Year: 2025
Type: article
Abstract: Stress has emerged as a significant contributor to various diseases in the modern world. Prolonged stress can lead to severe mental health issues such as depression, heart attack, and anxiety. Detecting and addressing stress in its early stages is crucial and is possible only through continuous monitoring. This paper presents the design of a cost-effective and accurate wearable device capable of detecting mental stress based on skin conductance, heart rate variability, and motion detected through an accelerometer. Additionally, it includes a mobile application that utilizes the device’s camera to detect stress. The mobile application also features a chatbot and an alleviation feed to help alleviate stress. The wearable device captures readings from its sensors and transmits the data to a smartphone via Bluetooth Low Energy. Through intelligent analysis of the correlations between these signals using machine learning algorithms, the application predicts whether the subject is experiencing stress. This approach not only helps users gain a better understanding of their stress patterns but also provides reliable data to healthcare professionals for more effective treatment.
Authors: Navoda Sewwandi, Kithsiri Samarasinghe
Institution: University of Moratuwa
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409212506