Heart rate variability analysis using fuzzy clustering and robust identification techniques was used to model experimental data for mental stress assessment during air traffic control simulations.
Observational (n=38)
Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart rate variability (HRV) analysis is a popular tool for assessing the activities of autonomic nervous system. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1) online monitoring of heart rate signals, 2) signal processing (e.g., using the continuous wavelet transform to extract the local features of HRV in time-frequency domain), 3) exploiting fuzzy clustering and fuzzy identification techniques to render robustness in HRV analysis against uncertainties due to individual variations, and 4) monitoring the functioning of autonomic nervous system under different stress conditions. Our experiments involved 38 physically fit subjects (26 male, 12 female, aged 18-29 years) in air traffic control task simulations. The subjective rating scores of mental workload were assessed using NASA task load index. Fuzzy clustering methods have been used to model the experimental data. Further, a robust fuzzy identification technique has been used to handle the uncertainties due to individual variations for the assessment of mental stress.
Kumar et al. (Mon,) conducted a observational in Mental stress (n=38). Heart rate variability analysis using fuzzy clustering was evaluated on Mental stress assessment. Heart rate variability analysis using fuzzy clustering and robust identification techniques was used to model experimental data for mental stress assessment during air traffic control simulations.