Development of threshold based EMG prosthetic hand

There is a real need of EMG (Electromyogram) based prosthetic hand for the amputee which should be economical as well as reliable. The cheap prosthetic hand available in market works passively. In those cases the patient does not feel the feeling of natural human hand. EMG based prosthetic hand provides the amputee feeling of natural human hand. The work that has been discussed here is to develop a prosthetic hand with one degree of freedom. The two motions developed were open and close. Most of the work is done at electronic level. The main work was to acquire the noiseless EMG signal and further to convert it into control signal for prosthetic hand, after suitable processing. For classification a threshold based technique has used rather than any classification technique like Artificial Neural Network (ANN), Fuzzy Logic and Genetic Algorithm (GA). It was tried to use the minimum hardware, without making any compromise with performance. It was done so, to achieve the target of developing a economical and reliable prosthetic hand. The threshold value used was variable and was controllable from outside by just varying the knob of potentiometer. This adds an additional dimension for tuning the device and scope to adjust the threshold according to muscle activity of subject. So the same prosthetic hand can be used by different amputees by just changing the threshold values only. The mechanical hand was having only two fingers to grasp the objects. The work was also extended to develop the frequency based Prosthetic hand. The scheme was to find out the frequency bands where the amplitude of open and close motions is different. The FFTs (Fast Fourier Transform) of EMG signal were calculated in MATLAB. The DSO (Digital Storage oscilloscope) was also having the facility of displaying the FFT of signal. It was found that there is certain possible frequency band which classifies the open and close motion of han

A preliminary investigation assessing the viability of classifying hand postures in seniors

<p>Abstract</p> <p>Background</p> <p>Fear of frailty is a main concern for seniors. Surface electromyography (sEMG) controlled assistive devices for the upper extremities could potentially be used to augment seniors' force while training their muscles and reduce their fear of frailty. In fact, these devices could both improve self confidence and facilitate independent leaving in domestic environments. The successful implementation of sEMG controlled devices for the elderly strongly relies on the capability of properly determining seniors' actions from their sEMG signals. In this research we investigated the viability of classifying hand postures in seniors from sEMG signals of their forearm muscles.</p> <p>Methods</p> <p>Nineteen volunteers, including seniors (70 years old in average) and young people (27 years old in average), participated in this study and sEMG signals from four of their forearm muscles (i.e. Extensor Digitorum, Palmaris Longus, Flexor Carpi Ulnaris and Extensor Carpi Radialis) were recorded. The feature vectors were built by extracting features from each channel of sEMG including autoregressive (AR) model coefficients, waveform length and root mean square (RMS). Multi-class support vector machines (SVM) was used as a classifier to distinguish between fifteen different essential hand gestures including finger pinching.</p> <p>Results</p> <p>Classification of hand gestures both in the pronation and supination positions of the arm was possible. Classified hand gestures were: rest, ulnar deviation, radial deviation, grasp and four different finger pinching configurations. The obtained average classification accuracy was 90.6% for the seniors and 97.6% for the young volunteers.</p> <p>Conclusions</p> <p>The obtained results proved that the pattern recognition of sEMG signals in seniors is feasible for both pronation and supination positions of the arm and the use of only four EMG channel is sufficient. The outcome of this study therefore validates the hypothesis that, although there are significant neurological and physical changes occurring in humans while ageing, sEMG controlled hand assistive devices could potentially be used by the older people.</p

