Biology subjects

Yamagami, M.

Publications and source records attributed to Yamagami, M..

4 recordsLinked to original sources

bioRxiv2024

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework allowed us to characterize user and decoder changes within co-adaptive myoelectric interfaces. Our framework further allowed us to predict how decoder algorithm changes impacted co-adaptive interface performance and revealed how interface properties can shape user behavior. Our findings demonstrate an experimentally-validated computational framework that can be used to design user-decoder interactions in closed-loop, co-adaptive neural interfaces. This framework opens future opportunities to optimize co-adaptive neural interfaces to expand the performance and application domains for neural interfaces.

neuroscience↗

bioRxiv2023

Co-adaptation improves performance in a dynamic human-machine interface

Despite the growing prevalence of learning algorithms in daily life, methods for analysis and synthesis of how these systems interact with people are limited. We studied optimization-based algorithms that co-adapt with people in the presence of dynamic machines, finding limitations on current theory that motivated us to conduct an experiment where human subjects interact through a dynamic interface with a machine that has complex dynamics. Experimental results provided evidence of co-adaptation and a trade-off between performance and the "effort" of the human and interface, defined as the norm of their output signals. We developed a parsimonious model of the human adaptation strategy observed in our experiments and conducted a simulation study using this model. Our computational results matched the empirical results, suggesting our human subjects adapted to minimize a combination of error and effort. These results demonstrate how co-adaptation between humans and intelligent interfaces shapes behavior and performance, and introduces a modeling framework that can be used in future work to systematically design interaction outcomes.

bioengineering↗

bioRxiv2022

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

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 movement-, gesture-, 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 individual modalities to attenuate noise. Multimodal human/machine interfaces could be beneficial in systems that require stability and robustness against perturbations such as motor rehabilitation and robotic manipulation.

bioengineering↗

bioRxiv2020

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

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feed-forward) control. This paper studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise) explains observed performance improvements.

neuroscience↗