Why the study?
Law enforcement teams face high occupational stress and stress-related diseases, and exercise is recommended to help combat stress and related health adversities.
Does long-term exercise training and physiological signal analysis using an AQCNN classifier improve stress prediction in women in law enforcement?
Observational
Does long-term exercise training and physiological signal analysis using an AQCNN classifier improve stress prediction in women in law enforcement?
Answers are grounded in this paper and cite their sources.
Long-term exercise training increases vagal tone in female law enforcement officers, and a novel AQCNN classifier can predict stress with 95.12% accuracy using physiological signals.
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.
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George et al. (2022) conducted an observational in Occupational stress. AQCNN (Aquila convolution neural network) classifier and long-term exercise training vs. Statistical technique, CNN, and ML-SVM was evaluated on Stress prediction accuracy.
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