Myoelectric control systems have advanced over decades, but their performance remains a challenge. Adaptive algorithms have emerged as a possible solution; however, the user is also always learning, causing the resulting system's performance to sometimes be subpar and even unstable. Most studies approximate these co-adaptive systems as combinations of deterministic and stochastic linear systems. Instead, we propose that chaos, a subtype of nonlinear dynamical systems, is required to explain the intermittent performance of myoelectric control systems. Slight, unknown, changes to a chaotic system can shift its dynamics between stable and unstable. In this work, a 10-day co-adaptive myoelectric control experiment was designed to explore this chaotic behaviour. Poincaré mapping, a technique used to visualize chaos, was used to observe how changes to experimental parameters influence the stability of co-adaptive systems and user learning. A better understanding of the dynamics of co-adaptive human-machine interfaces may enable the design and control of more robust systems in the future.
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