Biologically Inspired Algorithms Applied to Prosthetic Control

akademik.unsri.ac.id

The people are differed by their capabilities, skills and mental agilities. The evolution of human from childhood when they are completely dependent up to adultness the time they gradually set the dependency free is too complicated, by considering they have all started from almost one point but some become cleverer and some less.

Journal of Biomechanics, 2009

Neuroprostheses, implantable or non-invasive ones, are promising techniques to enable paralyzed individuals with conditions, such as spinal cord injury or spina bifida (SB), to control their limbs voluntarily. Direct cortical control of invasive neuroprosthetic devices and robotic arms have recently become feasible for primates. However, little is known about designing non-invasive, closed-loop neuromuscular control strategies for neural prostheses. Our goal was to investigate if an artificial neural network-based (ANN-based) model for closed-loop-controlled neural prostheses could use neuromuscular activation recorded from individuals with impaired spinal cord to predict their end-point gait parameters (such as stride length and step width). We recruited 12 persons with SB (5 females and 7 males) and collected their neuromuscular activation and end-point gait parameters during overground walking. Our results show that the proposed ANN-based technique can achieve a highly accurate prediction (e.g., R-values of 0.92-0.97, ANN (tansig+tansig) for single composition of data sets) for altered end-point locomotion. Compared to traditional robust regression, this technique can provide up to 80% more accurate prediction. Our results suggest that more precise control of complex neural prostheses during locomotion can be achieved by engaging neuromuscular activity as intrinsic feedback to generate end-point leg movement. This ANN-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses.

Medical & Biological Engineering & Computing, 1995

Various kinds of command source can be used to control an above-elbow prosthesis. But none of them can be used to perform a specified task easily. The research is devoted to investigation of the potential effectiveness of applying the kinematic data of the shoulder joint to control an upper-limb prosthesis. Using these data as input signals, an appropriate signal processing technique, pattern recognition, is utilised to derive control commands. The purpose of the investigation is to use these commands to control a multifunctional prosthesis so that an amputee can perform a few tasks. For testing performance accuracy, a goniometer is worn by a subject with intact arm. It is also interfaced to a digital computer. Next, he is asked to do one of the predefined tasks for which the joint angle trajectories have already been derived and stored. Almost as soon as the shoulder joint angles are sampled and sent to the computer, the program calculates the elbow and wrist angles. These values are compared with actual elbow and wrist angles, which are monitored by the goniometer.

Journal of Intelligent Information Systems, 2003

One of the major difficulties faced by those who are fitted with prosthetic devices is the great mental effort needed during the first stages of training. When working with myoelectric prosthesis, that effort increases dramatically. In this sense, the authors decided to devise a mechanism to help patients during the learning stages, without actually having to wear the prosthesis. The system is based on a real hardware and software for detecting and processing electromyografic (EMG) signals. The association of autoregressive (AR) models and a neural network is used for EMG pattern discrimination. The outputs of the neural network are then used to control the movements of a virtual prosthesis that mimics what the real limb should be doing. This strategy resulted in rates of success of 100% when discriminating EMG signals collected from the upper arm muscle groups. The results show a very easy-to-use system that can greatly reduce the duration of the training stages.

Proceedings of 7th International Electronic Conference on Sensors and Applications, 2020

The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user's intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework. This method involves the training of classifiers which form part of the pattern recognition scheme, but also induces additional and often undesired lead time in the prosthesis design phase. In this study, a three-phase identification framework is formulated to design a control architecture capable of self-learning patterns from bio-signal inputs from electromyography (neuromuscular) and electroencephalography (brain wave) biosensors, for a transhumeral amputee case study. The results show that the designed self-learning framework can help reduce lead time in prosthesis control interface customisation, and can also be extended as an adaptive control scheme to minimise the performance degradation of the prosthesis controller.

