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Biology subjects

Hargrove, L.

Publications and source records attributed to Hargrove, L..

3 recordsLinked to original sources

Effects of Uni- and Bidirectional Interaction During Dyadic Ankle and Wrist Tracking

Haptic human-robot-human interaction allows users to feel and respond to one anothers forces while interfacing with separate robotic devices, providing customizable infrastructure for studying physical interaction during motor tasks (i.e., physical rehabilitation). For both upper- and lower-limb tasks, previous work has shown that virtual interactions with a partner can improve motor performance and enhance individual learning. However, whether the mechanism of these improvements generalizes across different human systems is an open question. In this work, we investigate the effects of haptic interaction between healthy individuals during a trajectory tracking task involving single-joint movements at the wrist and ankle. We compare tracking performance and muscle activation during haptic conditions where pairs of participants were uni- and bidirectionally connected, in order to investigate the contribution of real-time responses from a partner during the interaction. Findings indicate similar improvements in tracking performance during the bidirectional interaction for both the wrist and ankle, despite significant differences in how individuals modulated co-contraction. For each joint, bidirectional and unidirectional interaction resulted in similar improvements for the worse partner in the dyad. For the better partner, bidirectional interaction outperformed unidirectional interaction, likely due to changes in movement planning that were not observed in the unidirectional condition. While these results suggest that unidirectional interaction is sufficient for error correction of less skilled individuals during simple motor tasks, they also highlight the mutual benefits of bidirectional interaction which are consistent across the upper and lower limbs.

bioengineering↗

Should bionic limb control mimic the human body? Impact of control strategy on bionic hand skill learning

A longstanding engineering ambition has been to design anthropomorphic bionic limbs: devices that look like and are controlled in the same way as the biological body (biomimetic). The untested assumption is that biomimetic motor control enhances device embodiment, learning, generalization, and automaticity. To test this, we compared biomimetic and non-biomimetic control strategies for able-bodied participants when learning to operate a wearable myoelectric bionic hand. We compared motor learning across days and behavioural tasks for two training groups: Biomimetic (mimicking the desired bionic hand gesture with biological hand) and Arbitrary control (mapping an unrelated biological hand gesture with the desired bionic gesture). For both trained groups, training improved bionic limb control, reduced cognitive reliance, and increased embodiment over the bionic hand. Biomimetic users had more intuitive and faster control early in training. Arbitrary users matched biomimetic performance later in training. Further, arbitrary users showed increased generalization to a novel control strategy. Collectively, our findings suggest that biomimetic and arbitrary control strategies provide different benefits. The optimal strategy is likely not strictly biomimetic, but rather a flexible strategy within the biomimetic to arbitrary spectrum, depending on the user, available training opportunities and user requirements.

neuroscience↗

Artificial Joint Speed Feedback for Myoelectric Prosthesis Control

I.Accurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the users intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty - joint speed. In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found modest improvement in overall reaching errors after perturbed control, and that high prosthesis control noise was compensated for by strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.

neuroscience↗