Myoelectric control offers a direct interface between human intent and various robotic applications through recorded muscle activity. Traditional control schemes realize this interface through direct mapping or pattern recognition techniques. The former approach provides reliable control at the expense of functionality, while the latter increases functionality at the expense of long-term reliability. An alternative approach, using concepts of motor learning, provides session-independent simultaneous control, but previously relied on consistent electrode placement over biomechanically independent muscles. This paper extends the functionality and practicality of the motor learning-based approach, using high-density electrode grids and muscle synergy-inspired decomposition to generate control inputs with reduced constraints on electrode placement. The method is demonstrated via real-time simultaneous and proportional control of a 4-DoF myoelectric interface over multiple days. Subjects showed learning trends consistent with typical motor skill learning without requiring any retraining or recalibration between sessions. Moreover, they adjusted to physical constraints of a robot arm after learning the control in a constraint-free virtual interface, demonstrating robust control as they performed precision tasks. The results demonstrate the efficacy of the proposed man-machine interface as a viable alternative to conventional control schemes for myoelectric interfaces designed for long-term use.

Of particular interest to the neuroscience and robotics communities is the understanding of how two humans could physically collaborate to perform motor tasks such as holding a tool or moving it across locations. When two humans physically interact with each other, sensory consequences and motor outcomes are not entirely predictable as they also depend on the other agent’s actions. The sensory mechanisms involved in physical interactions are not well understood. The present study was designed (1) to quantify human–human physical interactions where one agent (“follower”) has to infer the intended or imagined—but not executed—direction of motion of another agent (“leader”) and (2) to reveal the underlying strategies used by the dyad. This study also aimed at verifying the extent to which visual feedback (VF) is necessary for communicating intended movement direction. We found that the control of leader on the relationship between force and motion was a critical factor in conveying his/her intended movement direction to the follower regardless of VF of the grasped handle or the arms. Interestingly, the dyad’s ability to communicate and infer movement direction with significant accuracy improved (>83%) after a relatively short amount of practice. These results indicate that the relationship between force and motion (interpreting as arm impedance modulation) may represent an important means for communicating intended movement direction between biological agents, as indicated by the modulation of this relationship to intended direction. Ongoing work is investigating the application of the present findings to optimize communication of high-level movement goals during physical interactions between biological and non-biological agents.

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Proprioception refers to the senses of body position, movement, force and effort. Previous studies have demonstrated workspace and direction-dependent differences in arm proprioceptive sensitivity within the horizontal plane. In addition, studies of reaching in the vertical plane have shown that proprioception plays a key role in anticipating arm configuration dependent effects of gravity. This suggests that proprioceptive sensitivity could vary with the direction of arm displacement relative to the gravitational vector, as well as with arm configuration. To test these hypotheses, and to characterize proprioception more generally, we assessed the direction-dependence and arm postural-dependence of proprioceptive sensitivity in 3D space using a novel robotic paradigm. A subject’s right arm was coupled to a 7-df robot through a trough that stabilized the wrist and forearm, allowing for changes in configuration largely at the elbow and shoulder. Sensitivity was evaluated using a “same-different” task, where the subject’s hand was moved 1–4 cm away from an initial “test” position to a 2nd “judgment” position. The proportion of trials where subjects responded “different” when the positions were different (“hit rate”), and where they responded “different” when the positions were the same, (“false alarm rate”), were used to calculate d’, a measure of sensitivity derived from signal detection theory (SDT). Initially, a single initial arm posture was used and displacements were performed in six directions: upward, downward, forward, backward, leftward and rightward of the test position. In a follow-up experiment, data were obtained for four directions and two initial arm postures. As expected, sensitivity (d’) increased monotonically with distance for all six directions. Sensitivity also varied between directions, particularly at position differences of 2 and 3 cm. Overall, sensitivity reached near maximal values in this task at 2 cm for the leftward/rightward directions, 3 cm for upward/forward and 4 cm for the downward/backward directions. In addition, when data were grouped together for opposing directions, sensitivity showed a dependence upon arm posture. These data suggest arm proprioceptive sensitivity is both anisotropic in 3D space and configuration-dependent, which has important implications for sensorimotor control of the arm and human-robot interactions.

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