Control of hand prostheses using peripheral information

Grip force and force sharing in two different manipulation tasks with bottles

Grip force and force sharing during two activities of daily living were analysed experimentally in 10 right-handed subjects. Four different bottles, filled to two different levels, were manipulated for two tasks: transporting and pouring. Each test subject's hand was instrumented with eight thin wearable force sensors. The grip force and force sharing were significantly different for each bottle model. Increasing the filling level resulted in an increase in grip force, but the ratio of grip force to load force was higher for lighter loads. The task influenced the force sharing but not the mean grip force. The contributions of the thumb and ring finger were higher in the pouring task, whereas the contributions of the palm and the index finger were higher in the transport task. Mean force sharing among fingers was 30% for index, 29% for middle, 22% for ring and 19% for little finger. Practitioner Summary: We analysed grip force and force sharing in two manipulation tasks with bottles: transporting and pouring. The objective was to understand the effects of the bottle features, filling level and task on the contribution of different areas of the hand to the grip force. Force sharing was different for each task and the bottles features affected to both grip force and force sharing.

Adjacent regenerative peripheral nerve interfaces produce phase-antagonist signals during voluntary walking in rats

Background

Regenerative Peripheral Nerve Interfaces (RPNIs) are neurotized muscle grafts intended to produce electromyographic signals suitable for motorized prosthesis control. Two RPNIs producing independent agonist/antagonist signals are required for each control axis; however, it is unknown whether signals from adjacent RPNIs are independent. The purpose of this work was to determine signaling characteristics from two adjacent RPNIs, the first neurotized by a foot dorsi-flexor nerve and the second neurotized by a foot plantar-flexor nerve in a rodent model.

Methods

Two Control group rats had electrodes implanted onto the soleus (tibial nerve) and extensor digitorum longus (peroneal nerve) muscles in the left hind limb. Two Dual-RPNI group rats had two separate muscles grafted to the left thigh and each implanted with electrodes: the extensor digitorum longus was neurotized with a transected fascicle from the tibial nerve, and the tibialis anterior was implanted with a transected peroneal nerve. Four months post-surgery, rats walked on a treadmill, were videographed, and electromyographic signals were recorded. Amplitude and periodicity of all signals relative to gait period were quantified. To facilitate comparisons across groups, electromyographic signals were expressed as a percent of total stepping cycle activity for each stance and swing gait phase. Independence between peroneal and tibial nerve activations were assessed by statistical comparisons between groups during stance and swing.

Results

Electromyographic activity for Control and Dual-RPNI rats displayed alternating activation patterns coinciding with stance and swing. Significant signal amplitude differences between the peroneal and tibial nerves were found in both the Control and Dual-RPNI groups. Non-inferiority tests performed on Dual-RPNI group signal confidence intervals showed that activation was equivalent to the Control group in all but the peroneal RPNI construct during stance. The similar electromyographic activity obtained for Control and RPNI suggests the latter constructs activate independently during both stance and swing, and contain minimal crosstalk.

Conclusions

In-vivo myoelectric RPNI activity encodes neural activation patterns associated with gait. Adjacent RPNIs neurotized with agonist/antagonist nerves display activity amplitudes similar to Control during voluntary walking. The distinct and expected activation patterns indicate the RPNI may provide independent signaling in humans, suitable for motorized prosthesis control.

Exploiting Knowledge Composition to Improve Real-Life Hand Prosthetic Control

In myoelectric prosthesis control, one of the hottest topics nowadays is enforcing simultaneous and proportional (s/p) control over several degrees of freedom. This problem is particularly hard and the scientific community has so far failed to provide a stable and reliable s/p control, effective in daily-life activities. In order to improve the reliability of this form of control, in this paper we propose on-the-fly knowledge composition, thereby reducing the burden of matching several patterns at the same time, and simplifying the task of the system. In particular, we show that using our method it is possible to dynamically compose a model by juxtaposing subsets of previously gathered (sample, target) pairs in real-time, rather than composing a single model in the beginning and then hoping it can reliably distinguish all patterns. Fourteen intact subjects participated in an experiment, where repetitive daily-life tasks (e.g. ironing a cloth) were performed using a commercially available dexterous prosthetic hand mounted on a splint and wirelessly controlled using a machine learning method. During the experiment, the subjects performed these tasks using myocontrol with and without knowledge composition and the results demonstrate that employing knowledge composition allowed better performance, i.e. reducing the overall task completion time by 30%.

Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition

We propose a joint force estimation method to compute elbow flexion force using surface electromyogram (sEMG) considering time-varying effects in a fatigue condition. Muscle fatigue is a major cause inducing sEMG changes with respect to time over long periods and repetitive contractions. The proposed method composed the muscle-twitch model representing the force generated by a single spike and the spikes extracted from sEMG. In this study, isometric contractions at six different joint angles (10 subjects) and dynamic contractions with constant velocity (six subjects) were performed under non-fatigue and fatigue conditions. Performance of the proposed method was evaluated and compared with that of previous methods using mean absolute value (MAV). The proposed method achieved average 6.7 ± 2.8 %RMSE for isometric contraction and 15.6 ± 24.7%RMSE for isokinetic contraction under fatigue condition with more accurate results than the previous methods.

High Cable Forces Deteriorate Pinch Force Control in Voluntary-Closing Body-Powered Prostheses

Background

It is generally asserted that reliable and intuitive control of upper-limb prostheses requires adequate feedback of prosthetic finger positions and pinch forces applied to objects. Body-powered prostheses (BPPs) provide the user with direct proprioceptive feedback. Currently available BPPs often require high cable operation forces, which complicates control of the forces at the terminal device. The aim of this study is to quantify the influence of high cable forces on object manipulation with voluntary-closing prostheses.

Method

Able-bodied male subjects were fitted with a bypass-prosthesis with low and high cable force settings for the prehensor. Subjects were requested to grasp and transfer a collapsible object as fast as they could without dropping or breaking it. The object had a low and a high breaking force setting.

Results

Subjects conducted significantly more successful manipulations with the low cable force setting, both for the low (33% more) and high (50%) object’s breaking force. The time to complete the task was not different between settings during successful manipulation trials.

Conclusion

High cable forces lead to reduced pinch force control during object manipulation. This implies that low cable operation forces should be a key design requirement for voluntary-closing BPPs.

Electromyographic classification of effort in muscle strength assessment

Dual-channel evaluation of surface electromyogram (SEMG) signals acquired from amputee subjects using computational techniques for classification of arm motions is presented in this study. SEMG signals were classified by the neural network (NN) and interpretation was done using statistical techniques to extract the effectiveness of the recorded signals. From the results, it was observed that there exists a calculative difference in amplitude gain across different motions and that SEMG signals have great potential to classify arm motions. The outcomes indicated that the NN algorithm performs significantly better than other algorithms, with a classification rate (CR) of 96.40%. Analysis of variance (ANOVA) presents the results to validate the effectiveness of the recorded data to discriminate SEMG signals. The results are of significant thrust in identifying the operations that can be implemented for classifying upper-limb movements suitable for prostheses’ design.

Comparison of Constant-Posture Force-Varying EMG-Force Dynamic Models About the Elbow

Numerous techniques have been used to minimize error in relating the surface electromyogram (EMG) to elbow joint torque. We compare the use of three techniques to further reduce error. First, most EMG-torque models only use estimates of EMG standard deviation as inputs. We studied the additional features of average waveform length, slope sign change rate and zero crossing rate. Second, multiple channels of EMG from the biceps, and separately from the triceps, have been combined to produce two low-variance model inputs. We contrasted this channel combination with using each EMG separately. Third, we previously modeled nonlinearity in the EMG-torque relationship via a polynomial. We contrasted our model versus that of the classic exponential power law of Vredenbregt and Rau (1973). Results from 65 subjects performing constant-posture, force-varying contraction gave a "baseline" comparison error (i.e., error with none of the new techniques) of 5.5 ± 2.3% maximum flexion voluntary contraction (%MVC_F). Combining the techniques of multiple features with individual channels reduced error to 4.8 ± 2.2 %MVC_F, while combining individual channels with the power-law model reduced error to 4.7 ± 2.0 %MVC_F. The new techniques further reduced error from that of the baseline by ≈ 15 %.

Real-time simulation of hand motion for prosthesis control

Individuals with hand amputation suffer substantial loss of independence. Performance of sophisticated prostheses is limited by the ability to control them. To achieve natural and simultaneous control of all wrist and hand motions, we propose to use real-time biomechanical simulation to map between residual EMG and motions of the intact hand. Here we describe a musculoskeletal model of the hand using only extrinsic muscles to determine whether real-time performance is possible. Simulation is 1.3 times faster than real time, but the model is locally unstable. Methods are discussed to increase stability and make this approach suitable for prosthesis control.

