Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami, Momona; Peterson, Lauren N.; Howell, Darrin; Roth, Eatai; Burden, Samuel A.

doi:10.1109/tcyb.2021.3110187

Cited by 9 publications

(19 citation statements)

References 44 publications

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“…We constructed target references as sums of sinusoidal signals with random phases to create a pseudorandom target trajectory. The sinusoidal signals were designed to be at frequencies of prime multiples of the base frequency (0.05 Hz) below 1 Hz [58, 68]. The signals were at 0.1, 0.25, 0.55, 0.85 Hz in the horizontal direction and 0.15, 0.35, 0.65, 0.95 Hz in the vertical direction.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

Using Eye Gaze to Train an Adaptive Myoelectric Interface

Chou,

Madduri,

et al. 2024

Preprint

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“…We adopted a 1-degree-of-freedom (DOF) continuous human/machine task previously conducted by Yamagami et al (2021). Human users H are tasked with controlling a second-order linear-time invariant (LTI) machine dynamics M : …”

Section: Methodsmentioning

confidence: 99%

“…We constructed pseudorandom reference r and disturbance d as sums of sinusoidal signals interleaved at the stimulated frequencies (Yamagami et al, 2021; Yu et al, 2014). Stimulated frequencies Ω were designed to be eight prime multiples of the base frequency (0.05 Hz) below 1 Hz, where Ω = {0.1, 0.15, 0.25, 0.35, 0.55, 0.65, 0.85, 0.95 Hz}.…”

Section: Methodsmentioning

confidence: 99%

“…Given a LTI machine M and stimuli at specific frequencies, we can assume that humans H behave approximately like an LTI transformation (Yamagami et al, 2021). Hence the output can be written as linear combination in response to reference and disturbance in the output space : …”

Section: Methodsmentioning

confidence: 99%

“…In addition to the analyses on performance in tracking/rejecting the stimuli, we analyzed the human sensorimotor noise. To compute the human sensorimotor noise as the imputed disturbance , we first estimated the feedback controller (Yamagami et al, 2021): …”

Section: Methodsmentioning

confidence: 99%

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Evaluating a Human/Machine Interface with Redundant Motor Modalities for Trajectory-Tracking

Chou

Yamagami

Burden

2022

Preprint

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In human/machine interfaces (HMI), humans can interact with dynamic machines through a variety of sensory and motor modalities. Redundant motor modalities are known to have advantages in both human sensorimotor control and human-computer interaction: motor redundancy in sensorimotor control provides abundant solutions to achieve tasks; and incorporating diverse features from different modalities has improved the performance of gesture-, movement-, and brain-controlled computer interfaces. Our objective is to investigate whether redundant motor modalities enhance performance for a continuous trajectory-tracking task. We designed a multimodal human/machine interface with combined manual (joystick) and muscle (surface electromyography, sEMG) inputs and evaluated its closed-loop performance for tracking trajectories through second-order machine dynamics. In a human subjects experiment with 15 participants, we found that the multimodal interface outperformed the manual-only interface while performing comparably to the muscle-only interface; and that the multimodal interface enabled users to coordinate sensorimotor noise in individual modalities to improve performance. Multimodal human/machine interfaces could be beneficial in systems that require stability and robustness against perturbations such as motor rehabilitation and robotic manipulation.

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Right-left hand asymmetry in manual tracking: when poorer control is associated with better adaptation and interlimb transfer

Coudière

Rugy

Danion

2023

Psychological Research

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To date, interlimb transfer following visuomotor adaptation has been mainly investigated through discrete reaching movements. Here we explored this issue in the context of continuous manual tracking, a task in which the contribution of online feedback mechanisms is crucial, and in which there is a well-established right (dominant) hand advantage under baseline conditions. We had two objectives 1) to determine whether this preexisting hand asymmetry would persist under visuomotor rotation, 2) to examine interlimb transfer by assessing whether prior experience with the rotation by one hand benefit to the other hand. To address these, 44 right-handed participants were asked to move a joystick and to track a visual target following a rather unpredictable trajectory. Visuomotor adaptation was elicited by introducing a 90° rotation between the joystick motion and the cursor motion. Half of the participants adapted to the rotation first with the right hand, and then with the left, while the other half performed the opposite protocol. As expected during baseline trials, the left hand was less accurate while also exhibiting more variable and exploratory behavior. However, participants exhibited a left hand advantage during first exposure to the rotation. Moreover, interlimb transfer was observed albeit more strongly from the left to the right hand. We suggest that the less effective and more variable/exploratory control strategy of the left hand promoted its adaptation, which incidentally favored transfer from left to right hand. Altogether, this study speaks for further attention to the dominant/non-dominant asymmetry during baseline before examining interlimb transfer of adaptation.

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Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Cited by 9 publications

References 44 publications

Using Eye Gaze to Train an Adaptive Myoelectric Interface

Using Eye Gaze to Train an Adaptive Myoelectric Interface

Evaluating a Human/Machine Interface with Redundant Motor Modalities for Trajectory-Tracking

Right-left hand asymmetry in manual tracking: when poorer control is associated with better adaptation and interlimb transfer

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