Control of hand prostheses using peripheral information

Optimal stimulus profiles for neuroprosthetic devices: monophasic versus biphasic stimulation

Designing stimulation signals for neuroprosthetic devices can be cast as an optimal control problem. Rectangular Lilly-type stimulation waveforms have been used extensively in these devices. In this paper, we rigorously formulate the charge optimization problem from a control perspective and distinguish between monophasic and biphasic stimuli. We show that for a monophasic stimulus, the important factor in stimulating a neuron is the total delivered charge per unit cell membrane. This factor is a consequence of the subthreshold linear behavior of the neural membrane. On the other hand, biphasic pulses, which are ubiquitous in the neuron stimulation context, can stimulate a neuron in its non-linear range, thereby challenging the finding that total charge delivery is the only critical factor. As a result, there may be even more optimal stimulus profiles than Lilly-type rectangular waveforms for biphasic stimulation. Furthermore, solving the charge minimization problem also will reduce the risk of electrode corrosion, which is an important factor in improving the performance of neuroprosthetic devices.

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. With respect to classifiers, the Support Vector Machine was found to be the only method that consistently achieved top performance in combination with each feature extraction method.

Design and assessment of a low-cost, electromyographically controlled, prosthetic hand

The study reported here explored the design and realization of a low-cost, electromyographically controlled hand prosthesis for amputees living in developing countries. The developed prosthesis is composed of a light aluminum structure with opposing fingers connected to a DC motor that imparts only the movement of grasp. Problems associated with surface electromyographic signal acquisition and processing, motor control, and evaluation of grasp force were addressed, with the goal of minimizing cost and ensuring easy assembly. Simple analog front ends amplify and condition the electromyographic signals registered from two antagonist muscles by surface electrodes. Analog signals are sampled at 1 kHz and processed by a microcontroller that drives the motor with a supply voltage proportional to the muscular contraction, performing the opening and closing of the opposing fingers. Reliable measurements of the level of muscle contractions were obtained by specific digital processing: real-time operators implementing the root mean square value, mean absolute value, standard deviation, and mean absolute differential value were compared in terms of efficiency to estimate the EMG envelope, computational load, and time delay. The mean absolute value operator was adopted at a time window of 64 milliseconds. A suitable calibration procedure was proposed to overcome problems associated with the wide variation of electromyograph amplitude and background noise depending on the specific patient’s muscles selected. A pulse-width modulated signal drives the DC motor, allowing closing and opening of the prosthesis. The relationship between the motor-driver signal and the actual hand-grasp force developed by the prosthesis was measured using a hand-held grip dynamometer. The resulting force was proportional to current for moderate values of current and then saturated. The motor torque, and, in turn, the force elicited, can be measured by sensing the current absorbed by the motor. Therefore, the grasp force can be opportunely limited or controlled. The cost of the only electronic and mechanical components of the electromyographically controlled hand was about US$50; other costs, such as the cost of labor to assemble the prosthesis and the production of adapters for patients, were not estimated.

Sensory feedback in upper limb prosthetics

One of the challenges facing prosthetic designers and engineers is to restore the missing sensory function inherit to hand amputation. Several different techniques can be employed to provide amputees with sensory feedback: sensory substitution methods where the recorded stimulus is not only transferred to the amputee, but also translated to a different modality (modality-matched feedback), which transfers the stimulus without translation and direct neural stimulation, which interacts directly with peripheral afferent nerves. This paper presents an overview of the principal works and devices employed to provide upper limb amputees with sensory feedback. The focus is on sensory substitution and modality matched feedback; the principal features, advantages and disadvantages of the different methods are presented.

BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms

Background

Processing and pattern recognition of myoelectric signals have been at the core of prosthetic control research in the last decade. Although most studies agree on reporting the accuracy of predicting predefined movements, there is a significant amount of study-dependent variables that make high-resolution inter-study comparison practically impossible. As an effort to provide a common research platform for the development and evaluation of algorithms in prosthetic control, we introduce BioPatRec as open source software. BioPatRec allows a seamless implementation of a variety of algorithms in the fields of (1) Signal processing; (2) Feature selection and extraction; (3) Pattern recognition; and, (4) Real-time control. Furthermore, since the platform is highly modular and customizable, researchers from different fields can seamlessly benchmark their algorithms by applying them in prosthetic control, without necessarily knowing how to obtain and process bioelectric signals, or how to produce and evaluate physically meaningful outputs.

Results

BioPatRec is demonstrated in this study by the implementation of a relatively new pattern recognition algorithm, namely Regulatory Feedback Networks (RFN). RFN produced comparable results to those of more sophisticated classifiers such as Linear Discriminant Analysis and Multi-Layer Perceptron. BioPatRec is released with these 3 fundamentally different classifiers, as well as all the necessary routines for the myoelectric control of a virtual hand; from data acquisition to real-time evaluations. All the required instructions for use and development are provided in the online project hosting platform, which includes issue tracking and an extensive “wiki”. This transparent implementation aims to facilitate collaboration and speed up utilization. Moreover, BioPatRec provides a publicly available repository of myoelectric signals that allow algorithms benchmarking on common data sets. This is particularly useful for researchers lacking of data acquisition hardware, or with limited access to patients.

Conclusions

BioPatRec has been made openly and freely available with the hope to accelerate, through the community contributions, the development of better algorithms that can potentially improve the patient’s quality of life. It is currently used in 3 different continents and by researchers of different disciplines, thus proving to be a useful tool for development and collaboration.

Evaluating different combinations of feature selection algorithms and cost functions applied to iPCA tuning in myoelectric control systems

A myoelectric control system extracts information from electromyographic (EMG) signals and uses it to control different types of prostheses, so that people who suffered traumatisms, paralysis or amputations can use them to execute common movements. Recent research shows that the addition of a tuning stage, using the individual component analysis (iPCA), results in improved classification performance. We propose and evaluate a set of novel configurations for the iPCA tuning, based on a biologically inspired optimization procedure, the artificial bee colony algorithm. This procedure is implemented and tested using two different cost functions, the traditional classification error and the proposed correlation factor, which involves lower computational effort. We compare the tuned system's performance, in terms of correct classifications, to that of a system tuned using two standard algorithms, the sequential forward selection and the sequential floating forward selection. The statistical analyses of the results don't find a significant difference among the classification performances associated with the search algorithms (p < 0.01). On the other hand, they establish a significant difference among the classification performances related to the cost functions (p < 0.02).

The use of a bone-anchored device as a hard-wired conduit for transmitting EMG signals from implanted muscle electrodes

The use of a bone-anchored device to transmit electrical signals from internalized muscle electrodes was studied in a sheep model. The bone-anchored device was used as a conduit for the passage of a wire connecting an internal epimysial electrode to an external signal-recording device. The bone-anchored device was inserted into an intact tibia and the electrode attached to the adjacent M. peroneus tertius. "Physiological" signals with low signal-to-noise ratios were successfully obtained over a 12-week period by walking the sheep on a treadmill. Reliable transmission of multiple muscle signals across the skin barrier is essential for providing intuitive, biomimetic upper limb prostheses. This technology has the potential to provide a better functional and reliable solution for upper limb amputee rehabilitation: attachment and control.

Two-channel surface electromyography for individual and combined finger movements

This paper proposes the pattern recognition system for individual and combined finger movements by using two channel electromyography (EMG) signals. The proposed system employs Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The advantage of the SRDA is its speed which is faster than original LDA so that it could deal with multiple features. In addition, the use of ELM which is fast and has similar classification performance to well-known SVM empowers the classification system. The experimental results show that the proposed system was able to recognize the individual and combined fingers movements with up to 98 % classification accuracy by using only just two EMG channels.