An integrated interface for peripheral neural system recording and stimulation: system design, electrical tests and in-vivo results

The prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of 2 integrated circuits: a standard CMOS device for neural recording and a HVCMOS device for neural stimulation. The integrated circuits have been realized in 2 different 0.35μ m CMOS processes available from ams. The complete system incorporates 8 channels each including the analog front-end, the A/D conversion, based on a sigma delta architecture and a programmable stimulation module implemented as a 5-bit current DAC; two voltage boosters supply the output stimulation stage with a programmable voltage scalable up to 17V. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7μ V r m s , and to selectively elicit the tibial and plantar muscles using different active sites of the electrode.

Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Stable force-myographic control of a prosthetic hand using incremental learning

Force myography has been proposed as an appealing alternative to electromyography for control of upper limb prosthesis. A limitation of this technique is the non-stationary nature of the recorded force data. Force patterns vary under influence of various factors such as change in orientation and position of the prosthesis. We hereby propose an incremental learning method to overcome this limitation. We use an online sequential extreme learning machine where occasional updates allow continual adaptation to signal changes. The applicability and effectiveness of this approach is demonstrated for predicting the hand status from forearm muscle forces at various arm positions. The results show that incremental updates are indeed effective to maintain a stable level of performance, achieving an average classification accuracy of 98.75% for two subjects.

Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching

BACKGROUND

Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device.

OBJECTIVES

The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning).

STUDY DESIGN

Case series study.

METHODS

We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials.

RESULTS

Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method.

CONCLUSION

Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects.

CLINICAL RELEVANCE

Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.

The LET Procedure for Prosthetic Myocontrol: Towards Multi-DOF Control Using Single-DOF Activations

Simultaneous and proportional myocontrol of dexterous hand prostheses is to a large extent still an open problem. With the advent of commercially and clinically available multi-fingered hand prostheses there are now more independent degrees of freedom (DOFs) in prostheses than can be effectively controlled using surface electromyography (sEMG), the current standard human-machine interface for hand amputees. In particular, it is uncertain, whether several DOFs can be controlled simultaneously and proportionally by exclusively calibrating the intended activation of single DOFs. The problem is currently solved by training on all required combinations. However, as the number of available DOFs grows, this approach becomes overly long and poses a high cognitive burden on the subject. In this paper we present a novel approach to overcome this problem. Multi-DOF activations are artificially modelled from single-DOF ones using a simple linear combination of sEMG signals, which are then added to the training set. This procedure, which we named LET (Linearly Enhanced Training), provides an augmented data set to any machine-learning-based intent detection system. In two experiments involving intact subjects, one offline and one online, we trained a standard machine learning approach using the full data set containing single- and multi-DOF activations as well as using the LET-augmented data set in order to evaluate the performance of the LET procedure. The results indicate that the machine trained on the latter data set obtains worse results in the offline experiment compared to the full data set. However, the online implementation enables the user to perform multi-DOF tasks with almost the same precision as single-DOF tasks without the need of explicitly training multi-DOF activations. Moreover, the parameters involved in the system are statistically uniform across subjects.

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces

OBJECTIVE

Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed.

APPROACH

The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI's actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms.

MAIN RESULTS

Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users' needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications.

SIGNIFICANCE

The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use.

Design and evaluation of voluntary opening and voluntary closing prosthetic terminal device

Body-powered prostheses use a cable-operated system to generate forces and move prosthetic joints. However, this control system can only generate forces in one direction, so current body-powered prehensor designs allow the user either to voluntarily open or voluntarily close the tongs. Both voluntary opening (VO) and voluntary closing (VC) modes of operation have advantages for certain tasks, and many end-users desire a terminal device that can switch between the two modes. However, such a terminal device must maintain the same thumb position (i.e., point of Bowden cable attachment) and movement direction in both modes in order to avoid the need to readjust the harness after every mode switch. In this study, we demonstrate a simple design that fulfills these requirements while allowing the user to switch easily between modes. We describe the design concept, describe a rugged split-hook prototype, provide specifications (size, weight, efficiency, etc.), and present a pilot study in which five subjects with intact arms and two subjects with amputation used the VO and VC split-hook prehensor to perform the Southampton Hand Assessment Procedure. Subjects performed an average of 4 to 7 (+/- 0.2) points better when they could choose to switch between modes on a task-by-task basis than when they were constrained to using only VO or VC modes.

