KOPOS: A FRAMEWORK TO STUDY AND DETECT PHYSICAL AND COGNITIVE FATIGUE CONCURRENTLY

Abstract

Fatigue is one of the most prevalent phenomena in human beings, and yet its detection is highly subjective and poorly understood. The phenomenon of fatigue has a huge impact on performance, the ability to execute tasks safely and correctly, and the ability to retain or secure a job. Fatigue can be classified into two types: physical and cognitive fatigue. Physical fatigue may occur due to excessive physical exertion, while cognitive fatigue may occur due to excessive mental exertion. Historically, these two types of fatigue have been studied independently. However, in the real world, although these often occur at the same time, it has not always been easy to distinguish them or to understand which type of fatigue impacts the other. Therefore, it is important to study them together rather than separately in order to enable effective intervention and rehabilitation, regardless of the type of fatigue. This thesis presents a system designed to aid in human-centered studies that focus on the intervention of fatigue. It proposes a system that can detect both types of fatigue using the same setup. The system monitors the state of the body using physiological wearable sensors, which help us analyze the changes in the body due to fatigue. Using this multi-sensory approach, the system detects both physical and cognitive fatigue while collecting contextual data that help understand and predict occurrences of fatigue.

The sensors employed vary: some are specific for detecting cognitive fatigue, while some are specific for detecting physical fatigue. Others can be used to detect both types. This creates a computational challenge in terms of sensor fusion and data processing. To address this, the system uses a Dynamic Data Driven Application System (DDDAS) paradigm and context-aware fusion techniques. These techniques allow the system to handle the different combinations of sensors and detect fatigue with higher accuracy. The main contributions of this thesis can be summarized in the following four points.

• It proposes a framework to detect and help study and analyze the two types of fatigue.

• The proposed system addresses the sensor fusion challenges associated with different combinations of sensors for each type of fatigue. • The system takes into account the human behavior/activity context in which the data is collected. • The proposed system was evaluated using data from a user study that was conducted.