Sensory motor remapping of space in human-machine interfaces

Department of Physiology, Northwestern University, Chicago, Illinois, USA.
Progress in brain research (Impact Factor: 2.83). 12/2011; 191:45-64. DOI: 10.1016/B978-0-444-53752-2.00014-X
Source: PubMed


Studies of adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. These studies have also pointed out that adaptation to novel dynamics is aimed at preserving the trajectories of a controlled endpoint, either the hand of a subject or a transported object. We review some of these experiments and present more recent studies aimed at understanding how the motor system forms representations of the physical space in which actions take place. An extensive line of investigations in visual information processing has dealt with the issue of how the Euclidean properties of space are recovered from visual signals that do not appear to possess these properties. The same question is addressed here in the context of motor behavior and motor learning by observing how people remap hand gestures and body motions that control the state of an external device. We present some theoretical considerations and experimental evidence about the ability of the nervous system to create novel patterns of coordination that are consistent with the representation of extrapersonal space. We also discuss the perspective of endowing human-machine interfaces with learning algorithms that, combined with human learning, may facilitate the control of powered wheelchairs and other assistive devices.

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    • "Alternatively, recent works have supported a shift in myoelectric control applications towards human-embedded controllers learned through interaction with a constant mapping function associating sEMG inputs with control outputs (Antuvan et al., 2014). Mussa-Ivaldi et al. (2011) propose that the human motor system is capable of learning novel inverse mappings relating the effect of motor commands on control outputs while interacting with myoelectric interfaces. This learning has been modeled and verified in the presence of closed-loop feedback (Radhakrishnan et al., 2008; Chase et al., 2009; Héliot et al., 2010), allowing users to perform tasks simply by learning controls in a given task space (Mosier et al., 2005; Liu and Scheidt, 2008; Liu et al., 2011; Pistohl et al., 2013). "
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    • "Although intuitive decoders give better initial performance, other decoders with worse initial performance are capable of higher learning rates. Further, Mussa-Ivaldi et al. [20] propose that the human motor system attempts to uncover the novel inverse map relating the effect of motor commands on task-relevant variables through learning. This suggests that humans, while learning to perform a task with a novel control space, tend to explore the full space in order to form a complete inverse model. "
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