American Journal of Biomedical Sciences, 2009

An appropriate classification of the surface myoelectric signals (MES) allows people with disabilities to control assistive prosthetic devices. The performance of these pattern recognition methods significantly affects the accuracy and smoothness of the target movements. We designed an intelligent Support Vector Machine (SVM) classifier to incorporate potential variations in electrode placement, thus achieving high accuracy for predictive control. MES from seven locations of the forearm were recorded over six different sessions. Despite meticulous attempt to keep the recording locations consistent between trials, slight shifts may still occur affecting the classification performance. We hypothesize that the machine learning algorithm is able to compensate for these variations. The recorded data was first processed using Discrete Wavelet Transform over 9 frequency bands. As a result, a 63-dimension embedding of the wavelet coefficients were used as the training data for the SVM classifiers. For each session of recordings, a new classifier was trained using only the data sets from the previous sessions. The new classifier was then tested with the data obtained in the current session. The performance of the classifier was evaluated by calculating the sensitivity and specificity. The result indicated that after a critical number of recording sessions, the classifier accuracy starts to reach a plateau, meaning that inclusions of new training data will not significant improve the performance of the classifier. It was observed that the effect of electrode placement variations was reduced and that the classification accuracy of >89% can be obtained.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014

Partial-hand amputees often retain good residual wrist motion, which is essential for functional activities involving use of the hand. Thus, a crucial design criterion for a myoelectric, partial-hand prosthesis control scheme is that it allows the user to retain residual wrist motion. Pattern recognition (PR) of electromyographic (EMG) signals is a well-studied method of controlling myoelectric prostheses. However, wrist motion degrades a PR system's ability to correctly predict hand-grasp patterns. We studied the effects of (1) window length and number of hand-grasps, (2) static and dynamic wrist motion, and (3) EMG muscle source on the ability of a PR-based control scheme to classify functional hand-grasp patterns. Our results show that training PR classifiers with both extrinsic and intrinsic muscle EMG yields a lower error rate than training with either group by itself (p<0.001); and that training in only variable wrist positions, with only dynamic wrist movements, or wit...

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014

The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigate different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions, such as the multi-layer perceptron (MLP), were found to be more cost effective, as they can be successfully employed in their simplest form. In the prediction of individual movements, the one-vs-one (OVO) topology was found to improve classification accuracy across different classifiers and it was therefore used to benchmark the benefits of simultaneous control. As opposed to previous work reporting only offline accuracy, the classification performance and the resulting controllability are evaluated in real time using the motion test and target achievement control (TAC) test, respectively. We propose a simultaneous classification strategy based on MLP that outperformed a top classifier for individual movements (LDA-OVO), thus improving the state-of-the-art classification approach. Furthermore, all the presented classification strategies and data collected in this study are freely available in BioPatRec, an open source platform for the development of advanced prosthetic control strategies. Index Terms-Artificial limbs, artificial neural networks (ANN), mixed classes pattern recognition, prosthetic limbs, simultaneous pattern recognition. I. INTRODUCTION T HE state-of-the-art technology for the rehabilitation of amputees in clinics around the world is commonly a dualsite controlled myoelectric prosthesis. This device is controlled using a simple threshold detection method for the activation of one output (e.g., hand close) following one input, which is the myoelectric signals (MES) of a group of muscles (e.g., wrist flexors). Antagonistic muscles are therefore normally required to control one degree of freedom. In the case of a multifunctional prosthesis with several degrees of freedom (DoF), but still Manuscript

IEEE Transactions on Biomedical Engineering, 1983

Serbian Journal of Electrical Engineering, 2017

Electromyography (EMG) is a well known technique used for recording electrical activity produced by human muscles. In the last few decades, EMG signals are used as a control input for prosthetic hands. There are several multifunctional myoelectric prosthetic hands for amputees on the market, but so forth, none of these devices permits the natural control of more than two degrees of freedom. In this paper we present our implementation of the pattern classification using custom made components (electrodes and an embedded EMG amplifier). The components were evaluated in offline and online tests, in able bodied as well as amputee subjects. This type of control is based on computing the time domain features of the EMG signals recorded from the forearm and using these features as input for a Linear Discriminant Analysis (LDA) classifier estimating the intention of the prosthetic user.