Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR.

Cognitive fatigue, a state of reduced mental capacity arising from prolonged cognitive activity, poses significant challenges in various domains, from road safety to workplace productivity. Accurately detecting and mitigating cognitive fatigue is crucial for ensuring optimal performance and minimizing potential risks. This paper presents a comprehensive survey of the current landscape in cognitive fatigue detection. We systematically review various approaches, encompassing physiological, behavioral, and performance-based measures, for robust and objective fatigue detection. The paper further analyzes different challenges, including the lack of standardized ground truth and the need for context-aware fatigue assessment. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and address the multifaceted challenge of cognitive fatigue detection.

Cognitive Fatigue (CF) is the decline in cognitive abilities due to prolonged exposure to mentally demanding tasks. In this paper, we used gait cycle analysis, a biometric method related to human locomotion to identify cognitive fatigue in individuals. The proposed system in this paper takes two asynchronous videos of the gait of individuals to classify if they are cognitively fatigued or not. We leverage the pose estimation library OpenPose, to extract the body keypoints from the frames in the videos. To capture the spatial and temporal information of the gait cycle, a CNN-based model is used in the system to extract the embedded features which are then used to classify the cognitive fatigue level of individuals. To train and test the model, a gait dataset is built from 21 participants by collecting walking data before and after inducing cognitive fatigue using clinically used games. The proposed model can classify cognitive fatigue from the gait data of an individual with an accuracy of 81%.

Physical activities, according to the embodied cognition theory, are an important manifestation of cognitive functions. As a result, in this paper, the Activate Test of Embodied Cognition (ATEC) system is proposed to assess various cognitive measures. It consists of physical exercises with different variations and difficulty levels designed to provide assessment of executive and motor functions. This work focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. A self-supervised approach is employed in this work that can exploit a small set of annotated data to obtain an effective human activity representation. The performance of different self-supervised approaches along with a supervised method are investigated for automated cognitive assessment of children performing ATEC tasks. The results show that the supervised learning approach performance decreases as the training set becomes smaller, whereas the self-supervised methods maintain their performance by taking advantage of unlabeled data.