Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> The purpose of this study was to develop a real-time electromyogram (EMG) discrimination system to provide control commands for man-machine interface applications. A host computer with a plug-in data acquisition and processing board containing a TMS320 C31 floating-point digital signal processor was used to attain real-time EMG classification. Two-channel EMG signals were collected by two pairs of surface electrodes located bilaterally between the sternocleidomastoid and the upper trapezius. Five motions of the neck and shoulders were discriminated for each subject. The zero-crossing rate was employed to detect the onset of muscle contraction. The cepstral coefficients, derived from autoregressive coefficients and estimated by a recursive least square algorithm, were used as the recognition features. These features were then discriminated using a modified maximum likelihood distance classifier. The total response time of this EMG discrimination system was achieved about within 0.17 s. Four able bodied and two C5/6 quadriplegic subjects took part in the experiment, and achieved 95% mean recognition rate in discrimination between the five specific motions. The response time and the reliability of recognition indicate that this system has the potential to discriminate body motions for man-machine interface applications. <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 EMG-PR with Embedded System <s> This paper presents an analog-signal processor chip design for its application on the wireless bio-signal transmission system. The processor is used to handle the physiological signal such as the electrocardiography (ECG), electroencephalography (EEG), or the electromyography (EMG), and so on. All analog blocks in the processor chip are realized in a 0.5 /spl mu/m double-poly and double-metal CMOS technology. The measured results show the processor chip can capture the bio-signal with the frequency range of 0.5 Hz to 220 Hz, and has a total voltage gain of 54 dB, good agreement has been found between computer simulation and measured performance. Compared to the on-board prototype system, on-chip processor system can minimize the hardware area and reduce the product cost. <s> BIB002 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> This paper describes an Evolvable Hardware (EHW) chip, and the application of this chip as a controller for a myoelectric prosthetic hand. The chip consists of Genetic Algorithm (GA) hardware, reconfigurable hardware logic, a chromosome memory, a training data memory, and a 16-bit CPU core (NEC V30). This paper also briefly introduces other EHW chips being developed as part of the Ministry of International Trade and Industry's (MITI) Real World Computing Project (RWCP), which include an analogue EHW chip for cellular phones, a neural network EHW chip capable of autonomous reconfiguration, and a data compression EHW chip for electrophotographic printers. <s> BIB003 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> Classifying the motion of the five fingers of the hand using non-invasive bio-signal readings from the forearm is still an unsolved research challenge. Its solution is relevant to hands-free remote control devices, on-stage live performances, consumer entertainment, the video game industry, and most importantly the design of hand prosthetics for amputees. This paper proposes a solution that uses the continuous wavelet transform (CWT) decompositions of electromyography (EMG) signals from the forearm muscles, and Support Vector Machines (SVM) classification. The resulting design is a low cost, low power and low complexity portable embedded system that is strapped to the arm, where it collects EMG signals, classifies them in real-time, and sends the resulting class labels via Bluetooth to a remote interface. These labels are then converted into musical instrument digital interface (MIDI) commands that can be used to control any MIDI-controllable device. While the design is still at the prototype stage at best, it provides a proof-of-concept of non-invasive finger motion classification solely based on EMG readings from the forearm muscles. Experimental simulation of the expected system achieved 91% accuracy. <s> BIB004 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> The technology of human-computer interaction (HCI) is developing rapidly in tandem with the advancement of information and biological technologies. Many new types input device are introduced into this field; some of them are aimed to benefit special groups of people like old or disabled persons. In the meantime, Electromyography (EMG) and Inertia Measure Unit (IMU) have been readily available and extensively applied in control systems in many fields. In this paper, we propose a novel EMG-IMU based mouse controller that controls cursor movements based on IMU signals. The displacement of the cursor is determined by integrating the acceleration signal from the IMU, which moves with the operator's arm. The mouse operations such as left click, right click and wheel scroll, are commanded through EMG