Minimally-invasive Wearable Sensors and Data Processing Methods for Mental Stress Detection
Chronic stress is endemic to modern society. If we could monitor our mental state, we may be able to develop insights about how we respond to stress. However, it is unfeasible to continuously annotate stress levels all the time. In the studies conducted for this dissertation, a minimally-invasive wearable sensor platform and physiological data processing methods were developed to analyze a number of physiological correlates of mental stress.
We present a minimally obtrusive wearable sensor system that incorporates embedded and wireless communication technologies. The system is designed such that it provides a balance between data collection and user comfort. The system records the following stress related physiological and contextual variables: heart rate variability (HRV), respiratory activity, electrodermal activity (EDA), electromyography (EMG), body acceleration, and geographical location.
We assume that if the respiratory influences on HRV can be removed, the residual HRV will be more salient to stress in comparison with raw HRV. We develop three signal processing methods to separate HRV into a respiration influenced and residual HRV. The first method consists of estimating respiration-induced portion of HRV using a linear system identification method (autoregressive moving average model with exogenous inputs). The second method consists of decomposing HRV into respiration-induced principal dynamic mode and residual using nonlinear dynamics decomposition method (principal dynamic mode analysis). The third method consists of splitting HRV into respiration-induced power spectrum and residual in frequency domain using spectral weighting method. These methods were validated on a binary discrimination problem of two psychophysiological conditions: mental stress and relaxation. The linear system identification method, nonlinear dynamics decomposition method, and spectral weighting method classified stress and relaxation conditions at 85.2 %, 89.2 %, and 81.5 % respectively. When tonic and phasic EDA features were combined with the linear system identification method, the nonlinear dynamics decomposition method, and the spectral weighting method, the average classification rates were increased to 90.4 %, 93.2 %, and 88.1 % respectively.
To evaluate the developed wearable sensors and signal processing methods on multiple subjects, we performed case studies. In the first study, we performed experiments in a laboratory setting. We used the wearable sensors and signal processing methods to discriminate between stress and relaxation conditions. We achieved 81 % average classification rate in the first case study. In the second study, we performed experiments to detect stress in ambulatory settings. We collected data from the subjects who wore the sensors during regular daily activities. Relaxation and stress conditions were allocated during daily activities. We achieved a 72 % average classification rate in ambulatory settings.
Together, the results show achievements in recognizing stress from wearable sensors in constrained and ambulatory conditions. The best results for stress detection were achieved by removing respiratory influence from HRV and combining features from EDA.