Modeling extracellular electrical stimulation: I. Derivation and interpretation of neurite equations

Neuroprosthetic devices, such as cochlear and retinal implants, work by directly stimulating neurons with extracellular electrodes. This is commonly modeled using the cable equation with an applied extracellular voltage. In this paper a framework for modeling extracellular electrical stimulation is presented. To this end, a cylindrical neurite with confined extracellular space in the subthreshold regime is modeled in three-dimensional space. Through cylindrical harmonic expansion of Laplace's equation, we derive the spatio-temporal equations governing different modes of stimulation, referred to as longitudinal and transverse modes, under types of boundary conditions. The longitudinal mode is described by the well-known cable equation, however, the transverse modes are described by a novel ordinary differential equation. For the longitudinal mode, we find that different electrotonic length constants apply under the two different boundary conditions. Equations connecting current density to voltage boundary conditions are derived that are used to calculate the trans-impedance of the neurite-plus-thin-extracellular-sheath. A detailed explanation on depolarization mechanisms and the dominant current pathway under different modes of stimulation is provided. The analytic results derived here enable the estimation of a neurite's membrane potential under extracellular stimulation, hence bypassing the heavy computational cost of using numerical methods.

Continuous decoding of grasping tasks for a prospective implantable cortical neuroprosthesis

Background

In the recent past several invasive cortical neuroprostheses have been developed. Signals recorded from the motor cortex (area MI) have been decoded and used to control computer cursors and robotic devices. Nevertheless, few attempts have been carried out to predict different grips.

A Support Vector Machines (SVMs) classifier has been trained for a continuous decoding of four/six grip types using signals recorded in two monkeys from motor neurons of the ventral premotor cortex (area F5) during a reach-to-grasp task.

Findings

The results showed that four/six grip types could be extracted with classification accuracy higher than 96% using window width of 75–150 ms.

Conclusions

These results open new and promising possibilities for the development of invasive cortical neural prostheses for the control of reaching and grasping.

A note on the probability distribution function of the surface electromyogram signal

The probability density function (PDF) of the surface electromyogram (EMG) signals has been modelled with Gaussian and Laplacian distribution functions. However, a general consensus upon the PDF of the EMG signals is yet to be reached, because not only are there several biological factors that can influence this distribution function, but also different analysis techniques can lead to contradicting results. Here, we recorded the EMG signal at different isometric muscle contraction levels and characterised the probability distribution of the surface EMG signal with two statistical measures: bicoherence and kurtosis. Bicoherence analysis did not help to infer the PDF of measured EMG signals. In contrast, with kurtosis analysis we demonstrated that the EMG PDF at isometric, non-fatiguing, low contraction levels is super-Gaussian. Moreover, kurtosis analysis showed that as the contraction force increases the surface EMG PDF tends to a Gaussian distribution.

A BIO-MECHANICAL DESIGNED PROSTHETIC HAND WITH MULTI-CONTROL STRATEGIES

In order to mimic the natural appearance, motion, and perception of the human hand, a bio-mechatronic approach to design an anthropomorphic prosthetic hand — HIT/DLR Prosthetic Hand has been presented. It reproduces human hand in its fundamental structure such as appearance, weight, and dimensions. Its thumb can move along a cone surface in 3D space. Similar with that of human's, it combines with abduction and adduction from palmar position to lateral position. Actuated by only one motor, the middle finger, ring finger, and little finger can envelop complex-shaped objects. In addition, the bio-mechatronic integration and cosmetic designation make it much more like a genuine human hand.

HIT/DLR Prosthetic Hand can be controlled through voice control strategy, Electromyography (EMG) control strategy, EMG, and electrocutaneous sensory feedback (ESF) close loop control strategy. In EMG control system, 10 types of hand posture are recognized through six electrodes on the basis of support vector machine (SVM). The last control strategy can help an amputee recover the grasp perception, further improve the efficiency of EMG control, and reduce the hand mis-manipulation and force delivery mistakes.

