Abstract
This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major difficulty of the EMG decoder modeling is to decode EMG signals with high accuracy in real-time. Our recent study presented a switching mechanism for carving up a complex task into simple subtasks and trained different submodels with low nonlinearity. However, it was observed that a "bump" behavior of decoder output (i.e., the discontinuity) occurred during the switching between two submodels. The bumps might cause unexpected impacts on the affected limb and thus potentially injure patients. To improve this undesired transient behavior on decoder outputs, we attempt to maintain the continuity of the outputs during the switching between multiple submodels. A bumpless switching mechanism is proposed by parameterizing submodels with all shared states and applied in the construction of the EMG decoder. Numerical simulation and real-time experiments demonstrated that the bumpless decoder shows high estimation accuracy in both offline and online EMG decoding. Furthermore, the outputs achieved by the proposed bumpless decoder in both testing and verification phases are significantly smoother than the ones obtained by a multimodel decoder without a bumpless switching mechanism. Therefore, the bumpless switching approach can be used to provide a smooth and accurate motion intent prediction from multi-channel EMG signals. Indeed, the method can actually prevent participants from being exposed to the risk of unpredictable loads.
Highlights
I N THE advanced rehabilitation theory, it is generally accepted that users’ involvement is essential in both the therapy procedures and the development of rehabilitation technics [1], especially in rehabilitation robotics [2]
As it is essential to continuously estimate multi-joint motion intention from EMG signals during task switching, we explore a state-shared bumpless transfer for model switching to improve transient performance during switching
The activation reduction is in line with other robot-aided limb movements [39] and suggests that the control algorithm is effective in assisting arm limb motion [39]
Summary
I N THE advanced rehabilitation theory, it is generally accepted that users’ involvement is essential in both the therapy procedures and the development of rehabilitation technics [1], especially in rehabilitation robotics [2]. Comparing with physical sensor-based robot control strategies, strategies using bio-sensors, such as Electroencephalography, Electromyography (EMG) and Electroneurography, for rehabilitation, allow robotic movements to be triggered more naturally and simultaneously based on human motion. Among different bio-signals, the surface EMG signal has attracted much attention, because it is closely related to patients’ muscle activities, and can be collected with noninvasive sensors. For rehabilitation, upper limb motor relearning and recovery levels are required to be improved with proper intensive physiotherapy. For patients with partial motor capacity, it is essential to estimate patients’ motion intention for further assistance. Some studies have proposed methods to detect the motion intention by estimating limb motion in both static [3] and dynamic manners [4]
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