signals. The pattern recognition algorithm, Linear Discriminant Analysis (LDA), is adopted to classify the EMG data into several clusters, which correspond to the pre-defined mouse operations. Experimental results have indicated that the proposed mouse controller can achieve an accuracy of 88%. <s> BIB005 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> EMG pattern classification has been widely studied for decoding user intent for intuitive prosthesis control. However, EMG signals can be easily contaminated by noise and disturbances, which may degrade the classification performance. This study aims to design a real-time self-recovery EMG pattern classification interface to provide reliable user intent recognition for multifunctional prosthetic arm control. A novel self-recovery module consisting of multiple sensor fault detectors and a fast LDA classifier retraining strategy has been developed to immediately recover the classification performance from signal disturbances. The self-recovery EMG pattern recognition (PR) system has been implemented on an embedded system as a working prototype. Experimental evaluation has been performed on an able-bodied subject in real-time to classify three arm movements while signal disturbances were manually introduced. The results of this study may propel the clinical use of EMG PR for multifunctional prosthetic arm control. <s> BIB006 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <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> BIB007 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> BACKGROUND ::: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. ::: ::: ::: OBJECTIVES ::: The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). ::: ::: ::: STUDY DESIGN ::: Case series study. ::: ::: ::: METHODS ::: We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. ::: ::: ::: RESULTS ::: Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. ::: ::: ::: CONCLUSION ::: Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. ::: ::: ::: CLINICAL RELEVANCE ::: Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses. <s> BIB008 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> Recently commercialized powered prosthetic arm systems hold great potential in restoring function for people with upper-limb loss. However, effective use of such devices remains limited by conventional (direct) control methods, which rely on electromyographic signals produced from a limited set of muscles. Targeted Muscle Reinnervation (TMR) is a nerve transfer procedure that creates additional recording sites for myoelectric prosthesis control. The effects of TMR may be enhanced when paired with pattern recognition technology. We sought to compare pattern recognition and direct control in eight transhumeral amputees who had TMR in a balanced randomized cross-over study. Subjects performed a 6–8 week home trial using direct and pattern recognition control with a custom prostheses made from commercially available parts. Subjects showed statistically better performance in the Southampton Hand Assessment Procedure (p = 0.04) and the Clothespin relocation task (p = 0.02). Notably, these tests required movements along 3 degrees of freedom. Seven of 8 subjects preferred pattern recognition control over direct control. This study was the first home trial large enough to establish clinical and statistical significance in comparing pattern recognition with direct control. Results demonstrate that pattern recognition is a viable option and has functional advantages over direct control. <s> BIB009 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> INTRODUCTION ::: A commercially available EMG Pattern Recognition (EMG-PR) control system was adapted to interface with the multi-degree of freedom (DOF) DEKA Arm. ::: ::: ::: PURPOSE ::: To describe users' experience of controlling the DEKA Arm using EMG-PR. ::: ::: ::: METHODS ::: Sample: Twelve persons with upper limb amputation participated, 10 with transradial (TR), 2 with transhumeral (TH) level amputation. Ten were male, and 11 were users of a prosthesis at baselines. Design: This was a two-part study consisting of in-laboratory prosthetic training (Part A) and up to 12 weeks of home use of the prosthesis (Part B). Data collection: Qualitative data were collected through open-ended survey questions and semi-structured interviews. Data Analysis: The study used a qualitative case series design with a constant comparative approach to identify common categories of experience. Coding categories were iteratively refined until saturation of categories was achieved. The data were organized in a primary category, major categories of experience, factors impacting experience, and broader contextual factors. ::: ::: ::: RESULTS ::: Users had mixed perspectives on the desirability of the EMG-PR control system in combination with the DEKA Arm. Major aspects of user experience related to the system complexity, process of calibrating, and functional benefits. Factors influencing user experience included training and acclimation, fatigue, prosthesis design, technical issues and control changes. Broader