uActivity: A User-Specific Human Activity Recognition and Fall Detection for Elderly Care
Received: 20 June 2025 | Revised: 28 September 2025 | Accepted: 5 October 2025 | Online: 2 November 2025
Corresponding author: Md. Salah Uddin Yusuf
Abstract
Falls are a major concern among the elderly, as they can cause serious injuries and even death. Therefore, the development of an effective and reliable fall detection system for the elderly is a critical area of research that can significantly improve their safety and quality of life. This paper presents a user-centric Human Activity Recognition (uHAR) model that integrates machine learning algorithms and advanced sensor technology to analyze and classify the various activities and movements. The proposed model, called uActivity, aims to significantly improve the safety and well-being of elderly people, using a uHAR model that integrates multiple sensor data streams and machine learning algorithms. In the performance analysis of daily activity and fall classification, the uActivity algorithm demonstrates satisfactory results in accurately classifying activities and detecting falls among elderly individuals, achieving an accuracy of more than 99% in detecting falls. The proposed uHAR architecture with the uActivity algorithm can significantly improve the accuracy and reliability of fall detection and daily activity classification systems for elderly people.
Keywords:
human activity recognition, machine learning, LSTM, user-centric model, fall detectionDownloads
References
Y. Xiao et al., "Effect of baduanjin on the fall and balance function in middle-aged and elderly people: A protocol for systematic review and meta-analysis," Medicine, vol. 100, no. 37, Sep. 2021, Art. no. e27250. DOI: https://doi.org/10.1097/MD.0000000000027250
M. Chen, Q. Tang, S. Xu, P. Leng, and Z. Pan, "Design and Evaluation of an Augmented Reality-Based Exergame System to Reduce Fall Risk in the Elderly," International Journal of Environmental Research and Public Health, vol. 17, no. 19, Oct. 2020, Art. no. 7208. DOI: https://doi.org/10.3390/ijerph17197208
Y. Zhang and L. Duan, "Toward Elderly Care: A Phase-Difference-of-Arrival Assisted Ultra-Wideband Positioning Method in Smart Home," IEEE Access, vol. 8, pp. 139387–139395, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3012717
J. Li, D. Chen, X. Tang, and H. Li, "On the protective capacity of a safety vest for the thoracic injury caused by falling down," BioMedical Engineering OnLine, vol. 18, no. 1, Dec. 2019, Art. no. 40. DOI: https://doi.org/10.1186/s12938-019-0652-3
S. Hu, Q. Jia, L. Dong, J. Han, M. Guo, and W. Guo, "An evaluation method for product design solutions for healthy aging companionship," Frontiers in Public Health, vol. 10, Sep. 2022, Art. no. 919300. DOI: https://doi.org/10.3389/fpubh.2022.919300
B. C. Giao, T. H. An, N. T. H. Anh, and H. N. Minh, "Hidden Markov Model for recognition of skeletal databased hand movement gestures," EAI Endorsed Transactions on Context-aware Systems and Applications, vol. 4, no. 14, Jun. 2018, Art. no. 154819. DOI: https://doi.org/10.4108/eai.18-6-2018.154819
K. Gawrońska and J. Lorkowski, "Smart homes for the older population: particularly important during the COVID-19 outbreak," Rheumatology, vol. 59, no. 1, pp. 41–46, Mar. 2021. DOI: https://doi.org/10.5114/reum.2021.103939
S. Rastogi and J. Singh, "Human fall detection and activity monitoring: a comparative analysis of vision-based methods for classification and detection techniques," Soft Computing, vol. 26, no. 8, pp. 3679–3701, Apr. 2022. DOI: https://doi.org/10.1007/s00500-021-06717-x
M. Alanazi, R. S. Aldahr, and M. Ilyas, "Human Activity Recognition through Smartphone Inertial Sensors with ML Approach," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12780–12787, Feb. 2024. DOI: https://doi.org/10.48084/etasr.6586
