Design of an Effective Prosthetic Hand System for Adaptive Grasping with the Control of Myoelectric Pattern Recognition Approach

Simulation and experimental study on rope driven artificial hand and driven motor

BACKGROUND

Prosthetic hands have the potential to replace human hands. Using prosthetic hands can help patients with hand loss to complete the necessary daily living actions.

OBJECTIVE

This paper studies the design of a bionic, compact, low-cost, and lightweight 3D printing humanoid hand. The five fingers are underactuated, with a total of 9 degrees of freedom.

METHODS

In the design of an underactuated hand, it is a basic element composed of an actuator, spring, rope, and guide system. A single actuator is providing power for five fingers. And the dynamic simulation is carried out to calculate the motion trajectory effect.

RESULTS

In this paper, the driving structure of the ultrasonic motor was designed, and the structural size of the ultrasonic motor vibrator was determined by modal and transient simulation analysis, which replace the traditional brake, realize the lightweight design of prosthetic hand, improve the motion accuracy and optimize the driving performance of prosthetic hand.

CONCLUSIONS

By replacing traditional actuators with new types of actuators, lightweight design of prosthetic hands can be achieved, improving motion accuracy and optimizing the driving performance of prosthetic hands.

A Review of Myoelectric Control for Prosthetic Hand Manipulation

Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.

A Design of Biomimetic Prosthetic Hand

Many patients with upper limb defects desire myoelectric prosthetic hands, but they are still not used for some reasons. One of the most significant reasons is its external appearance, which has the discomfort caused by the structural difference between a human hand and a robotic link. The structure must be based on human anatomy to create a more natural-looking prosthesis. This study designed a biomimetic prosthetic hand with bones, ligaments, tendons, and multiple muscles based on the human musculoskeletal system. We verified the proposed prosthetic hand using the viscoelastic angle sensor to determine whether it works like a human hand. We also compared the finger force of the prosthetic hand with that of a human finger. It could be capable of controlling the angle and the stiffness of the joint by multiple extensor and flexor muscles, like humans.

Wireless Control Combining Myoelectric Signal and Human Body Communication for Wearable Robots

In this study, a communication module based on human body communication was developed to wirelessly control a wearable robot hand based on myoelectric signals. The communication module adopts 10–60 MHz band and an impulse radio multi-pulse position modulation method to achieve low transmission loss and high data rate. A technique to reduce the module size was developed by sharing the myoelectric signal detection electrode and transmitting electrode, and three receiving electrode structures were investigated to improve signal transmission performance. As a result, the developed communication module provides a packet detection rate of 100% and a bit error rate of less than 10−6 up to at least 110 cm along the arm, and a wearable robot hand was demonstrated to be properly controlled based on a human subject’s myoelectric signals.