Audio, Visual, and Electrodermal Arousal Signals as Predictors of Mental Fatigue Following Sustained Cognitive Work

Unraveling the interplay between mental workload, occupational fatigue, physiological responses and cognitive performance in office workers

Recently, cognitive demands in workplaces have surged significantly. This study explored the intricate relationship among mental workload (MWL), occupational fatigue, physiological responses, and cognitive performance in office workers by using collective semi-parametric models. One hundred office workers were selected from twenty offices involved in cognitive performance. MWL was assessed through the NASA Task Load Index (NASA-TLX), and occupational fatigue was measured using the Persian version of the Swedish Occupational Fatigue Inventory. Physiological responses, including respiratory rate, the electrical conductivity of the skin (ECS), Heart Rate (HR), and other heart-related parameters, were recorded from the participants during a work shift. Selective and Divided Attention tests were chosen to evaluate workers' cognitive function based on cognitive task analysis. The mean of MWL and occupational fatigue scores were 66.28 ± 11.76 and 1.62 ± 1.07, respectively. There was a significant moderate correlation between two dimensions, mental demand (0.429) and frustration (0.409), with functional fatigue. Also, Significant and, of course, nonlinear relationships were observed between MWL and HR (R2 = 0.44, P-value < 0.001) and ECS (R2 = 0.45, P-value < 0.001) and reaction time in selected (R2 = 0.34, P-value < 0.001) and divided test (R2 = 0.48, P-value < 0.001). Similarly, nonlinear relationships were observed between physiological responses and cognitive performance with fatigue among participants who had experienced higher levels of occupational fatigue. The MWL and fatigue seem to have a significant and non-linear effect on physiological parameters such as HR and ECS and cognitive parameters such as reaction time. Moreover, MWL can influence the dimension of functional fatigue of workers.

Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver's physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness.

Using Body-worn Accelerometers to Detect Physiological Changes During Periods of Blast Overpressure Exposure

Repetitive exposure to non-concussive blast expo-sure may result in sub-clinical neurological symptoms. These changes may be reflected in the neural control gait and balance. In this study, we collected body-worn accelerometry data on individuals who were exposed to repetitive blast overpressures as part of their occupation. Accelerometry features were gener-ated within periods of low-movement and gait. These features were the eigenvalues of high-dimensional correlation matrices, which were constructed with time-delay embedding at multiple delay scales. When focusing on the gait windows, there were significant correlations of the changes in features with the cumulative dose of blast exposure. When focusing on the low-movement frames, the correlation with exposure were lower than that of the gait frames and statistically insignificant. In a cross-validated model, the overpressure exposure was predicted from gait features alone. The model was statistically significant and yielded an RMSE of 1.27 dB. With continued development, the model may be used to assess the physiological effects of repetitive blast exposure and guide training procedures to minimize impact on the individual.

Detecting Parkinson's Disease from Wrist-Worn Accelerometry in the U.K. Biobank

Parkinson's disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.