T. Zhang, J. Sarrazin, G. Valerio, and D. Istrate, "Estimation of Human Body Vital Signs Based on 60 GHz Doppler Radar Using a Bound-Constrained Optimization Algorithm," Sensors, vol. 18, no. 7, Jul. 2018, Art. no. 2254. DOI: https://doi.org/10.3390/s18072254
M. Al-qaness and F. Li, "WiGeR: WiFi-Based Gesture Recognition System," ISPRS International Journal of Geo-Information, vol. 5, no. 6, Jun. 2016, Art. no. 92. DOI: https://doi.org/10.3390/ijgi5060092
K. Md. Shahiduzzaman, X. Hei, C. Guo, and W. Cheng, "Enhancing Fall Detection for Elderly with Smart Helmet in a Cloud-Network-Edge Architecture," in 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), Yilan, Taiwan, May 2019, pp. 1–2. DOI: https://doi.org/10.1109/ICCE-TW46550.2019.8991972
J. Peng, Y. Gao, X. Hei, and C. Zhang, "Low-Latency Guarantee of Wireless Body Area Networking for Fall Detection and Protection," in 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), Yilan, Taiwan, May 2019, pp. 1–2. DOI: https://doi.org/10.1109/ICCE-TW46550.2019.8991736
A. Wang, G. Chen, X. Wu, L. Liu, N. An, and C. Y. Chang, "Towards Human Activity Recognition: A Hierarchical Feature Selection Framework," Sensors, vol. 18, no. 11, Oct. 2018, Art. no. 3629. DOI: https://doi.org/10.3390/s18113629
Q. Meng et al., "Opportunities and Challenges for Chinese Elderly Care Industry in Smart Environment Based on Occupants’ Needs and Preferences," Frontiers in Psychology, vol. 11, Jun. 2020, Art. no. 1029. DOI: https://doi.org/10.3389/fpsyg.2020.01029
K. M. Shahiduzzaman and Md. S. U. Yusuf, "EdgeFall: a promising cloud-edge-end architecture for elderly fall care," International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 4, pp. 4721-4733, Aug. 2023. DOI: https://doi.org/10.11591/ijece.v13i4.pp4721-4733
K. M. Shahiduzzaman, J. Peng, Y. Gao, X. Hei, and W. Cheng, "Towards Accurate and Robust Fall Detection for the Elderly in a Hybrid Cloud-Edge Architecture," in 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/ IOP/SCI), Leicester, United Kingdom, Aug. 2019, pp. 1626–1631. DOI: https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00290
D. Micucci, M. Mobilio, and P. Napoletano, "UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones," Applied Sciences, vol. 7, no. 10, Oct. 2017, Art. no. 1101. DOI: https://doi.org/10.3390/app7101101
G. Vavoulas, C. Chatzaki, T. Malliotakis, M. Pediaditis, and M. Tsiknakis, "The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones," in International Conference on Information and Communication Technologies for Ageing Well and e-Health, Apr. 2016, vol. 2, pp. 143–151. DOI: https://doi.org/10.5220/0005792401430151
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, "A Public Domain Dataset for Human Activity Recognition Using Smartphones," in ESANN 2013 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 2013, pp. 437–442.
J. L. Reyes-Ortiz, L. Oneto, A. Samà, X. Parra, and D. Anguita, "Transition-Aware Human Activity Recognition Using Smartphones," Neurocomputing, vol. 171, pp. 754–767, Jan. 2016. DOI: https://doi.org/10.1016/j.neucom.2015.07.085
A. Sucerquia, J. López, and J. Vargas-Bonilla, "SisFall: A Fall and Movement Dataset," Sensors, vol. 17, no. 1, Jan. 2017, Art. no. 198. DOI: https://doi.org/10.3390/s17010198
D. Garcia-Gonzalez, D. Rivero, E. Fernandez-Blanco, and M. R. Luaces, "A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors," Sensors, vol. 20, no. 8, Apr. 2020, Art. no. 2200. DOI: https://doi.org/10.3390/s20082200
"uActivity." https://figshare.com/s/89d1f50634617a66d710?file=5290301.
Downloads
How to Cite
License
Copyright (c) 2025 Kazi Md. Shahiduzzaman, Md. Salah Uddin Yusuf, Md. Sajjad Hossen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