contextual factors, both personal and environmental, also impacted users' experience. ::: ::: ::: DISCUSSION/CONCLUSION ::: This study provided an in-depth description of user experience operating the DEKA Arm using EMG-PR control. The majority of participants expressed a preference for the controls of their personal prosthesis and controls rather than the iteration of EMG-PR controlled DEKA Arm used in this study. Most were positive about the future potential of EMG-PR as a control method. An understanding of patient experience will assist clinicians and patients choosing prosthetic options. <s> BIB010 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time EMG-PR with Embedded System <s> The functionality of upper limb prostheses can be improved by intuitive control strategies that use bioelectric signals measured at the stump level. One such strategy is the decoding of motor volition via myoelectric pattern recognition (MPR), which has shown promising results in controlled environments and more recently in clinical practice. Moreover, not much has been reported about daily life implementation and real-time accuracy of these decoding algorithms. This paper introduces an alternative approach in which MPR allows intuitive control of four different grips and open/close in a multifunctional prosthetic hand. We conducted a clinical proof-of-concept in activities of daily life by constructing a self-contained, MPR-controlled, transradial prosthetic system provided with a novel user interface meant to log errors during real-time operation. The system was used for five days by a unilateral dysmelia subject whose hand had never developed, and who nevertheless learned to generate patterns of myoelectric activity, reported as intuitive, for multi-functional prosthetic control. The subject was instructed to manually log errors when they occurred via the user interface mounted on the prosthesis. This allowed the collection of information about prosthesis usage and real-time classification accuracy. The assessment of capacity for myoelectric control test was used to compare the proposed approach to the conventional prosthetic control approach, direct control. Regarding the MPR approach, the subject reported a more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. This paper represents an alternative to the conventional use of MPR, and this alternative may be particularly suitable for a certain type of amputee patients. Moreover, it represents a further validation of MPR with dysmelia cases. <s> BIB011
An embedded system was customised to perform a specific task and function often with real-time control. This system was mainly based on microcontrollers or microprocessors. Real-time embedded for the EMG-PR for hand prosthesis can be enhanced to reduce cost, size and increase the reliability and performance of the prosthetic device. Almost all of the real-time EMG-PR control using embedded systems followed the same stages to operate, as shown in Figure 3 . Firstly, the sEMG signal is recorded from the subject muscle using electrodes. Then sEMG signal acquisition takes place. This signal goes through pre-processing techniques. The features are extracted and selected from the pre-processed signal. Once the selected features are classified, the command was sent to the embedded controller to control the end effector. Wirta et al.  first reported the used of embedded myoelectric pattern recognition system in 1963. The robotic arm was developed, and Discriminant analysis was chosen as a classification technique. After a few years during 1996, a real-time EMG-PR was proposed with a digital signal processing (DSP) (TMS320C31) based system having a modified maximum likelihood distance (MMLD) classifier. Four able-bodied and two quadriplegic subjects volunteered and were designated with five motions of neck and shoulders. The total response time for EMG discrimination was 0.17 s, and it achieved a 95% mean discrimination rate BIB001 . An analogue integrated circuit for the wireless transmission of physiological signals designed by Yeng et al. BIB002 focused more on the transmission system, not on the implementation of the prosthesis. In 1999, an Evolvable Hardware (EHW) chip for myoelectric artificial hands was developed to serve as a standard tool for hardware validation BIB003 . To access the computer for limbs disabled through their remaining muscles, a real-time assistive device was designed in 2007 with PR of EMG signals  . The signals were measured from the muscles of the lower arm of the subject during different wrist motions. The obtained signals were filtered, and a supervised multi-layer neural network trained by a backpropagation algorithm was used for the classification of the user's movement and clicking of a cursor. The drawback of that article was that the researcher focussed more on the qualitative evaluation of performance than presenting the control implementation. Similarly, Anbin et al. BIB005 proposed the novel combination of signals (EMG and inertial measurement unit (IMU)) to be used for mouse controller (cursor movements). LDA classifies the EMG data