Progressive unsupervised control of myoelectric upper limbs.

Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time.We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement.

Unsupervised Myocontrol of a Virtual Hand Based on a Coadaptive Abstract Motor Mapping.

Applications of simultaneous and proportional control for upper-limb prostheses typically rely on supervised machine learning to map muscle activations to prosthesis movements. This scheme often poses problems for individuals with limb differences, as they may not be able to reliably reproduce the training activations required to construct a natural motor mapping. We propose an unsupervised myocontrol paradigm that eliminates the need for labeled data by mapping the most salient muscle synergies in arbitrary order to a number of predefined prosthesis actions.

The Merits of Dynamic Data Acquisition for Realistic Myocontrol.

Natural myocontrol is the intuitive control of a prosthetic limb via the user's voluntary muscular activations. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a model that can decipher these muscular activations. A consequence of this approach is that the reliability of a myocontrol system depends on how representative this training data is for all types of signal variability that may be encountered when the amputee puts the prosthesis into real use.

Publisher Correction: Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics.

A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person's life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information.

On the Visuomotor Behavior of Amputees and Able-Bodied People During Grasping.

Visual attention is often predictive for future actions in humans. In manipulation tasks, the eyes tend to fixate an object of interest even before the reach-to-grasp is initiated. Some recent studies have proposed to exploit this anticipatory gaze behavior to improve the control of dexterous upper limb prostheses.

Natural Myocontrol in a Realistic Setting: a Comparison Between Static and Dynamic Data Acquisition.

Natural myocontrol employs pattern recognition to allow users to control a robotic limb intuitively using their own voluntary muscular activations. The reliability of myocontrol strongly depends on the signals initially collected from the users, which must appropriately capture the variability encountered later on during operation. Since myoelectric signals can vary based on the position and orientation of the limb, it has become best practice to gather data in multiple body postures.

Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data.

Control methods based on sEMG obtained promising results for hand prosthetics. Control system robustness is still often inadequate and does not allow the amputees to perform a large number of movements useful for everyday life. Only few studies analyzed the repeatability of sEMG classification of hand grasps.

Megane Pro: Myo-electricity, visual and gaze tracking data acquisitions to improve hand prosthetics.

During the past 60 years scientific research proposed many techniques to control robotic hand prostheses with surface electromyography (sEMG). Few of them have been implemented in commercial systems also due to limited robustness that may be improved with multimodal data. This paper presents the first acquisition setup, acquisition protocol and dataset including sEMG, eye tracking and computer vision to study robotic hand control.

Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know.

Domain adaptation methods have been proposed to reduce the training efforts needed to control an upper-limb prosthesis by adapting well performing models from previous subjects to the new subject. These studies generally reported impressive reductions in the required number of training samples to achieve a certain level of accuracy for intact subjects. We further investigate two popular methods in this field to verify whether this result also applies to amputees.

Natural control capabilities of robotic hands by hand amputated subjects.

People with transradial hand amputations who own a myoelectric prosthesis currently have some control capabilities via sEMG. However, the control systems are still limited and not natural. The Ninapro project is aiming at helping the scientific community to overcome these limits through the creation of publicly available electromyography data sources to develop and test machine learning algorithms.

Classification of hand movements in amputated subjects by sEMG and accelerometers.

Numerous recent studies have aimed to improve myoelectric control of prostheses. However, the majority of these studies is characterized by two problems that could be easily fulfilled with recent resources supplied by the scientific literature. First, the majority of these studies use only intact subjects, with the unproved assumption that the results apply equally to amputees.

Characterization of a benchmark database for myoelectric movement classification.

In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by jointly recording surface electromyography signals from the forearm and kinematics of the hand and wrist while subjects perform a predefined set of actions and postures.

Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography.

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)-Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics.

Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification.

There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which contains surface electromyography data for 6 DOF force activations as well as for 40 discrete hand movements. The evaluation involves a modern kernel method and compares performance of three feature representations and three kernel functions.

Stable myoelectric control of a hand prosthesis using non-linear incremental learning.

Stable myoelectric control of hand prostheses remains an open problem. The only successful human-machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to remove these limitations, but their performance is thus far inadequate: myoelectric signals change over time under the influence of various factors, deteriorating control performance.

Electromyography data for non-invasive naturally-controlled robotic hand prostheses.

Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs.

Exploiting accelerometers to improve movement classification for prosthetics.

Recent studies have explored the integration of additional input modalities to improve myoelectric control of prostheses. Arm dynamics in particular are an interesting option, as these can be measured easily by means of accelerometers. In this work, the benefit of accelerometer signals is demonstrated on a large scale movement classification task, consisting of 40 hand and wrist movements obtained from 20 subjects.

On the challenge of classifying 52 hand movements from surface electromyography.

The level of dexterity of myoelectric hand prostheses depends to large extent on the feature representation and subsequent classification of surface electromyography signals. This work presents a comparison of various feature extraction and classification methods on a large-scale surface electromyography database containing 52 different hand movements obtained from 27 subjects. Results indicate that simple feature representations as Mean Absolute Value and Waveform Length can achieve similar performance to the computationally more demanding marginal Discrete Wavelet Transform.

Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression.

Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated incrementally in real-time and require minimal human intervention. Incremental Sparse Spectrum Gaussian Process Regression is an algorithm that is targeted specifically for use in this context.

Experiences in the creation of an electromyography database to help hand amputated persons.

Currently, trans-radial amputees can only perform a few simple movements with prosthetic hands. This is mainly due to low control capabilities and the long training time that is required to learn controlling them with surface electromyography (sEMG). This is in contrast with recent advances in mechatronics, thanks to which mechanical hands have multiple degrees of freedom and in some cases force control.