{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:41:34Z","timestamp":1782387694869,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2123749"],"award-info":[{"award-number":["2123749"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2149950"],"award-info":[{"award-number":["2149950"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF Research Experiences for Undergraduates Program","doi-asserted-by":"publisher","award":["2123749"],"award-info":[{"award-number":["2123749"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF Research Experiences for Undergraduates Program","doi-asserted-by":"publisher","award":["2149950"],"award-info":[{"award-number":["2149950"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have developed and deployed a SmartFall system on a commodity-based smartwatch which has been trialled by nine elderly participants. The system, while being usable and welcomed by the participants in our trials, has two serious limitations. The first limitation is the inability to collect a large amount of personalized data for training. When the fall detection model, which is trained with insufficient data, is used in the real world, it generates a large amount of false positives. The second limitation is the model drift problem. This means an accurate model trained using data collected with a specific device performs sub-par when used in another device. Therefore, building one model for each type of device\/watch is not a scalable approach for developing smartwatch-based fall detection system. To tackle those issues, we first collected three datasets including accelerometer data for fall detection problem from different devices: the Microsoft watch (MSBAND), the Huawei watch, and the meta-sensor device. After that, a transfer learning strategy was applied to first explore the use of transfer learning to overcome the small dataset training problem for fall detection. We also demonstrated the use of transfer learning to generalize the model across the heterogeneous devices. Our preliminary experiments demonstrate the effectiveness of transfer learning for improving fall detection, achieving an F1 score higher by over 10% on average, an AUC higher by over 0.15 on average, and a smaller false positive prediction rate than the non-transfer learning approach across various datasets collected using different devices with different hardware specifications.<\/jats:p>","DOI":"10.3390\/s23031105","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T01:33:51Z","timestamp":1674092031000},"page":"1105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Transfer Learning on Small Datasets for Improved Fall Detection"],"prefix":"10.3390","volume":"23","author":[{"given":"Nader","family":"Maray","sequence":"first","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne Hee","family":"Ngu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6968-6536","authenticated-orcid":false,"given":"Jianyuan","family":"Ni","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minakshi","family":"Debnath","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"ref_1","unstructured":"(2019, June 17). Falls Are the Leading Cause of Death in Older Americans, Available online: https:\/\/www.cdc.gov\/media\/releases\/2016\/p0922-older-adult-falls.html."},{"key":"ref_2","unstructured":"(2019, June 17). Facts About Falls, Available online: https:\/\/www.cdc.gov\/falls\/facts.html."},{"key":"ref_3","unstructured":"(2019, June 17). 2017 Profile of Older Americans, Available online: https:\/\/acl.gov\/sites\/default\/files\/AgingandDisabilityinAmerica\/2017OlderAmericansProfile.pdf."},{"key":"ref_4","unstructured":"(2019, November 18). Preventing Falls in Hospitals, Available online: https:\/\/www.ahrq.gov\/professionals\/systems\/hospital\/fallpxtoolkit\/index.html."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tacconi, C., Mellone, S., and Chiari, L. (2011, January 23\u201326). Smartphone-based applications for investigating falls and mobility. Proceedings of the 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, Dublin, Ireland.","DOI":"10.4108\/icst.pervasivehealth.2011.246060"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.measurement.2019.03.079","article-title":"Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch","volume":"140","author":"Chen","year":"2019","journal-title":"Measurement"},{"key":"ref_7","unstructured":"(2022, December 20). Medical Life Alert Systems. Available online: http:\/\/www.lifealert.com."},{"key":"ref_8","unstructured":"(2019, November 18). Mobilehelp Smart. Available online: https:\/\/www.mobilehelp.com\/pages\/smart."},{"key":"ref_9","unstructured":"(2019, April 18). Apple Watch Series 4. Available online: http:\/\/www.apple.com\/apple-watch-series-4\/activity\/."},{"key":"ref_10","unstructured":"(2022, December 14). RightMinder\u2014Fall Detection for Android Smartwatches and Android Phones. Available online: https:\/\/mhealthspot.com\/2017\/03\/rightminder-android-wear-app-seniors\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3428666","article-title":"Ensemble Deep Learning on Wearables Using Small Datasets","volume":"2","author":"Mauldin","year":"2021","journal-title":"ACM Trans. Comput. Healthcare"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mauldin, T.R., Canby, M.E., Metsis, V., Ngu, A.H., and Rivera, C.C. (2018). SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning. Sensors, 18.","DOI":"10.3390\/s18103363"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/B978-0-12-804766-8.00005-4","article-title":"Neurologic examination in the elderly","volume":"167","author":"Paulson","year":"2019","journal-title":"Handb. Clin. Neurol."},{"key":"ref_14","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., and Bengio, Y. (2016, January 20\u201325). End-to-end attention-based large vocabulary speech recognition. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"ref_17","unstructured":"Zhu, X.J. (2022, December 01). Semi-Supervised Learning Literature Survey. Available online: https:\/\/pages.cs.wisc.edu\/~jerryzhu\/pub\/ssl_survey.pdf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Maqsood, M., Nazir, F., Khan, U., Aadil, F., Jamal, H., Mehmood, I., and Song, O.y. (2019). Transfer learning assisted classification and detection of Alzheimer\u2019s disease stages using 3D MRI scans. Sensors, 19.","DOI":"10.3390\/s19112645"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1002\/mrm.27969","article-title":"Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning","volume":"83","author":"Byra","year":"2020","journal-title":"Magn. Reson. