A health monitoring and exercise optimization method based on fusion of action recognition and physiological signals—A study on students at the Zhejiang police college
Abstract
With the growing emphasis on health management and the widespread adoption of smart devices, the demand for motion recognition and health anomaly monitoring has become increasingly pressing. In particular, for students at the Zhejiang Police College, maintaining good physical fitness and health is critical because they often face high-intensity work pressure and unpredictable work environments. Fundamentally, health monitoring is a pattern recognition challenge, wherein the objective is to extract critical features from multimodal data to detect abnormal behaviors or health conditions, thereby enabling real-time surveillance of individual health statuses. Against this backdrop, this paper introduces a novel health monitoring framework (PSCLM), which integrates convolutional neural networks (CNN), long short-term memory networks (LSTM), and modal decomposition techniques, aiming to enhance the precision and resilience of action recognition and health anomaly detection. Initially, the proposed framework analyzes inertial sensor data and constructs a convolutional long short-term memory (CLM) model utilizing CNN and LSTM to facilitate movement type recognition. Subsequently, given the significance of heart rate as a vital indicator during physical activity, heart rate and electrocardiogram (ECG) signals, along with their variational mode decomposition (VMD) decomposition features, are incorporated with movement recognition features to achieve multidimensional and hierarchical fusion of physiological signals during exercise. Finally, leveraging these fused features, the framework achieves comprehensive monitoring of exercise-induced health states. Experimental evaluations reveal that the PSCLM framework attains an average accuracy exceeding 0.9 for action recognition on publicly available datasets, while its accuracy for abnormal state detection reaches 0.95, outperforming traditional approaches and single deep learning models significantly. This research not only provides an innovative technological approach for sports health management and anomaly detection but also establishes a critical foundation for advancing next-generation intelligent health monitoring systems.
References
1. He Z, Li W, Salehi H, et al. Integrated structural health monitoring in bridge engineering. Automation in Construction. 2022; 136: 104168. doi: 10.1016/j.autcon.2022.104168
2. Dong CZ, Catbas FN. A review of computer vision–based structural health monitoring at local and global levels. Structural Health Monitoring. 2020; 20(2): 692-743. doi: 10.1177/1475921720935585
3. Sujith AVLN, Sajja GS, Mahalakshmi V, et al. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neuroscience Informatics. 2022; 2(3): 100028. doi: 10.1016/j.neuri.2021.100028
4. Jayawickrema UMN, Herath HMCM, Hettiarachchi NK, et al. Fibre-optic sensor and deep learning-based structural health monitoring systems for civil structures: A review. Measurement. 2022; 199: 111543. doi: 10.1016/j.measurement.2022.111543
5. Jefiza A, Pramunanto E, Boedinoegroho H, et al. Fall detection based on accelerometer and gyroscope using back propagation. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). 2017: 1-6. doi: 10.1109/eecsi.2017.8239149
6. Ishii S, Nkurikiyeyezu K, Luimula M, et al. ExerSense: Real-Time Physical Exercise Segmentation, Classification, and Counting Algorithm Using an IMU Sensor. Activity and Behavior Computing. 2020; 204: 239–255.
7. Dawar N, Kehtarnavaz N. Action Detection and Recognition in Continuous Action Streams by Deep Learning-Based Sensing Fusion. IEEE Sensors Journal. 2018; 18(23): 9660-9668. doi: 10.1109/jsen.2018.2872862
8. Kui L, Chen C, Jafari R, et al. Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition. IEEE Sensors Journal. 2014; 14(6): 1898-1903. doi: 10.1109/jsen.2014.2306094
9. Li H, Shrestha A, Fioranelli F, et al. Multisensor data fusion for human activities classification and fall detection. 2017 IEEE SENSORS. Published online October 2017. doi: 10.1109/icsens.2017.8234179
10. Ehatisham-Ul-Haq M, Javed A, Azam MA, et al. Robust Human Activity Recognition Using Multimodal Feature-Level Fusion. IEEE Access. 2019; 7: 60736-60751. doi: 10.1109/access.2019.2913393
11. Radu V, Tong C, Bhattacharya S, et al. Multimodal Deep Learning for Activity and Context Recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2018; 1(4): 1-27. doi: 10.1145/3161174
12. Sadeghi R, Banerjee T, Hughes JC, et al. Sleep quality prediction in caregivers using physiological signals. Computers in Biology and Medicine. 2019; 110: 276-288. doi: 10.1016/j.compbiomed.2019.05.010
13. Nho YH, Lim JG, Kwon DS. Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device. IEEE Access. 2020; 8: 40389-40401. doi: 10.1109/access.2020.2969453
14. Kim Y, Jeung J, Song Y, et al. A Wearable System for Heart Rate Recovery Evaluation with Real-Time Classification on Exercise Condition. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Published online November 1, 2021: 7609-7612. doi: 10.1109/embc46164.2021.9629673
15. Xiao N, Yu W, Han X. Wearable heart rate monitoring intelligent sports bracelet based on Internet of things. Measurement. 2020; 164: 108102. doi: 10.1016/j.measurement.2020.108102
16. Spathis D, Perez-Pozuelo I, Brage S, et al. Learning Generalizable Physiological Representations from Large-scale Wearable Data. arXiv preprint arXiv:2011.04601. 2020; 1-6.
17. Chong W, Kim S, Yu C, et al. Analysis of Health Management Using Physiological Data Based on Continuous Exercise. International Journal of Precision Engineering and Manufacturing. 2021; 22(5): 899-907. doi: 10.1007/s12541-021-00503-3
18. Gao ZK, Li YL, Yang YX, et al. A recurrence network-based convolutional neural network for fatigue driving detection from EEG. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2019; 29(11). doi: 10.1063/1.5120538
19. Porumb M, Stranges S, Pescapè A, et al. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Scientific Reports. 2020; 10(1). doi: 10.1038/s41598-019-56927-5
20. Shi X, Wang Z, Zhao H, et al. Threshold-Free Phase Segmentation and Zero Velocity Detection for Gait Analysis Using Foot-Mounted Inertial Sensors. IEEE Transactions on Human-Machine Systems. 2023; 53(1): 176-186. doi: 10.1109/thms.2022.3228515
21. Lin F, Wang Z, Zhao H, et al. Adaptive Multi-Modal Fusion Framework for Activity Monitoring of People With Mobility Disability. IEEE Journal of Biomedical and Health Informatics. 2022; 26(8): 4314-4324. doi: 10.1109/jbhi.2022.3168004
22. Yang S, Yang H, Li N, et al. Short-Term Prediction of 80–88 km Wind Speed in Near Space Based on VMD–PSO–LSTM. Atmosphere. 2023; 14(2): 315. doi: 10.3390/atmos14020315
23. Bollampally A, Kavitha J, Sumanya P, et al. Optimizing Edge Computing for Activity Recognition: A Bidirectional LSTM Approach on the PAMAP2 Dataset. Engineering, Technology & Applied Science Research. 2024; 14(6): 18086-18093. doi: 10.48084/etasr.8861
24. Triantafyllidis A, Kondylakis H, Katehakis D, et al. Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review. JMIR mHealth and uHealth. 2022; 10(4): e32344. doi: 10.2196/32344
25. Ali F, El-Sappagh S, Islam SMR, et al. An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems. 2021; 114: 23-43. doi: 10.1016/j.future.2020.07.047
Copyright (c) 2025 Author(s)

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.