A virtual myoelectric prosthesis training system capable of providing instructions on hand operations (original) (raw)
Related papers
A training system for the MyoBock hand in a virtual reality environment
2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2013
This paper proposes a novel EMG-based MyoBock training system that consistently provides a variety of functions ranging from EMG signal control training to task training. Using the proposed training sytem, a trainee controls a virtual hand (VH) in a 3D virtual reality (VR) environment using EMG signals and position/posture information recorded from the trainee. The trainee can also perform tasks such as holding and moving virtual objects using the system. In the experiments of this study, virtual task training developed with reference to the Box and Block Test (BBT) used to evaluate myoelectric prostheses was conducted with two healthy subjects, who repeatedly performed 10 one-minute tasks involving grasping a ball in one box and transporting it to another. The BBT experiments were also conducted in a real environment before and after the virtual training, with results showing an improvement in the number of tasks successfully completed. It was therefore confirmed that the proposed system could be used for myoelectric prosthesis control training.
Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees
A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, volun- tary electromyography (EMG) signals and to simultaneously con- trol movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied par- ticipants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a prac- tical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encour- aging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.
A Comprehensive Review of Myoelectric Prosthesis Control
ArXiv, 2021
Prosthetic hands can be used to support upper-body amputees. Myoelectric prosthesis, one of the externally-powered active prosthesis categories, requires proper processing units in addition to recording electrodes and instrumentation amplifiers. In this paper, the following myoelectric prosthesis control methods were discussed in detail: On-off and finite-state, proportional, direct, and posture, simultaneous, classification and regression-based control, and deep learning methods. Myoelectric control performance indices, such as completion time and rate, throughput, lag, and path length, were reviewed. The advantages and disadvantages of the control methods were also discussed. Some of myoelectric prosthesis control's significant challenges are comfort, durability, cost, the application of under-sampled signals, and electrode shift. Moreover, the proposed algorithms must be usually tuned after each don and doff, which is not comfortable for the users. Realtime simultaneous and p...
Shared human–robot proportional control of a dexterous myoelectric prosthesis
Nature Machine Intelligence, 2019
Myoelectric prostheses allow users to recover lost functionality by controlling a robotic device with their remaining muscle activity. Such commercial devices can give users a high level of autonomy, but still do not approach the dexterity of the intact human hand. We present here a method to control a robotic hand, shared between user intention and robotic automation. The algorithm allows user-controlled movements when high dexterity is desired, but also assisted grasping when robustness is paramount. This combination of features is currently lacking in commercial prostheses and can greatly improve prosthesis usability. First, we design and test a myoelectric proportional controller that can predict multiple joint angles simultaneously and with high accuracy. We then implement online control with both able-bodied and amputee subjects. Finally, we present a shared control scheme in which robotic automation aids in object grasping by maximizing contact area between hand and object, greatly increasing grasp success and object hold times in both a virtual and a physical environment. Our results present a viable method of prosthesis control implemented in real time, for reliable articulation of multiple simultaneous degrees of freedom. In the United States alone, about 1.6 million people live with an amputation, 541,000 of which affect the upper limbs 1. This condition diminishes quality of life, mobility and independence, while also imparting a social stigma 2. Upper limb prostheses controlled using surface electromyographic (sEMG) signals attempt to restore hand and arm functionality by using the amputee's remaining muscle activity to control movements of a prosthetic device. However, the capabilities of current commercial prostheses are still grossly inferior compared to the dexterity of the human hand. Commercial devices usually use a two-recording-channel system to control a single degree of freedom (DoF), i.e. one sEMG channel for flexion and one for extension 3. While intuitive, the system provides little dexterity. Patients abandon myoelectric prostheses at high rates, in part because they feel that the level of control is insufficient to merit the price and complexity of these devices 4-6 In recent years, various research groups have made significant advances in myoelectric prosthesis control in laboratory and prototype environments. Many groups have demonstrated great success in grasp classification, which is a common approach for prosthesis control, but limits the user to a library of trained hand postures 7-10. However a few groups have now attempted to decode single finger movements 11-13. Despite high decoding accuracy, these studies showed results mainly from able-bodied subjects performing offline tests. With cited decoding performances of upwards of 90-95% for each method, we see a clear dichotomy between laboratory experiments and clinical viability, a point that is addressed by Jiang et al 14. The idea of "shared control", that is, automation of some portion of the motor command, is already a topic of interest within the field of robotics and neuroengineering 15-18. Indeed, shared control approach can play a key role in robotic applications involving human-robot interfacing such as prosthetic body parts. The limited sensory-motor control abilities in this case make the subjects
The Journal of Hand Surgery, 2005