into several groups of 128 ms time window and 32 ms increment window, which correspond to the pre-defined computer mouse operations. The results showed an accuracy of 88%. In 2007, Bitar et al. BIB004 explained in detail the design of portable Musical Instrument Digital Interface (MIDI) using a continuous wavelet transform (CWT) decomposition and SVM. A low-complexity portable dsPIC33FJ256GP710 embedded system was designed that collects and classifies EMG signals. This embedded system is quite inexpensive and consumes less power. The output from four-channel was sampled at 1 kHz frequency using the dsPIC's on-chip A/D converters. The channel window (fixed-length windows of 0.6 s with 0.3 s overlap) was normalised by its respective power. The CWT coefficients were computed for each and every channel separately, and the desired features were extracted. Finally, six class classifications were performed using the SVM classifier, and the decision of the classifier transmits the result as labels in real-time using Bluetooth to a remote interface. Moreover, to control a MIDI-enabled device (mechanical prosthetic hand), these labels were then converted to MIDI commands. The experiment showed an achieved 91% accuracy. Ke et al BIB007 presented the latest progress on EMG-PR control of a prosthetic hand. EMG signal was acquired using an armband with eight-channel electrodes. A powerful embedded system was introduced to deal with the decoding algorithm of EMG signals. These real-time surface myoelectric signals decoding and EMG training (onboard) were incorporated in the embedded system to control a prosthetic hand of six DOFs. The result showed that the possibility of speeding up the movement of a PR prosthetic arm, making it more suited for daily application, is promising. In 2013, Xiaorong et al. BIB006 proposed a first real-time EMG-PR self-recovery classification using a cumulative sum algorithm (CUSUM) detector. Forty-eight motion artefacts were introduced on 12 real-time testing trials. CUSUM detector successfully detected the 43 artifacts, which lead to 93.5% of the elimination of misclassification caused by motion artefact. Similarly, in 2015, Ann et al. BIB008 compared the non-adaptive (conventional) and adaptive control (real-time) prediction learning. The experiment was performed on one trans-humeral prosthesis and three able-bodied subjects. EMG signal acquired using eight channels sampled at 1 kHz to classify the eight classes of motion. Subjects were asked to wear a Bento arm (anthropometric robotic arm), which consists of MTT (AX-18 smart robotic arm) incorporating five DOF. The result shows the adaptive control decreases the total switching time and improve myoelectric robotic arm during uninterrupted use by subjects (amputee and normally limbed). A few articles showed the real-time control of commercially available prostheses for finding the user experience with pattern recognition control. Understanding the patient's experience can help clinicians and patients who choose prosthetic options. The commercially available EMG-PR control was interfaced with multiple degrees of freedom DEKA arm BIB010 . This study provided an extensive description of the user experience of operating a DEKA arm using EMG-PR control. The majority of the participants preferred the future prospective of EMG-PR as a control measure. Mastinu et al. BIB011 presented the real-time implementations of PR techniques on dysmelia subjects (congenital disorder). The subject was asked to use iLimb-ultra (Touch Bionics, UK) for five consecutive days during the experiment. This system is known as the artificial limb controller which includes a pattern recognition system. The classification accuracy and motion test of the system were compared with different classes on motion (open hand, closed hand, side grip, fine grip and pointer) individually. The real-time pattern recognition accuracy for motion test (subjects were asked to perform as directed on-screen) was higher than the classification or execution accuracy. Hargrove et al. BIB009 demonstrated the outcomes obtained from the commercially available prosthetic used by subjects undergone targeted muscle reinnervation (TMR). Subjects wore a commercially available prosthesis to perform different household tasks. A comparison of direct method and pattern recognition methods in TMR subjects were performed, and statistical significance of both methods was evaluated. Users performed well with pattern recognition incorporated devices. Additionally, among the eight subjects participated, seven preferred pattern recognition control. Some of the papers related to the real-time with embedded packages are summarised and shown in Table 2 . Among many classifiers (NN and SVM), LDA is one of the most used classifiers for real-time embedded systems. LDA's main advantages are its simplicity of implementation in an embedded processor. Although many studies have been done and the embedded system has been implemented to develop the prosthetic of a lost limb using EMG-PR control, a major issue of achieving natural and reliable control of limb remains unsolved. 