Detection of Stress using Wearable Biosensor

Abstract

As the population increases in the world, the ratio of health care takers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during every day activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into”stressful” or”non stressful” situations. Our classification results show that this method is a good starting point towards real-time stress detection Stress is a natural reaction to various stress-inducing factors which can lead to physiological and behavioural changes. If persists for a longer period, stress can cause harmful effects on our body. Stress is a major concern in our day-to-day life. The human environments including worksite, home or society can induce stress on an individual to some extent. There are many ways that our body can react to stress, these reactions are mainly classified to either physiological reactions which includes the ‘fight or flight’ response by the Autonomous nervous system (ANS) of our body or behavioural reactions which includes defensive behaviour, days functional and expressive behaviour, the body sensors along with the concept of the Internet of Things can provide rich information about one’s mental and physical health.