Assessing Fatigue with Multimodal Wearable Sensors and Machine Learning

Fatigue is a loss in cognitive or physical performance due to various physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It has an adverse effect on the human body and can slow down reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditions that can result in physiological complications. However, tools to understand and assess fatigue are minimal. This paper first focuses on building an experimental setup that induces cognitive fatigue (CF) and physical fatigue (PF) through multiple cognitive and physical tasks while simultaneously recording physiological data. Second, self-reported visual analog scores (VAS) from the participants are reported after each task to confirm fatigue induction. Finally, an evaluation system is built that utilizes machine learning (ML) models to detect states of CF and PF from sensor data, thus providing an objective measure. Random Forest performs the best in detecting PF with an accuracy of 80.5% while correctly predicting the true PF condition 88% of the time. On the other hand, the long short-term memory (LSTM) recurrent neural network produces the best results in detecting CF in the subjects (with 84.1% accuracy, 0.9 recall).