Decomposition strategy for surface EMG with few channels：a simulation study.

OBJECTIVE: In the specific use of electromyogram (EMG) driven prosthetics, the user's disability reduces the space available for the electrode array. We propose a framework for EMG decomposition adapted to the condition of a few channels (less than 30 observations), which can elevate the potential of prosthetics in terms of cost and applicability. APPROACH: The new framework contains a peel-off approach, a refining strategy for motor unit spike train (MUST) and motor unit action potential (MUAP) and a re-subtracting strategy to adapt the framework to few channels environments. Simulated EMG signals were generated to test the framework. In addition, we quantify and analyze the effect of strategies used in the framework. MAIN RESULTS: The results show that the new algorithm has an average improvement of 19.97% in the number of motor units (MUs) identified compared to the control algorithm. Quantitative analysis of the usage strategies shows that the re-subtracting and refining strategies can effectively improve the performance of the framework under the condition of few channels. SIGNIFICANCE: These prove that the new framework can be applied to few channel conditions, providing a optimization space for neural interface design in cost and user adaptation.

A Semi-Autonomous Hierarchical Control Framework for Prosthetic Hands Inspired by Dual Streams of Human.

The routine use of prosthetic hands significantly enhances amputees' daily lives, yet it often introduces cognitive load and reduces reaction speed. To address this issue, we introduce a wearable semi-autonomous hierarchical control framework tailored for amputees. Drawing inspiration from the visual processing stream in humans, a fully autonomous bionic controller is integrated into the prosthetic hand control system to offload cognitive burden, complemented by a Human-in-the-Loop (HIL) control method. In the ventral-stream phase, the controller integrates multi-modal information from the user's hand-eye coordination and biological instincts to analyze the user's movement intention and manipulate primitive switches in the variable domain of view. Transitioning to the dorsal-stream phase, precise force control is attained through the HIL control strategy, combining feedback from the prosthetic hand's sensors and the user's electromyographic (EMG) signals. The effectiveness of the proposed interface is demonstrated by the experimental results. Our approach presents a more effective method of interaction between a robotic control system and the human.

Modular Bioinspired Hand with Multijoint Rigid-Soft Finger Possessing Proprioception.

Soft robot hands have the advantage of remarkable adaptability for grasping. Especially for the soft and fragile objects, soft fingers had presented their much excellent potential compared with their rigid counterparts. However, less degree of freedom, lower force output, lack of proprioception, and poor controllability still limit the application. Inspired by the anatomical structure of the human hand and following the idea of combining soft joints, rigid skeletons and embedded soft curvature sensors, modular dexterous hands composed of multijoint fingers are proposed in this study. Each finger has three quasi-joints, in which metacarpophalangeal soft-joint can realize adduction/abduction and bending motions, and distal two interphalangeal soft-joints are actuated by one actuator. Similar to human hand, soft-joint so-called quasi-joint has a short length of constant curvature segment. The integrated Indium Gallium Alloy sensors with Kelvin Bridge for proprioception can accurately detect joint angles, while closed-loop control based on proprioception was accomplished. Kinematics and statics modeling method of the rigid-soft finger is proposed. To further verify the performance of this design, prototypes of three-fingered and five-fingered hands are developed. The multifingered hands had demonstrated their capability of adaptive grasp and dexterous manipulation, while the force output of the three-fingered hand is up to 31.82 N, and 32 grasp types had accomplished by the five-fingered hand.

Co-optimization of robotic design and skill inspired by human hand evolution.

During evolution of the human hand, evolutionary morphology has been closely related to behavior in complicated environments. Numerous researchers have revealed that learned skills have affected hand evolution. Inspired by this phenomenon, a co-optimization approach for underactuated hands is proposed that takes grasping skills and structural parameters into consideration. In our proposal, hand design, especially the underactuated mechanism, can be parameterized and shared with all the local agents. These mechanical parameters can be updated globally by the independent agents. In addition, we also train human-like 'feeling' of grasping: grasping stability is estimated in advance before the object drops, which can speed up grasping training. In this paper, our method is instantiated to address the optimization problem for the torsion spring mechanical parameters of an underactuated robotic hand with multi-actuators, and then the optimized results are transferred to the actual physical robotic hand to test the improvement of grasping. This collaborative evolution process leverages the dexterity of the multi-actuators and the adaptivity of the underactuated mechanism.