Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Cost effective electro-resistive band based myo activated prosthetic upper limb for amputees in the developing world

In several developing areas of the world, many amputees have no access to any assistive devices for a normal living. The complexity and huge cost of current prosthetics in the market limit the reach and its applications to these underserved people. The goal of this work is to design a low-cost and efficient myo activated prosthetic upper limb for persons with disabilities who are living in resource-limited areas. The design of the hand is composed of two fingers which are made up of low-cost material and using rapid prototyping methods. The surface EMG signals are captured through wearable electro-resistive bands (ERBs) connected to the amputee's residual arm. Using an Arduino microcontroller, the signals are processed to drive a DC motor for open and close of hand. The design aims to achieve reduced power, lower weight, lower maintenance cost and ease of assembly