A sensory feedback system for prosthetic hand based on evoked tactile sensation

The lack of reliable sensory feedback has been one of the barriers in prosthetic hand development. Restoring sensory function from prosthetic hand to amputee remains a great challenge to neural engineering. In this paper, we present the development of a sensory feedback system based on the phenomenon of evoked tactile sensation (ETS) at the stump skin of residual limb induced by transcutaneous electrical nerve stimulation (TENS). The system could map a dynamic pattern of stimuli to an electrode placed on the corresponding projected finger areas on the stump skin. A pressure transducer placed at the tip of prosthetic fingers was used to sense contact pressure, and a high performance DSP processor sampled pressure signals, and calculated the amplitude of feedback stimulation in real-time. Biphasic and charge-balanced current pulses with amplitude modulation generated by a multi-channel laboratory stimulator were delivered to activate sensory nerves beneath the skin. We tested this sensory feedback system in amputee subjects. Preliminary results showed that the subjects could perceive different levels of pressure at the tip of prosthetic finger through evoked tactile sensation (ETS) with distinct grades and modalities. We demonstrated the feasibility to restore the perceptual sensation from prosthetic fingers to amputee based on the phenomenon of evoked tactile sensation (ETS) with TENS.

A Study on Estimation of Joint Force Through Isometric Index Finger Abduction With the Help of SEMG Peaks for Biomedical Applications

We propose a new method to estimate joint force using a biomechanical muscle model and peaks of surface electromyography (SEMG). The SEMG measurement was carried out from the first dorsal interosseous muscle during isometric index finger abduction. The SEMG peaks were used as the input of the biomechanical muscle model which is a transfer function to generate the force. The force estimation performance ( R(2) ) was evaluated using the proposed method with nine healthy subjects, and a former method using a mean absolute value (MAV), which is the full-wave rectified and averaged (or low-pass filtered) signal of SEMG in a time window, was compared with the proposed method; the performance of the proposed method (0.94 ± 0.03) was better than that of MAV (0.90 ± 0.02). The proposed method could be widely applied to quantitative analysis of muscle activities based on SEMG.

Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders

An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.

Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME)

The selection of suitable peripheral nerve electrodes for biomedical applications implies a trade-off between invasiveness and selectivity. The optimal design should provide the highest selectivity for targeting a large number of nerve fascicles with the least invasiveness and potential damage to the nerve. The transverse intrafascicular multichannel electrode (TIME), transversally inserted in the peripheral nerve, has been shown to be useful for the selective activation of subsets of axons, both at inter- and intra-fascicular levels, in the small sciatic nerve of the rat. In this study we assessed the capabilities of TIME for the selective recording of neural activity, considering the topographical selectivity and the distinction of neural signals corresponding to different sensory types. Topographical recording selectivity was proved by the differential recording of CNAPs from different subsets of nerve fibers, such as those innervating toes 2 and 4 of the hindpaw of the rat. Neural signals elicited by sensory stimuli applied to the rat paw were successfully recorded. Signal processing allowed distinguishing three different types of sensory stimuli such as tactile, proprioceptive and nociceptive ones with high performance. These findings further support the suitability of TIMEs for neuroprosthetic applications, by exploiting the transversal topographical structure of the peripheral nerves.

More with less: A comparative kinematical analysis of Django Reinhardt's adaptations to hand injury

BACKGROUND

At the age of 18 years, jazz guitarist Django Reinhardt (1910-1953) sustained significant burns to his left-hand ring and little fingers; yet, subsequently, he relearned to play and achieved international fame, despite his injuries.

CASE DESCRIPTION AND METHODS

Archive film footage and novel motion analysis software were used to compare movements of Django's fretting hand with that of six other guitarists of the same genre.

FINDINGS AND OUTCOMES

Django employed greater abduction of index and middle fingers (-9.11 ± 6.52° vs -5.78 ± 2.41°; p < 0.001) and more parallel alignment of fingers to the guitar neck (157.7 ± 3.37° vs 150.59 ± 2.67°; p < 0.001) compared to controls.

CONCLUSION

In response to debilitating hand injury, Django developed quantifiable compensatory adaptation of function of his remaining functional fingers by developing an original playing technique.

CLINICAL RELEVANCE

Hand function following injury may be optimized by maximizing latent degrees of freedom in remaining digits, rather than through extensive surgical reconstruction or complex prostheses. Further study of adaptation strategies may inform prosthesis design.

MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy

Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

Real-Time Task Discrimination for Myoelectric Control Employing Task-Specific Muscle Synergies

We present a novel formulation that employs task-specific muscle synergies and state-space representation of neural signals to tackle the challenging myoelectric control problem for lower arm prostheses. The proposed framework incorporates information about muscle configurations, e.g., muscles acting synergistically or in agonist/antagonist pairs, using the hypothesis of muscle synergies. The synergy activation coefficients are modeled as the latent system state and are estimated using a constrained Kalman filter. These task-dependent synergy activation coefficients are estimated in real-time from the electromyogram (EMG) data and are used to discriminate between various tasks. The task discrimination is helped by a post-processing algorithm that uses posterior probabilities. The proposed algorithm is robust as well as computationally efficient, yielding a decision with > 90% discrimination accuracy in approximately 3 ms . The real-time performance and controllability of the algorithm were evaluated using the targeted achievement control (TAC) test. The proposed algorithm outperformed common machine learning algorithms for single- as well as multi-degree-of-freedom (DOF) tasks in both off-line discrimination accuracy and real-time controllability (p < 0.01).

Comparison of body-powered voluntary opening and voluntary closing prehensor for activities of daily life

Persons with an upper-limb amputation who use a body-powered prosthesis typically control the prehensor through contralateral shoulder movement, which is transmitted through a Bowden cable. Increased cable tension either opens or closes the prehensor; when tension is released, some passive element, such as a spring, returns the prehensor to the default state (closed or open). In this study, we used the Southampton Hand Assessment Procedure to examine functional differences between these two types of prehensors in 29 nondisabled subjects (who used a body-powered bypass prosthesis) and 2 persons with unilateral transradial amputations (who used a conventional body-powered device). We also administered a survey to determine whether subjects preferred one prehensor or the other for specific tasks, with a long-term goal of assessing whether a prehensor that could switch between both modes would be advantageous. We found that using the voluntary closing prehensor was 1.3 s faster (p = 0.02) than using the voluntary opening prehensor, across tasks, and that there was consensus among subjects on which types of tasks they preferred to do with each prehensor type. Twenty-five subjects wanted a device that could switch between the two modes in order to perform particular tasks.

Joint force estimation using time-varying SEMG feature in fatiguing contraction

Many studies have estimated joint force using surface electromyography (SEMG), however, the time-variant characteristic of SEMG is not considered. The change of SEMG amplitude is one of manifestations of muscle fatigue. This study proposes a force estimation method using SEMG in fatiguing contraction. The SEMG amplitude is used to determine the signal states by k-means clustering method. According to the signal state changes, the corresponding gain is used to estimate the force. The target contraction is an isometric abduction of an index finger in static and dynamic force conditions for 5 healthy subjects. The estimation performance was evaluated by percentage of root mean squared error (RMSE). The RMSE for the proposed method is 2.5 ± 1.0% under static condition and 8.8 ± 1.2% under dynamic condition. The accuracy using a constant gain calculated at initial time was used to compare with the proposed method. The RMSE are 8.9 ± 2.2% under static condition and 10.1 ± 2.4% under dynamic condition. The proposed method had better performance in both conditions.

Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints

Integrating learned predictions into a prosthetic control system promises to enhance multi-joint prosthesis use by amputees. In this article, we present a preliminary study of different cases where it may be beneficial to use a set of temporally extended predictions--learned and maintained in real time--within an engineered or learned prosthesis controller. Our study demonstrates the first successful combination of actor-critic reinforcement learning with real-time prediction learning. We evaluate this new approach to control learning during the myoelectric operation of a robot limb. Our results suggest that the integration of real-time prediction and control learning may speed control policy acquisition, allow unsupervised adaptation in myoelectric controllers, and facilitate synergies in highly actuated limbs. These experiments also show that temporally extended prediction learning enables anticipatory actuation, opening the way for coordinated motion in assistive robotic devices. Our work therefore provides initial evidence that realtime prediction learning is a practical way to support intuitive joint control in increasingly complex prosthetic systems.

