Mental Stress Assessment Using PPG Signal a Deep Neural Network Approach

Abstract

Due to the pace of modern life and the shift of nature of work from physical to cognitive, mental stress is increasing in every profession. Mental stress has now become a leading cause of work-related illness. There are numerous sedentary occupations, such as those in the IT industry, where individuals are required to work for extended periods of time, leading to stress. Working for extended periods under mental stress can increase the risk of life-threatening diseases like cardiovascular diseases, mental health disorders etc. There is, thus, a requirement for a non-obtrusive tool to detect mental stress. In this work, pulse rate variability (PRV) of 15 subjects during 5 cognitive states (relaxation, deep breathing, and during three mental tasks involving three levels of mental stress) was examined using photoplethysmography (PPG). The result of this study indicates that 18 features (9-time domain and 9 frequency domain) are statistically significant at p < 0.05 as per the Friedman test in 5 cognitive states. The machine-learning algorithm based upon a multi-layer perceptron (MLP) was able to classify with an overall accuracy of 85.1±1.1%. Classification accuracy further improved by using deep neural networks (DNN) to 91±1.1%.

KEYWORDS:

• Mental stress
• Mental stress detection
• Pulse rate variability
• Power spectral density

Additional information

Notes on contributors

Prerita Kalra

Prerita Kalra is currently working in the Optical Devices and Systems Division of CSIR-CSIO. She has previously worked with Infosys and is a BE graduate in electronics and communication engineering from Chandigarh College of Engineering and Technology, Panjab University, Chandigarh. She has been a merit scholarship holder throughout college. An electronics enthusiast with a keen interest in embedded system design, she has worked on MATLAB & Simulink and on various IoT platforms provided by Intel, Texas and Arduino. She has published her work in various journals as well as presented her work at various conferences organised by reputed organisations and professional bodies like PGIMER, CSIR-CSIO, CDAC Chandigarh, Institution of Electronics and Telecommunication Engineers and Indian Society of Ergonomics. She has been a regular participant in technical competitions such as Texas Instruments India Innovation Challenge Design Contest (Texas Instruments) and Formula Student India (FMSCI). Email: [email protected]

Vivek Sharma

Vivek Sharma is currently associated with Lovely Professional University as an assistant professor in Product and Industrial Design Department. He did Bachelor of Technology in electronics and communication engineering from Kurukshetra University, Kurukshetra, Master of Engineering in industrial design from Punjab Engineering College, Chandigarh and currently pursuing Doctorate from Centre of Excellence(Industrial & Product Design) Punjab Engineering College, Chandigarh. He is an active researcher in the area of affective computing, industrial design and kansei engineering.