Low-cost and open-source anthropomorphic prosthetics hand using linear actuators

A robust, low cost, open-source, and low power consumption in the research of prosthetics hand is essential. The purpose of this study is to develop a low-cost, open-source anthropomorphic prosthetics hand using linear actuator based on electromyography (EMG) signal control. The main advantages of this proposed method are the low-cost, lightweight and simplicity of controlling the prosthetic hand using only single channel. This is achieved by evaluating the DC motor and exploring number of locations of the EMG signal. The development of prosthetics hand consists of 3D anthropomorphic hand design, active electrodes, microcontroller, and linear actuator. The active electrodes recorded the EMG signal from extensor carpi radialis longus. The built-in EMG amplifier on the electrode amplified the EMG signal. Further, the A/D converter in the Arduino microcontroller converted the analog signal into digital. A filtering process consisted of bandpass and notch filter was performed before it used as a control signal. The linear actuator controlled each finger for flexion and extension motion. In the assessment of the design, the prosthetic hand capable of grasping ten objects. In this study, the cost and weight of the prosthetics hand are 471.99 US$ and 0.531 kg, respectively. This study has demonstrated the design of low cost and open-source of prosthetics hand with reasonable cost and lightweight. Furthermore, this development could be applied to amputee subjects

Analysis of sEMG on biceps brachii and brachioradialis in static conditions: Effect of joint angle and contraction level

Despite several previous investigations, the direct correlation between the elbow joint angle and the activities of related muscles is still an unresolved topic. The sEMG signals were recorded from biceps brachii (6x8 electrodes, 10mm IED, d=3mm) and brachioradialis (1x8 electrodes, 5mm IED, d=3mm) of ten subjects. The subjects were asked to perform isometric elbow flexion at five joint angles with four contraction levels with respect to the maximum contraction (MVC) at that joint angle. The RMS values of biceps brachii (BB) and brachioradialis (BR) are computed within 500ms epoch and averaged over the muscle’s active region. These values increase along as force increases regardless the joint angle. Concerning the different joint angle, we found that as the arm extended, the RMS values of seven subjects decreased, while the RMS values of three subjects increased. This behavior suggests different strategies of muscle contribution to the task in different subjects but may also be attributed to the technical issues discussed in Chapter 2 - 7. Prior to this investigation, several issues related to the sEMG signals recording and processing were evaluated. Analysis on the effect of different elbow joint angle on the position of the innervations zone (IZ) of biceps brachii muscle indicates that the IZ shifts distally 24±9mm as the subjects extend their arms. Thus to assure sEMG signal recording, a grid of electrodes is selected instead of bipolar electrodes. The issue of spatial aliasing, which has not been addressed before, was studied. Greater electrode’s diameter implies higher spatial low pass filtering effect which gives an advantage as anti-aliasing filter in space. On the other hand, this low pass filtering effect increase the error on the power for the single sEMG image (d=10mm, 10mm IED) to 3±13.5% compared to the continuous image. Larger IED introduces RMS estimation error up to ±18% for the single sEMG image (15mm IED). However, taking the mean of a group of maps, the error of the mean is negligible (<3%). Furthermore, the envelope of the rectified EMG has been investigated. Five digital low pass filters (Butterworth, Chebyshev, Inverse Chebyshev, and Elliptic) with five different orders, four cut off frequencies and one or bi-directional filtering were tested using simulated sEMG interference signals. The results show that different filters are optimal for different applications. Power line interference is one of the sources of impurity of the sEMG signals. Notch filter, spectral interpolation, adaptive filter, and adaptive noise canceller with phase locked loop were compared. Another factor that affects the amplitude of sEMG is the subcutaneous layer thickness (ST). Higher contraction level and greater elbow joint angle lead to thinner ST. RMS values tend to decrease for thicker ST at a rate of 1.62 decade/decade

Concept of an exoskeleton for industrial applications with modulated impedance based on Electromyographic signal recorded from the operator

