Affiliations: [a]
Department of Biomedical Engineering, Sahrdaya College of Engineering and Technology, Thrissur, Kerala, India
| [b]
Department of Electrical and Electronics Engineering, Government Engineering College, Thrissur, Kerala, India
| [c]
Department of Electrical and Electronics Engineering, Jyothi Engineering College, Thrissur, Kerala, India
| [d]
Department of Electrical and Electronics Engineering, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. Remya George, Department of Biomedical Engineering, Sahrdaya College of Engineering and Technology, Thrissur, Kerala 680684, India. E-mail: remyageorge92@gmail.com.
Abstract: Law enforcement teams across the globe experience the highest occupational stress and stress-related diseases. Physical exercise and an active lifestyle are recommended as part of their profession to equip them to fight stress and related health adversities. The research is carried out using objective measures of Heart Rate Variability (HRV), Electro Dermal Activity (EDA), Heart Rate Recovery (HRR), and subjective questionnaires. HRV was generated with an electrocardiogram (ECG) signal acquired using NI myRIO 1900 interfaced with the Vernier EKG sensor. HRR was acquired with the help of a Polar chest strap exercise heart rate monitor and EDA acquisition was carried out with Mindfield E-Sense electrodes. Then statistical features are extracted from the collected data, and feed to the AQCNN (Aquila convolution neural network) classifier to predict the stress. Signal analyses were done in Kubios 4.0, Ledalab V3.x in a MATLAB environment. The results pointed out that exercise training is effective in increasing the vagal tone of the Autonomic Nervous System (ANS) and hence improves the recovery potential of the cardiovascular system from stress. The proposed AQCNN method improves the accuracy by 95.12% which is better than 93.13%, 85.36% and 80.13% from Statistical technique, CNN and ML-SVM respectively. The findings have the potential to influence decision-making in the selection and training of recruits in high-stress positions, hence optimizing the cost and time of training by identifying maladaptive recruits early.
