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

Measurement of muscle contraction timing for prosthesis control : a comparison between electromyography and force-myography

Active hand prostheses are usually controlled by electromyography (EMG) signals acquired from few muscles available in the residual limb. In general, it is necessary to estimate the envelope of the EMG in real-time to implement the control of the prosthesis. Recently, sensors based on Force Sensitive Resistor (FSR) proved to be a valid alternative to monitor muscle contraction. However, FSR-based sensors measure the mechanical phenomena related to muscle contraction rather than those electrical. The aim of this study is to test the difference between the EMG and force signal in controlling a prosthetic hand. Particular emphasis has been placed on verify the prosthesis activation speed and their application to fast grabbing hand prosthesis as the “Federica” hand. Indeed, there is an intrinsic electro-mechanical delay during muscle contraction, since the electrical activation of muscle fibres always precedes their mechanical contraction. However, the EMG signal needs to be processed to control prosthesis and such filtering unavoidably causes a delay. On the contrary the force signal doesn’t need any processing. Both EMG and force signals were simultaneously recorded from the flexor carpi ulnaris muscle, while subject performed wrist flexions. The raw EMG signals were rectified and low-pass filtered to extract their envelopes. Different widespread operators were used: Moving Average, Root Mean Square, Butterworth low-pass; the cut-off frequency was set to 5 Hz. Afterward, a classic double threshold method was used to compute the muscle contraction onsets (i.e. the signal should exceed a threshold level for a certain time period). Results showed that the lag introduced by the low-pass filtering of the rectified EMG, generates delays greater than those associated with the force sensor. This analysis confirms the possibility of using force sensors as a convenient alternative to EMG signals in the control of prostheses