Assessing Human Feedback Parameters for Disturbance-Rejection

Peterson, Lauren N.; Chou, Amber H.Y.; Burden, Samuel A.; Yamagami, Momona

doi:10.1016/j.ifacol.2023.01.094

Cited by 1 publication

(1 citation statement)

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“…We found that raw gaze delayed the target reference by approximately 250-300 ms and had a phase lag of 0.4 radians below 1 Hz when a participant intentionally gazed at the target. This roughly aligned with the human visuomotor delay in a first-order tracking task [54]. To estimate the target position from gaze position, we removed the phase lag using the Fast Fourier Transform in the frequency domain.…”

Section: Eye Data Pre-processingmentioning

confidence: 99%

Using Eye Gaze to Train an Adaptive Myoelectric Interface

Chou,

Madduri,

et al. 2024

Preprint

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Myoelectric interfaces hold promise in consumer and health applications, but they are currently limited by variable performance across users and poor generalizability across tasks. To address these limitations, we consider interfaces that continually adapt during operation. Although current adaptive interfaces can reduce inter-subject variability, they still generalize poorly between tasks because they make use of task-specific data during training. To address this limitation, we propose a new paradigm to adapt myoelectric interfaces using natural eye gaze as training data. We recruited 11 subjects to test our proposed method on a 2D computer cursor control task using high-density surface EMG signals measured from forearm muscles. We find comparable task performance between our gaze-trained paradigm and the current task-dependent method. This result demonstrates the feasibility of using eye gaze to replace task-specific training data in adaptive myoelectric interfaces, holding promise for generalization across diverse computer tasks.

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Section: Eye Data Pre-processingmentioning

confidence: 99%