Purpose: To develop a system for refined motor control of artificial hands based on multiple electromyographic (EMG) recordings, allowing multiple patterns of hand movements. Methods: Five subjects with traumatic below-elbow amputations and 1 subject with a congenital below-elbow failure of formation performed 10 imaginary movements with their phantom hand while surface electrodes recorded the EMG data. In a training phase a data glove with 18 degrees of freedom was used for positional recording of movements in the contralateral healthy hand. These movements were performed at the same time as the imaginary movements in the phantom hand. An artificial neural network (ANN) then could be trained to associate the specific EMG patterns recorded from the amputation stump with the analogous specific hand movements synchronously performed in the healthy hand. The ability of the ANN to predict the 10 imaginary movements offline, when they were reflected in a virtual computer hand, was assessed and calculated. Results: After the ANN was trained the subjects were able to perform and control 10 hand movements in the virtual computer hand. The subjects showed a median performance of 5 types of movement with a high correlation with the movement pattern of the data glove. The subjects seemed to relearn to execute motor commands rapidly that had been learned before the accident, independent of how old the injury was. The subject with congenital below-elbow failure of formation was able to perform and control several hand movements in the computer hand that cannot be performed in a myoelectric prosthesis (eg, opposition of the thumb). Conclusions: With the combined use of an ANN and a data glove, acting in concert in a training phase, amputees rapidly can learn to execute several imaginary movements in a virtual computerized hand, this opens promising possibilities for motor control of future hand prostheses. (J Hand Surg 2005;30A:780 -789.
Effects of prosthesis use on the capability to control myoelectric robotic prosthetic hands
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
The dexterous natural control of robotic prosthetic hands with non-invasive techniques is still a challenge: surface electromyography gives some control capabilities but these are limited, often not natural and require long training times; the application of pattern recognition techniques recently started to be applied in practice. While results in the scientific literature are promising they have to be improved to reach the real needs. The Ninapro database aims to improve the field of naturally controlled robotic hand prosthetics by permitting to worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark database. Currently, the Ninapro database includes data from 67 intact subjects and 11 amputated subject performing approximately 50 different movements. The data are aimed at permitting the study of the relationships between surface electromyography, kinematics and dynamics. The Ninapro acquisition protocol was created in order to be easy to be reproduced. Currently, the number of datasets included in the database is increasing thanks to the collaboration of several research groups.
Myoelectric Controlled Hand Prosthesis
iitr.ac.in
The human hand is a very complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different ...
Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands
Journal of Rehabilitation Research and Development, 2014
A myoelectric controller should provide an intuitive and effective human-machine interface that deciphers user intent in real-time and is robust enough to operate in daily life. Many myoelectric control architectures have been developed, including pattern recognition systems, finite state machines, and more recently, postural control schemes. Here, we present a comparative study of two types of finite state machines and a postural control scheme using both virtual and physical assessment procedures with seven nondisabled subjects. The Southampton Hand Assessment Procedure (SHAP) was used in order to compare the effectiveness of the controllers during activities of daily living using a multigrasp artificial hand. Also, a virtual hand posture matching task was used to compare the controllers when reproducing six target postures. The performance when using the postural control scheme was significantly better (p < 0.05) than when using the finite state machines during the physical assessment when comparing within-subject averages using the SHAP percent difference metric. The virtual assessment results described significantly greater completion rates (97% and 99%) for the finite state machines, but the movement time tended to be faster (2.7 s) for the postural control scheme. Our results substantiate that postural control schemes rival other state-of-the-art myoelectric controllers.
Effect of Feedback during Virtual Training of Grip Force Control with a Myoelectric Prosthesis
The aim of this study was to determine whether virtual training improves grip force control in prosthesis use, and to examine which type of augmented feedback facilitates its learning most. Thirty-two able-bodied participants trained grip force with a virtual ball-throwing game for five sessions in a two-week period, using a myoelectric simulator. They received either feedback on movement outcome or on movement execution. Sixteen controls received training that did not focus on force control. Variability over learning was examined with the Tolerance-Noise-Covariation approach, and the transfer of grip force control was assessed in five test-tasks that assessed different aspects of force control in a pretest, a posttest and a retention test. During training performance increased while the variability in performance was decreased, mainly by reduction in noise. Grip force control only improved in the test-tasks that provided information on performance. Starting the training with a task that required low force production showed no transfer of the learned grip force. Feedback on movement execution was detrimental to grip force control, whereas feedback on movement outcome enhanced transfer of grip force control to tasks other than trained. Clinical implications of these results regarding virtual training of grip force control are discussed. Citation: Bouwsema H, van der Sluis CK, Bongers RM (2014) Effect of Feedback during Virtual Training of Grip Force Control with a Myoelectric Prosthesis. PLoS ONE 9(5): e98301.