AI-based Detection of Stress using Heart Rate Data obtained from Wearable Devices
Anurag Mandal, Manasi Paradkar, Prabodh Panindre, Sunil Kumar
Abstract
Stress, a pervasive issue in today's world, significantly impacts mental and physical health. With approximately 20% of adults experiencing mental health disorders each year, and depression affecting over 260 million people globally, effective stress manage- ment is crucial. Chronic stress is also associated with physical ailments, including cardiovascular diseases, the leading global cause of death. In parallel, the wearable device market has witnessed substantial growth, with over 300 million units sold worldwide in 2021. These devices offer a unique opportunity to collect physiological and behavioral data, making them invaluable for stress detection and prediction. Combining artificial intelligence (AI) and wearables, this paper explores their accuracy in capturing vital data and the potential to revolutionize stress management, ultimately improving mental and physical well-being. In this paper, the ensemble-based classifier known as the Histogram-based Gradient Boosting Classifier detected stress from heart rates with an accuracy of 73%. This establishes the independent nature of Heart Rates for stress prediction, emphasizing that there is no need for sophisticated ECG and HRV readings. This opens up the possibility of using sensors as used in wearable devices to effectively detect stress in our day-to-day lives.