Development of threshold based EMG prosthetic hand

There is a real need of EMG (Electromyogram) based prosthetic hand for the amputee which should be economical as well as reliable. The cheap prosthetic hand available in market works passively. In those cases the patient does not feel the feeling of natural human hand. EMG based prosthetic hand provides the amputee feeling of natural human hand. The work that has been discussed here is to develop a prosthetic hand with one degree of freedom. The two motions developed were open and close. Most of the work is done at electronic level. The main work was to acquire the noiseless EMG signal and further to convert it into control signal for prosthetic hand, after suitable processing. For classification a threshold based technique has used rather than any classification technique like Artificial Neural Network (ANN), Fuzzy Logic and Genetic Algorithm (GA). It was tried to use the minimum hardware, without making any compromise with performance. It was done so, to achieve the target of developing a economical and reliable prosthetic hand. The threshold value used was variable and was controllable from outside by just varying the knob of potentiometer. This adds an additional dimension for tuning the device and scope to adjust the threshold according to muscle activity of subject. So the same prosthetic hand can be used by different amputees by just changing the threshold values only. The mechanical hand was having only two fingers to grasp the objects. The work was also extended to develop the frequency based Prosthetic hand. The scheme was to find out the frequency bands where the amplitude of open and close motions is different. The FFTs (Fast Fourier Transform) of EMG signal were calculated in MATLAB. The DSO (Digital Storage oscilloscope) was also having the facility of displaying the FFT of signal. It was found that there is certain possible frequency band which classifies the open and close motion of han

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

<p>Abstract</p> <p>Background</p> <p>Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.</p> <p>Methods</p> <p>Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.</p> <p>Results</p> <p>The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.</p> <p>Conclusions</p> <p>The results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.</p

Development of a biological signal-based evaluator for robot-assisted upper-limb rehabilitation: a pilot study

Bio-signal based assessment for upper-limb functions is an attractive technology for rehabilitation. In this work, an upper-limb function evaluator is developed based on biological signals, which could be used for selecting different robotic training protocols. Interaction force (IF) and participation level (PL, processed surface electromyography (sEMG) signals) are used as the key bio-signal inputs for the evaluator. Accordingly, a robot-based standardized performance testing (SPT) is developed to measure these key bio-signal data. Moreover, fuzzy logic is used to regulate biological signals, and a rules-based selector is then developed to select different training protocols. To the authors’ knowledge, studies focused on biological signal-based evaluator for selecting robotic training protocols, especially for robot-based bilateral rehabilitation, has not yet been reported in literature. The implementation of SPT and fuzzy logic to measure and process key bio-signal data with a rehabilitation robot system is the first of its kind. Five healthy participants were then recruited to test the performance of the SPT, fuzzy logic and evaluator in three different conditions (tasks). The results show: (1) the developed SPT has an ability to measure precise bio-signal data from participants; (2) the utilized fuzzy logic has an ability to process the measured data with the accuracy of 86.7% and 100% for the IF and PL respectively; and (3) the proposed evaluator has an ability to distinguish the intensity of biological signals and thus to select different robotic training protocols. The results from the proposed evaluator, and biological signals measured from healthy people could also be used to standardize the criteria to assess the results of stroke patients later

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Métodos Computacionales para el Reconocimiento de Patrones Mioeléctricos en el Control de Exoesqueletos Robóticos: Una Revisión

El desarrollo de las interfaces hombre-máquina ha representado una línea de investigación interesante y ampliamente estudiada en el campo de la rehabilitación. En este sentido, para potencializar los procesos de rehabilitación física de las personas con discapacidad motora hay un esfuerzo creciente en la comunidad científica hacia el desarrollo de nuevos dispositivos robóticos, como los exoesqueletos. El control mioeléctrico es una técnica avanzada concerniente con la detección, procesamiento, clasificación y aplicación de señales electromiográficas para el control de sistemas externos y dispositivos de rehabilitación. En la terapia física efectuada mediante el uso de sistemas robóticos, es fundamental una identificación efectiva de la intención de los movimientos humanos para comandar tales sistemas. En la literatura se han utilizado ampliamente las señales electromiográficas de superficie, teniendo en cuenta que las mismas pueden reflejar la intención del movimiento. Este artículo proporciona una revisión de las técnicas y métodos computacionales que han sido utilizados, basados en técnicas de extracción de características y reconocimiento de patrones para el control mioléctrico de exoesqueletos. Se abordan trabajos que hacen uso de estos métodos, para el control de los dispositivos robóticos, y se plantean direcciones futuras en este campo de investigación

Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic