This comprehensive review systematically evaluates Machine Learning (ML) methodologies employed in the detection, prediction, and analysis of mental stress and its consequent mental disorders (MDs). Utilizing a rigorous scoping review process, the investigation delves into the latest ML algorithms, preprocessing techniques, and data types employed in the context of stress and stress-related MDs. The findings highlight that Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models consistently exhibit superior accuracy and robustness among all machine learning algorithms examined. Furthermore, the review underscores that physiological parameters, such as heart rate measurements and skin response, are prevalently used as stress predictors in ML algorithms. This is attributed to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. Additionally, 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. The synthesis of this review identifies 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 detection and prediction of stress and stress-related MDs.

Convolution neural networks (CNN) are computation intensive to train. It consists of a substantial number of multidimensional dot products between many kernels and inputs. We observe that there are notable similarities among the vectors extracted from inputs (i.e., input vectors). If one input vector is similar to another one, its computations with the kernels are also similar to those of the other and therefore, can be skipped by reusing the already-computed results. Based on this insight, we propose a novel scheme based on locality sensitive hashing (LSH) to exploit the similarity of computations during CNN training in a hardware accelerator. The proposed scheme, called SIMCNN, uses a cache (SIMCACHE) to store LSH signatures of recent input vectors along with the computed results. If the LSH signature of a new input vector matches with that of an already existing vector in the SIMCACHE, the already-computed result is reused for the new vector. SIMCNN is the first work that exploits computational similarity for accelerating CNN training in hardware. The paper presents a detailed design, workflow, and implementation of SIMCNN. Our experimental evaluation with four different deep learning models shows that SIMCNN saves a significant number of computations and therefore, improves training time up to 43%.