Biology subjects

Madduri, M.

Publications and source records attributed to Madduri, M..

2 recordsLinked to original sources

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↗