Sai Zhang

Profile

Project Title:

Summary: Computer users often experience fluctuations in alertness that can affect their work safety, efficiency, and satisfaction. To address this issue, we aim to conduct a series of cognitive neuroscience experiments that measure drowsiness and mental fatigue during human-computer interaction (HCI). With the recent devel- opment of machine learning algorithms and also as the next step, we plan to propose a machine learning pipeline to detect neurophysiological fluctuations in alertness using datastream from video cameras, key- boards, and mice. To justify the effectiveness of such approach, we will further examine whether these models can detect drowsiness and mental fatigue during naturalistic human-computer interaction in real- world settings.

The main objective of this proposal is three-fold:

1) Identify patterns in human behavior that can be used to determine alertness during human-computer interaction

2) Develop an algorithm that can detect these patterns and thereby measure alertness

3) Verify the effectiveness of this approach in detecting alertness in real-world settings. Our goal is to obtain basic findings that can be used to develop alertness monitoring systems for computer users. These findings will help improve work satisfaction and mental wellbeing by allowing users to take breaks and return to work with a refreshed mind.

Supervisor:

• Dr Valdas Noreika

Research