Background: Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs. Objective: This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs. Methods: Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs. Results: A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. Conclusions: The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people’s routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus’s spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle’s effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers’ safety in the pandemic situation.

(1) Background: Physiological responses, such as heart rate and heart rate variability, have been increasingly utilized to monitor, detect, and predict mental stress. This review summarizes and synthesizes previous studies which analyzed the impact of mental stress on heart activity as well as mathematical, statistical, and visualization methods employed in such analyses. (2) Methods: A total of 119 articles were reviewed following the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Non-English documents, studies not related to mental stress, and publications on machine learning techniques were excluded. Only peer-reviewed journals and conference proceedings were considered. (3) Results: The studies revealed that heart activities and behaviors changed during stressful events. The majority of the studies utilized descriptive statistical tests, including t-tests, analysis of variance (ANOVA), and correlation analysis, to assess the statistical significance between stress and non-stress events. However, most of them were performed in controlled laboratory settings. (4) Conclusions: Heart activity shows promise as an indicator for detecting stress events. This review highlights the application of time series techniques, such as autoregressive integrated moving average (ARIMA), detrended fluctuation analysis, and autocorrelation plots, to study heart rate rhythm or patterns associated with mental stress. These models analyze physiological data over time and may help in understanding acute and chronic cardiovascular responses to stress.

The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

Neural network training is inherently sequential where the layers finish the forward propagation in succession, followed by the calculation and back-propagation of gradients (based on a loss function) starting from the last layer. The sequential computations significantly slow down neural network training, especially the deeper ones. Prediction has been successfully used in many areas of computer architecture to speed up sequential processing. Therefore, we propose ADA-GP, which uses gradient prediction adaptively to speed up deep neural network (DNN) training while maintaining accuracy. ADA-GP works by incorporating a small neural network to predict gradients for different layers of a DNN model. ADA-GP uses a novel tensor reorganization method to make it feasible to predict a large number of gradients. ADA-GP alternates between DNN training using backpropagated gradients and DNN training using predicted gradients. ADA-GP adaptively adjusts when and for how long gradient prediction is used to strike a balance between accuracy and performance. Last but not least, we provide a detailed hardware extension in a typical DNN accelerator to realize the speed up potential from gradient prediction. Our extensive experiments with fifteen DNN models show that ADA-GP can achieve an average speed up of 1.47 × with similar or even higher accuracy than the baseline models. Moreover, it consumes, on average, 34% less energy due to reduced off-chip memory accesses compared to the baseline accelerator.

Modern computers rely heavily on caches to achieve higher performance. Unfortunately, a cache indexing scheme can often cause an uneven distribution of addresses across cache sets resulting in many evictions of useful cache blocks. To address this issue, we propose SmartIndex , a self-optimized indexing scheme that leverages machine learning to actively learn the memory access pattern and dynamically adjust indexes to evenly distribute the cache lines across all sets in the cache, thereby reducing cache misses. Experimental results on a set of 26 memory-intensive applications show that for non-uniform applications, SmartIndex can reduce the misses per kilo instructions (MPKI) of a direct mapped cache by up to 39%, translating into an IPC speedup of 7.23% compared to the conventional power-of-two indexing scheme. Our experiments also show that SmartIndex can work with any cache associativity.