The introduction of an active exoskeleton that enhances the operator power in the manufacturing field was demonstrated in literature to lead to beneficial effects in terms of reducing fatiguing and the occurrence of musculo-skeletal diseases. However, a large number of manufacturing operations would not benefit from power increases because it rather requires the modulation of the operator stiffness. However, in literature, considerably less attention was given to those robotic devices that regulate their stiffness based on the operator stiffness, even if their introduction in the line would aid the operator during different manipulations respect with the exoskeletons with variable power. In this thesis the description of the command logic of an exoskeleton for manufacturing applications, whose stiffness is modulated based on the operator stiffness, is described. Since the operator stiffness cannot be mechanically measured without deflecting the limb, an estimation based on the superficial Electromyographic signal is required. A model composed of 1 joint and 2 antagonist muscles was developed to approximate the elbow and the wrist joints. Each muscle was approximated as the Hill model and the analysis of the joint stiffness, at different joint angle and muscle activations, was performed. The same Hill muscle model was then implemented in a 2 joint and 6 muscles (2J6M) model which approximated the elbow-shoulder system. Since the estimation of the exerted stiffness with a 2J6M model would be quite onerous in terms of processing time, the estimation of the operator end-point stiffness in realtime would therefore be questionable. Then, a linear relation between the end-point stiffness and the component of muscle activation that does not generate any end-point force, is proposed. Once the stiffness the operator exerts was estimated, three command logics that identifies the stiffness the exoskeleton is required to exert are proposed. These proposed command logics are: Proportional, Integral 1 s, and Integral 2 s. The stiffening exerted by a device in which a Proportional logic is implemented is proportional, sample by sample, to the estimated stiffness exerted by the operator. The stiffening exerted by the exoskeleton in which an Integral logic is implemented is proportional to the stiffness exerted by the operator, averaged along the previous 1 second (Integral 1 s) or 2 seconds (Integral 2 s). The most effective command logic, among the proposed ones, was identified with empirical tests conducted on subjects using a wrist haptic device (the Hi5, developed by the Bioengineering group of the Imperial College of London). The experimental protocol consisted in a wrist flexion/extension tracking task with an external perturbation, alternated with isometric force exertion for the estimation of the occurrence of the fatigue. The fatigue perceived by the subject, the tracking error, defined as the RMS of the difference between wrist and target angles, and the energy consumption, defined as the sum of the squared signals recorded from two antagonist muscles, indicated the Integral 1 s logic to be the most effective for controlling the exoskeleton. A logistic relation between the stiffness exerted by the subject and the stiffness exerted by the robotic devices was selected, because it assured a smooth transition between the maximum and the minimum stiffness the device is required to exert. However, the logistic relation parameters are subject-specific, therefore an experimental estimation is required. An example was provided. Finally, the literature about variable stiffness actuators was analyzed to identify the most suitable device for exoskeleton stiffness modulation. This actuator is intended to be integrated on an existing exoskeleton that already enhances the operator power based on the operator Electromyographic signal. The identified variable stiffness actuator is the DLR FSJ, which controls its stiffness modulating the preload of a single spring

Feature and muscle selection for an effective hand motion classifier based on electromyography

An issue that arises in the hand motion classification based on the electromyography (EMG) system is the failure of choosing the right features and number of muscles. These parameters are fundamental in determining the accuracy and effectiveness of the classifier system. Therefore, the objective of this study is to develop and evaluate an effective hand motion classifier based on the EMG signal. The three-channel of EMG was collected by placing three pairs of electrodes on the surface of the skin. Six statistic features (mean, variance, standard deviation, kurtosis, skewness, and entropy) were selected to extract the EMG signal using a window length of 100 samples. A muscle and features selection is applied to the classifier machine (linear discriminant analysis (LDA), support vector machine (SVM) and K nearest neighborhood (KNN)) to retrieve the most useful feature and muscle. In this study, we found that there was no significant difference in accuracy among a number of muscles (p-value&gt;0.05). LDA and SVM showed the best accuracy and no significant difference in accuracy between both were found. This study concluded that EMG signal from a single muscle can classify the hand motion (hand close, open, wrist flexion, and extension) effectively.