Myoelectric controlled interfaces have become a research interest for use in advanced prostheses, exoskeletons, and robot teleoperation. Current research focuses on improving a user's initial performance, either by training a decoding function for a specific user or implementing "intuitive" mapping functions as decoders. However, both approaches are limiting, with the former being subject specific, and the latter task specific. This paper proposes a paradigm shift on myoelectric interfaces by embedding the human as controller of the system to be operated. Using abstract mapping functions between myoelectric activity and control actions for a task, this study shows that human subjects are able to control an artificial system with increasing efficiency by just learning how to control it. The method efficacy is tested by using two different control tasks and four different abstract mappings relating upper limb muscle activity to control actions for those tasks. The results show that all subjects were able to learn the mappings and improve their performance over time. More interestingly, a chronological evaluation across trials reveals that the learning curves transfer across subsequent trials having the same mapping, independent of the tasks to be executed. This implies that new muscle synergies are developed and refined relative to the mapping used by the control task, suggesting that maximal performance may be achieved by learning a constant, arbitrary mapping function rather than dynamic subject- or task-specific functions. Moreover, the results indicate that the method may extend to the neural control of any device or robot, without limitations for anthropomorphism or human-related counterparts.
The development of a portable assistive device to aid patients affected by
neuromuscular disorders has been the ultimate goal of assistive robots since the
late 1960s. Despite significant advances in recent decades, traditional rigid
exoskeletons are constrained by limited portability, safety, ergonomics,
autonomy and, most of all, cost. In this study, we present the design and
control of a soft, textile-based exosuit for assisting elbow flexion/extension
and hand open/close. We describe a model-based design, characterisation and
testing of two independent actuator modules for the elbow and hand,
respectively. Both actuators drive a set of artificial tendons, routed through
the exosuit along specific load paths, that apply torques to the human joints by
means of anchor points. Key features in our design are under-actuation and the
use of electromagnetic clutches to unload the motors during static posture.
These two aspects, along with the use of 3D printed components and off-the-shelf
fabric materials, contribute to cut down the power requirements, mass and
overall cost of the system, making it a more likely candidate for daily use and
enlarging its target population. Low-level control is accomplished by a
computationally efficient machine learning algorithm that derives the system’s
model from sensory data, ensuring high tracking accuracy despite the
uncertainties deriving from its soft architecture. The resulting system is a
low-profile, low-cost and wearable exosuit designed to intuitively assist the
wearer in activities of daily living.
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