Federated learning for nurse stress prediction using wearable sensors: Integrating biomechanical data
Abstract
In today’s fast-paced work environments, accurately predicting stress levels is essential for effective healthcare workforce management, particularly among nurses in high-pressure settings. Despite the availability of various mental health initiatives, timely detection of stress remains challenging due to concerns over sensitive personal data privacy. To address this, we propose a federated learning (FL) framework that utilizes artificial intelligence (AI) to predict nurse stress levels by integrating distributed biomechanical data from wearable sensors, thereby preventing data leakage. Biometric features from datasets at each FL client are extracted and used to train local neural network (NN) models. After several aggregation rounds, the global model converges to predict nurse stress levels. Simulations demonstrate the effectiveness of our method, achieving over 90% prediction accuracy, which enhances the feasibility of privacy-preserving stress monitoring and offers scalable solutions for occupational health management.
References
1. Sani MM, Jafaru Y, Ashipala DO, et al. Influence of work-related stress on patient safety culture among nurses in a tertiary hospital: a cross-sectional study. BMC nursing. 2024; 23(1): 81.
2. Lenzo V, Quattropani MC, Sardella A, et al. Depression, anxiety, and stress among healthcare workers during the COVID-19 outbreak and relationships with expressive flexibility and context sensitivity. Frontiers in Psychology. 2021; 12: 623033.
3. Hosseini S, Gottumukkala R, Katragadda S, et al. A multimodal sensor dataset for continuous stress detection of nurses in a hospital. Scientific Data. 2022; 9(1): 255.
4. Purcell SR, Kutash M, Cobb S. The relationship between nurses’ stress and nurse staffing factors in a hospital setting. Journal of Nursing Management. 2011; 19(6): 714-720.
5. Babapour AR, Gahassab-Mozaffari N, Fathnezhad-Kazemi A. Nurses’ job stress and its impact on quality of life and caring behaviors: a cross-sectional study. BMC nursing. 2022; 21(1): 75.
6. Gedam S, Paul S. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access. 2021; 9: 84045-84066.
7. Chen J, Abbod M, Shieh JS. Pain and stress detection using wearable sensors and devices—A review. Sensors. 2021; 21(4): 1030.
8. Gil-Martin M, San-Segundo R, Mateos A, et al. Human stress detection with wearable sensors using convolutional neural networks. IEEE Aerospace and Electronic Systems Magazine. 2022; 37(1): 60-70.
9. Gonzalez-Carabarin L, Castellanos-Alvarado EA, Castro-Garcia P, Garcia-Ramirez MA. Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response. Computer methods and programs in biomedicine. 2021; 209: 106314.
10. Hafer JF, Vitali R, Gurchiek R, et al. Challenges and advances in the use of wearable sensors for lower extremity biomechanics. Journal of biomechanics. 2023; 157: 111714.
11. Hughes GT, Camomilla V, Vanwanseele B, et al. Novel technology in sports biomechanics: Some words of caution. Sports Biomechanics. 2024; 23(4): 393-401.
12. Bonawitz K, Eichner H, Grieskamp W, et al. Towards federated learning at scale: System design. Proceedings of machine learning and systems. 2019; 1: 374-388.
13. Konečný J, McMahan HB, Ramage D, et al. Federated optimization: Distributed machine learning for on-device intelligence. arXiv; 2016.
14. Hussain F, Hussain F, Ehatisham-ul-Haq M, et al. Activity-aware fall detection and recognition based on wearable sensors. IEEE Sensors Journal. 2019; 19(12): 4528-4536.
15. Ilyas CMA, Nasrollahi K, Rehm M, et al. Rehabilitation of traumatic brain injured patients: Patient mood analysis from multimodal video. In: Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP); 2018.
16. Wong CK, Ho DTY, Tam AR, et al. Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial. BMJ open. 2020; 10(7): e038555.
17. Regalia G, Onorati F, Lai M, et al. Multimodal wrist-worn devices for seizure detection and advancing research: focus on the Empatica wristbands. Epilepsy research. 2019; 153: 79-82.
18. Sundararajan K, Georgievska S, Te Lindert BH, et al. Sleep classification from wrist-worn accelerometer data using random forests. Scientific reports. 2021; 11(1): 24.
19. Delmastro F, Di Martino F, Dolciotti C. Cognitive training and stress detection in mci frail older people through wearable sensors and machine learning. Ieee Access. 2020; 8: 65573-65590.
20. Morris D, Saponas TS, Guillory A, et al. RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2014.
21. Dasari SB, Mallareddy CT, Annavarapu S, et al. Detection of Mental Stress Levels Using Electroencephalogram Signals (EEG). In: Proceedings of the 2023 2nd International Conference on Futuristic Technologies (INCOFT); 2023.
22. Mathur A, Sethia D. Body sensor-based multimodal nurse stress detection using machine learning. In: Proceedings of the 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS); 2024.
23. Ruder S. An overview of gradient descent optimization algorithms. arXiv; 2016.
24. McMahan B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017; 2017.
25. Hosseini S, Gottumukkala R. Nurse Stress Prediction Wearable Sensors. Kaggle; 2023.
26. Hall MA. Correlation-based feature selection of discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000); 2000.
27. Zhao Y, Li M, Lai L, et al. Federated learning with non-iid data. arXiv; 2018.
28. Hsieh K, Phanishayee A, Mutlu O, Gibbons P. The non-iid data quagmire of decentralized machine learning. In: Proceedings of the International Conference on Machine Learning; 2020.
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