Enhancing Healthcare Monitoring: A Deep Learning Approach to Human Activity Recognition using Wearable Sensors
Received: 13 October 2024 | Revised: 29 October 2024 and 2 November 202X | Accepted: 3 November 202 | Online: 12 November 2024
Corresponding author: Sami Aziz Alshammari
Abstract
Wearable devices and deep learning methods for Human Activity Recognition (HAR) have attracted a lot of interest because they could change healthcare monitoring. This study presents a CNN-LSTM model to accurately and reliably detect human movements from smartphone sensor data. The proposed model takes advantage of both the strengths of Long Short-Term Memory (LSTM) networks for modeling time and Convolutional Neural Networks (CNNs) for extracting features from space. This enables determining how the input data change over time and space. This study examines whether this method can work and is practical in real-life healthcare settings, focused on uses such as watching patients from distance, caring for the elderly, and therapy. The proposed model was evaluated on publicly accessible standard datasets. Various architectural configurations and hyperparameters were examined to determine their performance. The proposed CNN-LSTM model performed well and has great potential for practical use in activity tracking and environment understanding systems.
Keywords:
deep learning, healthcare monitoring, smartphone sensor data, volutional neural network (CNN), human activity recognition (HAR), long short term memory (LSTM)Downloads
References
L. Bibbò and M. M. B. R. Vellasco, "Human Activity Recognition (HAR) in Healthcare," Applied Sciences, vol. 13, no. 24, Jan. 2023, Art. no. 13009.
K. M. Gritsyuk and V. I. Gritsyuk, "Convolutional and Long Short-Term Memory Neural Networks Based Models for Remaining Useful Life Prediction," International Journal on Information Technologies & Security, vol. 14, no. 1, 2022.
A. Nouriani, R. A. McGovern, and R. Rajamani, "Deep-Learning-Based Human Activity Recognition Using Wearable Sensors," IFAC-PapersOnLine, vol. 55, no. 37, pp. 1–6, Jan. 2022.
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.
L. Bao and S. S. Intille, "Activity Recognition from User-Annotated Acceleration Data," in Pervasive Computing, Vienna, Austria, 2004, pp. 1–17.
C. A. Ronao and S.-B. Cho, "Human activity recognition with smartphone sensors using deep learning neural networks," Expert Systems with Applications, vol. 59, pp. 235–244, Oct. 2016.
Z. Wang, W. Yan, and T. Oates, "Time series classification from scratch with deep neural networks: A strong baseline," in 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, May 2017, pp. 1578–1585.
K. Gusain, A. Gupta, and B. Popli, "Transition-Aware Human Activity Recognition Using eXtreme Gradient Boosted Decision Trees," in Advanced Computing and Communication Technologies, 2018, pp. 41–49.
I. N. Yulita and S. Saori, "Human Activities and Postural Transitions Classification using Support Vector Machine and K-Nearest Neighbor Methods," IOP Conference Series: Earth and Environmental Science, vol. 248, no. 1, Nov. 2019, Art. no. 012025.
C. L. Liu, W. H. Hsaio, and Y. C. Tu, "Time Series Classification With Multivariate Convolutional Neural Network," IEEE Transactions on Industrial Electronics, vol. 66, no. 6, pp. 4788–4797, Jun. 2019.
J. Zhu, H. Chen, and W. Ye, "A Hybrid CNN–LSTM Network for the Classification of Human Activities Based on Micro-Doppler Radar," IEEE Access, vol. 8, pp. 24713–24720, 2020.
J. Huang, S. Lin, N. Wang, G. Dai, Y. Xie, and J. Zhou, "TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition," IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 1, pp. 292–299, Jan. 2020.
K. Xia, J. Huang, and H. Wang, "LSTM-CNN Architecture for Human Activity Recognition," IEEE Access, vol. 8, pp. 56855–56866, 2020.
J. R. Kwapisz, G. M. Weiss, and S. A. Moore, "Activity recognition using cell phone accelerometers," ACM SIGKDD Explorations Newsletter, vol. 12, no. 2, pp. 74–82, Mar. 2011.
F. J. Ordóñez and D. Roggen, "Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition," Sensors, vol. 16, no. 1, Jan. 2016, Art. no. 115.
H. S. Saad, J. F. W. Zaki, and M. M. Abdelsalam, "Employing of machine learning and wearable devices in healthcare system: tasks and challenges," Neural Computing and Applications, vol. 36, no. 29, pp. 17829–17849, Oct. 2024.
S. S. Pushpalatha and S. Math, "Human activity recognition using hybrid model," International Journal on Information Technologies & Security, vol. 14, no. 4, pp. 55–66, 2022.
S. W. Pienaar and R. Malekian, "Human Activity Recognition using LSTM-RNN Deep Neural Network Architecture," in 2019 IEEE 2nd Wireless Africa Conference (WAC), Pretoria, South Africa, Aug. 2019, pp. 1–5.
N. Y. Hammerla, S. Halloran, and T. Ploetz, "Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables." arXiv, Apr. 29, 2016.
R. Chavarriaga et al., "The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition," Pattern Recognition Letters, vol. 34, no. 15, pp. 2033–2042, Nov. 2013.
S. Mohsen, A. Elkaseer, and S. G. Scholz, "Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition," IEEE Access, vol. 9, pp. 150508–150521, 2021.
D. Anguita, A. Ghio, L. Oneto, X. Parra Perez, and J. L. Reyes Ortiz, "A public domain dataset for human activity recognition using smartphones," presented at the Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2013, pp. 437–442.
F. Cruciani et al., "Feature learning for Human Activity Recognition using Convolutional Neural Networks," CCF Transactions on Pervasive Computing and Interaction, vol. 2, no. 1, pp. 18–32, Mar. 2020.
M. Ronald, A. Poulose, and D. S. Han, "iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition," IEEE Access, vol. 9, pp. 68985–69001, 2021.
M. Akter, S. Ansary, M. A.-M. Khan, and D. Kim, "Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination," Sensors, vol. 23, no. 12, Jan. 2023, Art. no. 5715.
Downloads
How to Cite
License
Copyright (c) 2024 Sami Aziz Alshammari, Nasser S. Albalawi
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.
