Detecting Sequential Human Mental Workload Using UNet with ContinuityAware Loss Applied to Streamed Physiological Signals Human mental workload perception is an important problem with multiple applications in the areas of industry, transportation, military and medical contexts. In this paper, we present a deep learning model developed for detecting sequential driver mental workload using physiological signals. We modify a fully convolutional UNet in order to learn from multiple temporal physiological signals as inputs. We also demonstrate that the elementwise crossentropy loss function, which has been adopted by many other deep learning architectures for classification or segmentation purposes, is not sufficient for the sequential workload segmentation because it treats each data point as an independent classification object while ignoring the neighboring relation between the points. We introduce a new loss function that is sensitive to local continuity property in workload sequence. The proposed method is evaluated on two sets of data collected by wearable sensors to record multiple physiological signals. The effectiveness of an introduced scalar used to control the influence of the continuityaware loss is investigated. Our experiments show that the proposed UNet is effective in detecting sequential human mental workload. Furthermore, when the proposed continuityaware loss is combined with the elementwise binary crossentropy loss with the properly selected controlling scalar value, the quality of the predicted mental workload segments is significantly improved.

Detection of driver stress in realworld driving environment using physiological signals Detection of driver stress is an important component in many ADASs (Advanced DriverAssistance Systems), and a challenging problem when it is applied to realworld driving environment. In this paper, we present a convolutional neural network (CNN) designed to detect driverxe2x80x99s stress levels with four physiological signals, i.e., heart rate, heart rate variability, breathing rate, and galvanic skin response. The proposed model is shift invariant and is capable of handling the imbalanced data set issue. The performances of the proposed models are evaluated using realworld driving data in three different types of driver stress detection tasks, i.e., the singledriver, the cohorts of drivers, and alldriver stress detection task respectively. The experimental results demonstrate that the proposed model is capable of reliably detecting the driverxe2x80x99s stress levels. More importantly we demonstrate that a model can be trained on the data collected from drivers with similar cognitive capabilities and then generalized to new drivers with similar cognitive capabilities for stress detection.