019.2949715","article-title":"A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention","volume":"7","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nafea, O., Abdul, W., Muhammad, G., and Alsulaiman, M. (2021). Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning. Sensors, 21.","DOI":"10.3390\/s21062141"},{"key":"ref_17","first-page":"1","article-title":"Deformable Convolutional Networks for Multimodal Human Activity Recognition Using Wearable Sensors","volume":"71","author":"Xu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes. Sensors, 21.","DOI":"10.3390\/s21051636"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7316954","DOI":"10.1155\/2018\/7316954","article-title":"Deep residual bidir-LSTM for human activity recognition using wearable sensors","volume":"2018","author":"Zhao","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9893","DOI":"10.1109\/ACCESS.2018.2890675","article-title":"InnoHAR: A Deep Neural Network for Complex Human Activity Recognition","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., Phaphan, W., and Jitpattanakul, A. (2024, January 10\u201312). Harnessing Deep Learning for Activity Recognition in Seniors\u2019 Daily Routines with Wearable Sensors. Proceedings of the 2024 47th International Conference on Telecommunications and Signal Processing (TSP), Prague, Czech Republic.","DOI":"10.1109\/TSP63128.2024.10605946"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nair, N., Thomas, C., and Jayagopi, D.B. (2018, January 20\u201321). Human activity recognition using temporal convolutional network. Proceedings of the 5th International Workshop on Sensor-Based Activity Recognition and Interaction, Berlin, Germany.","DOI":"10.1145\/3266157.3266221"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107671","DOI":"10.1016\/j.asoc.2021.107671","article-title":"Attention induced multi-head convolutional neural network for human activity recognition","volume":"110","author":"Khan","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15310","DOI":"10.1038\/s41598-024-65850-3","article-title":"A human activity recognition method based on Vision Transformer","volume":"14","author":"Han","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TCE.2023.3266506","article-title":"A human activity recognition method based on lightweight feature extraction combined with pruned and quantized CNN for wearable device","volume":"69","author":"Yi","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"56855","DOI":"10.1109\/ACCESS.2020.2982225","article-title":"LSTM-CNN architecture for human activity recognition","volume":"8","author":"Xia","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.patcog.2017.12.024","article-title":"Efficient dense labelling of human activity sequences from wearables using fully convolutional networks","volume":"78","author":"Yao","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Thu, N.T.H., and Han, D.S. (2024, January 5\u20138). Handle Dense Labeling in Human Activity Recognition Using Self Attention and BiLSTM. Proceedings of the 2024 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE59016.2024.10444332"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106529","DOI":"10.1016\/j.engappai.2023.106529","article-title":"A new CNN-LSTM architecture for activity recognition employing wearable motion sensor data: Enabling diverse feature extraction","volume":"124","author":"Barshan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_30","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_31","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the ESANN, Bruges, Belgium."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a new benchmarked dataset for activity monitoring. Proceedings of the 2012 16th International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., and Havinga, P.J. (2016). Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors, 16.","DOI":"10.3390\/s16040426"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ustad, A., Logacjov, A., Trolleb\u00f8, S.\u00d8., Thingstad, P., Vereijken, B., Bach, K., and Maroni, N.S. (2023). Validation of an activity type recognition model classifying daily physical behavior in older adults: The HAR70+ model. Sensors, 23.","DOI":"10.3390\/s23052368"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Logacjov, A., Bach, K., Kongsvold, A., B\u00e5rdstu, H.B., and Mork, P.J. (2021). HARTH: A human activity recognition dataset for machine learning. Sensors, 21.","DOI":"10.3390\/s21237853"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1016\/j.jbiomech.2006.11.001","article-title":"Balance control during stair negotiation in older adults","volume":"40","author":"Lee","year":"2007","journal-title":"J. Biomech."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1111\/j.1532-5415.2004.52162.x","article-title":"Stair performance in people aged 75 and older","volume":"52","author":"Hamel","year":"2004","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Elshehabi, M., Del Din, S., Hobert, M.A., Warmerdam, E., S\u00fcnkel, U., Schmitz-H\u00fcbsch, T., Behncke, L.M., Heinzel, S., Brockmann, K., and Metzger, F.G. (2022). Walking parameters of older adults from a lower back inertial measurement unit, a 6-year longitudinal observational study. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.789220"},{"key":"ref_39","unstructured":"Junker, H., Lukowicz, P., and Troster, G. (November, January 31). Sampling frequency, signal resolution and the accuracy of wearable context recognition systems. Proceedings of the Eighth International Symposium on Wearable Computers, Arlington, VA, USA."},{"key":"ref_40","unstructured":"Yang, X., Dinh, A., and Chen, L. (2010, January 16\u201318). Implementation of a wearerable real-time system for physical activity recognition based on Naive Bayes classifier. Proceedings of the 2010 International Conference on Bioinformatics and Biomedical Technology, Chengdu, China."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"42565","DOI":"10.1109\/ACCESS.2019.2905575","article-title":"Segmentation and recognition of basic and transitional activities for continuous physical human activity","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/THMS.2015.2489688","article-title":"Toward personalized activity recognition systems with a semipopulation approach","volume":"46","author":"Hong","year":"2015","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1016\/j.patcog.2014.11.014","article-title":"A novel ensemble method for classifying imbalanced data","volume":"48","author":"Sun","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"25060","DOI":"10.1109\/JSEN.2024.3414168","article-title":"CSI-based human activity recognition via lightweight CNN model and data augmentation","volume":"24","author":"Amoud","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111301","DOI":"10.1016\/j.jbiomech.2022.111301","article-title":"Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets","volume":"144","author":"Bicer","year":"2022","journal-title":"J. Biomech."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112358","DOI":"10.1016\/j.jbiomech.2024.112358","article-title":"Generative adversarial networks to create synthetic motion capture datasets including subject and gait characteristics","volume":"177","author":"Bicer","year":"2024","journal-title":"J. Biomech."},{"key":"ref_48","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T09:04:59Z","timestamp":1771923899000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,18]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26041320"],"URL":"https:\/\/doi.org\/10.3390\/s26041320","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,18]]}}}