Biology subjects

Burden, S. A.

Publications and source records attributed to Burden, S. A..

8 recordsLinked to original sources

bioRxiv2025

Quantifying human adaptation to a novel split-belt walking condition after broad experience at different belt speeds

Humans can adapt their gait to minimize energy cost when given sufficient exposure to novel energy landscapes. However, it remains unclear whether broad experience in one energy landscape is sufficient to initiate continuous optimization when encountering similar but distinct conditions. In this study, we used visual biofeedback to guide 15 participants to broadly explore walking gaits with different step length asymmetries (SLAs) while walking on a split-belt treadmill with a large split-belt ratio. We subsequently tested how participants adapted their walking patterns during free exploration trials at the large split-belt ratio and at a new, smaller split-belt ratio. Our results showed that during guided exploration, participants were exposed to energetically favorable conditions. However, participants did not self-select walking patterns that minimized their metabolic cost during either free exploration trial. When first exposed to the smaller split-belt ratio, participants immediately adjusted their leg swing distances and belt contact times while maintaining similar step lengths. Throughout this trial, they continued adapting by significantly increasing step lengths on the fast belt. Taken together, our results suggest that participants actively modified their gait strategy when exposed to an energy landscape distinct from the one in which they gained broad experience.

neuroscience↗

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↗

bioRxiv2024

Using Eye Gaze to Train an Adaptive Myoelectric Interface

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. CCS Concepts* Human-centered computing [->] Interaction devices; Empirical studies in HCI.

bioengineering↗

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

A Game-Theoretic Model for Co-Adaptive Brain-Machine Interfaces

Co-adaptation in brain-machine interfaces (BMIs) can improve performance and facilitate user learning. We propose and analyze a mathematical model for co-adaptation in BMIs. We model the brain and the decoder as strategic agents who seek to minimize their individual cost functions, leading to a game-theoretic formulation of interaction. We frame our BMI model as a potential game to identify stationary points (Nash equilibria) of the brain-decoder interactions, which correspond to points at which both the brain and the decoder stop adapting. Assuming the brain and the decoder adapt using gradientbased schemes, we analytically show how convergence to these equilibria depends on agent learning rates. This theoretical framework presents a basis for simulating co-adaption using dynamic game theory and can be extended to tasks with multiple dimensions and to different decoder models. This framework can ultimately be used to inform adaptive decoder design to shape brain learning and optimize BMI performance.

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↗

bioRxiv2020

Predicting walking response to ankle exoskeletons using data-driven models

IIAO_SCPLOWBSTRACTC_SCPLOWDespite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model - phase-varying (PV), linear phase-varying (LPV), and nonlinear phase-varying (NPV) - leveraged Floquet Theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For twelve unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV models predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee, and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven phase-varying models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

systems biology↗