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1049\/iet-cvi.2017.0524","article-title":"On combining active and transfer learning for medical data classification","volume":"13","author":"Tang","year":"2019","journal-title":"IET Comput. Vis."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neucom.2018.04.081","article-title":"Automatic ICD-9 coding via deep transfer learning","volume":"324","author":"Zeng","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","article-title":"Open access series of imaging studies: Longitudinal MRI data in nondemented and demented older adults","volume":"22","author":"Marcus","year":"2010","journal-title":"J. Cogn. Neurosci."},{"key":"ref_27","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2014, January 22\u201324). Decaf: A deep convolutional activation feature for generic visual recognition. Proceedings of the INTERNATIONAL Conference on Machine Learning. PMLR, Bejing, China."},{"key":"ref_28","unstructured":"Palanisamy, K., Singhania, D., and Yao, A. (2020). Rethinking CNN models for audio classification. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Koike, T., Qian, K., Kong, Q., Plumbley, M.D., Schuller, B.W., and Yamamoto, Y. (2020, January 20\u201324). Audio for Audio is Better? An Investigation on Transfer Learning Models for heart sound classification. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175450"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gemmeke, J.F., Ellis, D.P., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., and Ritter, M. (2017, January 5\u20139). Audio set: An ontology and human-labeled dataset for audio events. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ni, J., Sarbajna, R., Liu, Y., Ngu, A.H., and Yan, Y. (2022, January 22\u201327). Cross-modal knowledge distillation for Vision-to-Sensor action recognition. Proceedings of the ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9746752"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ni, J., Ngu, A.H., and Yan, Y. (2022, January 10). Progressive Cross-modal Knowledge Distillation for Human Action Recognition. Proceedings of the 30th ACM International Conference on Multimedia, Lisbon, Portugal.","DOI":"10.1145\/3503161.3548238"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, F., Shirahama, K., Nisar, M.A., Huang, X., and Grzegorzek, M. (2020). Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification. Sensors, 20.","DOI":"10.3390\/s20154271"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gikunda, P., and Jouandeau, N. (2021, January 13\u201315). Homogeneous Transfer Active Learning for Time Series Classification. Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Virtually Online.","DOI":"10.1109\/ICMLA52953.2021.00129"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Morales, F.J.O.n., and Roggen, D. (2016). Deep Convolutional Feature Transfer across Mobile Activity Recognition Domains, Sensor Modalities and Locations, Association for Computing Machinery.","DOI":"10.1145\/2971763.2971764"},{"key":"ref_37","unstructured":"Zhou, X., Zhai, N., Li, S., and Shi, H. (2022). Time Series Prediction Method of Industrial Process with Limited Data Based on Transfer Learning. IEEE Trans. Ind. Informatics, 1\u201310."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s13748-019-00190-2","article-title":"User-centered fall detection using supervised, on-line learning and transfer learning","volume":"8","author":"Villar","year":"2019","journal-title":"Prog. Artif. Intell."},{"key":"ref_39","first-page":"1","article-title":"IoT Middleware: A Survey on Issues and Enabling Technologies","volume":"4","author":"Ngu","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/JIOT.2021.3086043","article-title":"An IoT Edge Computing Framework Using Cordova Accessor Host","volume":"9","author":"Ngu","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_42","unstructured":"Kumar, V., Choudhary, A., and Cho, E. (2020). Data augmentation using pre-trained transformer models. arXiv."},{"key":"ref_43","unstructured":"Kuroyanagi, I., Hayashi, T., Adachi, Y., Yoshimura, T., Takeda, K., and Toda, T. (2022, December 01). Anomalous Sound Detection with Ensemble of Autoencoder and Binary Classification Approaches. Technical Report. DCASE2021 Challenge. Available online: https:\/\/dcase.community\/documents\/challenge2021\/technical_reports\/DCASE2021_Kuroyanagi_96_t2.pdf."},{"key":"ref_44","unstructured":"Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., and Malossi, C. (2018). Bagan: Data augmentation with balancing gan. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, X., Metsis, V., Wang, H., and Ngu, A. (2022). TTS-GAN: A Transformer-Based Time-Series Generative Adversarial Network, Springer Science and Business Media Deutschland GmbH, Texas State University. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-09342-5_13"},{"key":"ref_46","first-page":"6696","article-title":"Neural controlled differential equations for irregular time series","volume":"33","author":"Kidger","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","unstructured":"Hasani, R., Lechner, M., Amini, A., Rus, D., and Grosu, R. (March, January 22). Liquid time-constant networks. Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, Virtually."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113082","DOI":"10.1016\/j.eswa.2019.113082","article-title":"DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction","volume":"143","author":"Liu","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_49","unstructured":"Li, S., Jin, X., Xuan, Y., Zhou, X., Chen, W., Wang, Y.X., and Yan, X. (2019). Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Adv. Neural Inf. Process. Syst., 32."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:09:08Z","timestamp":1760119748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,18]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031105"],"URL":"https:\/\/doi.org\/10.3390\/s23031105","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,18]]}}}