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.

Communication challenges faced by individuals with speech impairments present a unique set of difficulties, often hindering effective interaction. This research paper centers on addressing these challenges by employing ChatGPT, a sophisticated large language model (LLM) developed by OpenAI, within the framework of Human-Computer Interaction (HCI). The study investigates the intricate landscape of speech impairments, emphasizing the inherent complexities in vocal expression. Our work highlights the pivotal role of ChatGPT in offering a tailored and adaptable communication solution.The paper highlights the contributions to HCI principles and assistive technologies, showcasing the innovative integration of ChatGPT. Emphasizing interdisciplinary collaboration, the study positions itself at the forefront of leveraging large language models to provide a comprehensive theoretical framework tailored to the unique needs of individuals with speech impairments.

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%.

We introduce an innovative assistive robotic setup tailored for assessing the cognitive state of individuals with spinal cord injuries (SCI) during their daily activities. Utilizing physiological sensors such as ECG, EEG, and EDA, along with cameras for facial expression, our system is designed to detect and evaluate cognitive fatigue in participants as they engage with a collaborative robot. Specifically, two tasks, Cooking Pasta Sauce and Getting Ready for Work–have been crafted to gather data on cognitive states. Participants interact with the robot using natural language (English) to perform tasks, while their physiological responses, facial expressions, and activities are recorded. The study comprises three phases of cognitive fatigue: baseline, moderate, and severe. Cognitive fatigue is induced through the N-back task paradigm, and its severity is assessed using the Visual Analogue Scale for Fatigue (VAS-F) questionnaire. Our system is designed to intervenes during the tasks based on the detected cognitive fatigue levels. In this paper, we concentrate on validating the cognitive fatigue detection system using only physiological sensors during task performance, achieving an accuracy of 85.7% and a recall of 0.87. We provide detailed insights into the system design and present a preliminary analysis of the gathered data.