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Abstract: Limb amputation can cause severe functional disability for the performance of activities of daily living (ADLs). Amputees use prosthetic devices on a regular basis to perform ADLs. Prosthetic devices require substantial amount of cognitive resources, which can lead to device rejection. However, prior research was mainly focused on measuring physical performance of using prosthetic devices. Assessing cognitive workload of prostheses is critical to ensure device usability. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features, metrics, and tasks. The proposed algorithms can help manufacturers and clinicians predict cognitive workload of future EMG-based prosthetic devices in early design phases.
Bio: Maryam Zahabi is an assistant professor in the Wm Michael Barnes ’64 department of industrial and systems engineering at Texas A&M University. Her research focuses on human performance modeling with applications in assistive technologies. She received her PhD in industrial and systems engineering from North Carolina State University in 2017. Dr. Zahabi’s research has received support from agencies including the NSF, DARPA, and U.S. DOT. She is also the recipient of the 2021 NSF CAREER Award. Dr. Zahabi has published over 35 journal papers in the human factors area and serves an associate editor for IEEE Transactions on Human-Machine Systems journal.