On the viability of implantable electrodes for the natural control of artificial limbs: review and discussion

The control of robotic prostheses based on pattern recognition algorithms is a widely studied subject that has shown promising results in acute experiments. The long-term implementation of this technology, however, has not yet been achieved due to practical issues that can be mainly attributed to the use of surface electrodes and their highly environmental dependency. This paper describes several implantable electrodes and discusses them as a solution for the natural control of artificial limbs. In this context "natural" is defined as producing control over limb movement analogous to that of an intact physiological system. This includes coordinated and simultaneous movements of different degrees of freedom. It also implies that the input signals must come from nerves or muscles that were originally meant to produce the intended movement and that feedback is perceived as originating in the missing limb without requiring burdensome levels of concentration. After scrutinizing different electrode designs and their clinical implementation, we concluded that the epimysial and cuff electrodes are currently promising candidates to achieving a long-term stable and natural control of robotic prosthetics, provided that communication from the electrodes to the outside of the body is guaranteed.

A hybrid tool for reaching and grasping rehabilitation: the ArmeoFES

Many research groups are currently working with robotic devices for hand grasp rehabilitation and restoration. A common problem in this area is the fact that existing and commercially available robotic exoskeletons are able to provide gravity compensation of the shoulder and elbow but do not provide any support for the grasping and releasing movements of the hand. The lack of a flexible support technology for the hand reduces the possible ways in which clinicians can deal with the issue of a personalized, effective rehabilitation. This paper presents new software that allows FES assisted grasping to integrate with the ArmeoSpring (Hocoma AG). The system uses a Man-In-The-Loop control approach, whereby surface EMG signals from proximal muscles are used to trigger and modulate multichannel FES applied to distal muscles, thus allowing patient induced and strength adapted movement control of the hand. Combining volitionally controlled FES with arm-weight-compensation allows early adoption of FES assisted therapy for patients, augmenting their functionalities and extending training capabilities with the ArmeoSpring.

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

Background

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting.

Methods

Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm.

Results

The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm.

Conclusions

These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

Virtual reality environment for simulating tasks with a myoelectric prosthesis: an assessment and training tool

Intuitively and efficiently controlling multiple degrees of freedom is a major hurdle in the field of upper limb prosthetics. A virtual reality myoelectric transhumeral prosthesis simulator has been developed for cost-effectively testing novel control algorithms and devices. The system acquires EMG commands and residual limb kinematics, simulates the prosthesis dynamics, and displays the combined residual limb and prosthesis movements in a virtual reality environment that includes force-based interactions with virtual objects. A virtual Box and Block Test is demonstrated. Three normally-limbed subjects performed the simulated test using a sequential and a synchronous control method. With the sequential method, subjects moved on average 6.7±1.9 blocks in 120 seconds, similar to the number of blocks transhumeral amputees are able to move with their physical prostheses during clinical evaluation. With the synchronous method, subjects moved 6.7±2.2 blocks. The virtual reality prosthesis simulator is thus a promising tool for developing and evaluating control methods, prototyping novel prostheses, and training amputees.

Activities on PNS neural interfaces for the control of hand prostheses

The development of interfaces linking the human nervous system with artificial devices is an important area of research. Several groups are working on the development of devices able to restore sensory-motor function in subjects affected by neurological disorders, injuries or amputations. Neural electrodes implanted in peripheral nervous system, and in particular intrafascicular electrodes, seem to be a promising approach for the control of hand prosthesis thanks to the possibility to selectively access motor and sensory fibers for decoding motor commands and delivering sensory feedback. In this paper, activities on the use of PNS interfaces for the control of hand prosthesis are presented. In particular, the design and feasibility study of a self-opening neural interface is presented together with the decoding of ENG signals in one amputee to control a dexterous hand prosthesis.