Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Application of Myoelectric Prosthesis <s> Technological advances in upper-limb prosthetic design offer dramatically increased possibilities for powered movement. The DEKA Arm system allows users 10 powered degrees of movement. Learning to control these movements by utilizing a set of motions that, in most instances, differ from those used to obtain the desired action prior to amputation is a challenge for users. In the Department of Veterans Affairs "Study to Optimize the DEKA Arm," we attempted to facilitate motor learning by using a virtual reality environment (VRE) program. This VRE program allows users to practice controlling an avatar using the controls designed to operate the DEKA Arm in the real world. In this article, we provide highlights from our experiences implementing VRE in training amputees to use the full DEKA Arm. This article discusses the use of VRE in amputee rehabilitation, describes the VRE system used with the DEKA Arm, describes VRE training, provides qualitative data from a case study of a subject, and provides recommendations for future research and implementation of VRE in amputee rehabilitation. Our experience has led us to believe that training with VRE is particularly valuable for upper-limb amputees who must master a large number of controls and for those amputees who need a structured learning environment because of cognitive deficits. <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Application of Myoelectric Prosthesis <s> Although myoelectric prosthesis has been researched for almost 60 years, a high quality prosthetic arm with dexterous hand manipulation and stable control system is always hard to find and amputee acceptance remains low. One of the major challenges is the lack of a portable and powerful embedded system to implement the electromyography (EMG) pattern recognition (PR) algorithms, other challenges include design of dexterous prosthetic hands, the placement of multichannel electrodes, trade-off between low power consumption and small size and so on. This paper presents our latest progress on EMG PR controlled prosthetic hand. In the system, an armband with 8-channel electrodes is used to acquire the EMG signals. A powerful embedded system which deals with the EMG signals decoding algorithms is introduced. On board EMG training and real-time surface myoelectric signals decoding is implemented in the embedded system to control a 6 degree of freedoms (DOFs) prosthetic hand. The promising results of this work show its potential of speeding up the translation of PR prosthetic arm into daily application. <s> BIB002
The high functionality (multiple DOF) and high accuracy are achieved on testing offline and real-time collected data from amputees. However, when tested by amputees or patients for real-time usability, it does not give the same level of accuracy. Moving from the virtual environment to the real world requires the implementation of prosthetic devices. One of the major challenges that influenced the usability of the prosthesis is the lack of robust and a portable embedded system to implement the EMG-PR algorithms, other challenges include the design of dexterous prosthetic hands, multichannel electrodes placement, compensation between power consumption, small size, etc. BIB002 . Various hardware has been implemented to develop prosthetic hands for persons with disabilities. Hardware chips are designed for filtering EMG signals and other applications such as grasp detection and human-computer interventions to obtain an accurate signal for prosthetic arm control. Furthermore, virtual environments allow the user to practice different controlling gestures that the designated prosthetic device supposed to control in real-world BIB001 . Some of the experimental outcomes on the real-time collected data from amputee, real-time with embedded packages and real-time using virtual reality environment are discussing in the following Sections 4.1-4.3, respectively.