A comparative analysis of three non-invasive human-machine interfaces for the disabled

In the framework of rehabilitation robotics, a major role is played by the human–machine interface (HMI) used to gather the patient’s intent from biological signals, and convert them into control signals for the robotic artifact. Surprisingly, decades of research have not yet declared what the optimal HMI is in this context; in particular, the traditional approach based upon surface electromyography (sEMG) still yields unreliable results due to the inherent variability of the signal. To overcome this problem, the scientific community has recently been advocating the discovery, analysis, and usage of novel HMIs to supersede or augment sEMG; a comparative analysis of such HMIs is therefore a very desirable investigation. In this paper, we compare three such HMIs employed in the detection of finger forces, namely sEMG, ultrasound imaging, and pressure sensing. The comparison is performed along four main lines: the accuracy in the prediction, the stability over time, the wearability, and the cost. A psychophysical experiment involving ten intact subjects engaged in a simple finger-flexion task was set up. Our results show that, at least in this experiment, pressure sensing and sEMG yield comparably good prediction accuracies as opposed to ultrasound imaging; and that pressure sensing enjoys a much better stability than sEMG. Given that pressure sensors are as wearable as sEMG electrodes but way cheaper, we claim that this HMI could represent a valid alternative/augmentation to sEMG to control a multi-fingered hand prosthesis.

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. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Applications of sensory feedback in motorized upper extremity prosthesis: a review

Dexterous hand movement is possible due to closed loop control dependent on efferent motor output and afferent sensory feedback. This control strategy is significantly altered in those with upper limb amputation as sensations of touch and movement are inherently lost. For upper limb prosthetic users, the absence of sensory feedback impedes efficient use of the prosthesis and is highlighted as a major factor contributing to user rejection of myoelectric prostheses. Numerous sensory feedback systems have been proposed in literature to address this gap in prosthetic control; however, these systems have yet to be implemented for long term use. Methodologies for communicating prosthetic grasp and touch information are reviewed, including discussion of selected designs and test results. With a focus on clinical and translational challenges, this review highlights and compares techniques employed to provide amputees with sensory feedback. Additionally, promising future directions are discussed and highlighted.

The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

Dexterous control of a prosthetic hand using fine-wire intramuscular electrodes in targeted extrinsic muscles

Restoring dexterous motor function equivalent to that of the human hand after amputation is one of the major goals in rehabilitation engineering. To achieve this requires the implementation of a effortless human-machine interface that bridges the artificial hand to the sources of volition. Attempts to tap into the neural signals and to use them as control inputs for neuroprostheses range in invasiveness and hierarchical location in the neuromuscular system. Nevertheless today, the primary clinically viable control technique is the electromyogram measured peripherally by surface electrodes. This approach is neither physiologically appropriate nor dexterous because arbitrary finger movements or hand postures cannot be obtained. Here we demonstrate the feasibility of achieving real-time, continuous and simultaneous control of a multi-digit prosthesis directly from forearm muscles signals using intramuscular electrodes on healthy subjects. Subjects contracted physiologically appropriate muscles to control four degrees of freedom of the fingers of a physical robotic hand independently. Subjects described the control as intuitive and showed the ability to drive the hand into 12 postures without explicit training. This is the first study in which peripheral neural correlates were processed in real-time and used to control multiple digits of a physical hand simultaneously in an intuitive and direct way.

Quantification of muscle-derived signal interference during monopolar needle electromyography of a peripheral nerve interface in the rat hind limb

High-fidelity signal acquisition is critical for the fundamental control of a neuroprosthesis. Our group has developed a bio-artificial interface consisting of a muscle graft neurotized by a severed nerve in a rat hind limb model. This regenerative peripheral nerve interface (RPNI) permits nerve signal transmission, amplification, and detection via in situ electromyography (EMG). Our study examined the magnitude of signal interference from simultaneously contracting muscles adjacent to our muscle of interest. In eighteen F344 rats, the extensor digitorum longus (EDL) muscle was used to fabricate simulated RPNI constructs of various sizes in which the neurovascular pedicle was preserved, obviating the need for reinnervation or revascularization. After 3 weeks of recovery, in situ EMG testing was performed using electrical stimulation of the common peroneal nerve. A recording needle was placed in the EDL muscle with a reference/ground electrode in the contralateral toe webspace, comprising a monopolar recording configuration. The superficial peroneal nerve was transected to further isolate stimulation of the anterior compartment. Recordings from the EDL were performed before and after excision of the tibialis anterior (TA) and extensor hallucis longus (EHL) muscles. After TA/EHL excision, EDL compound muscle action potential (CMAP) peak-to-peak amplitudes were significantly lower by an average of 7.4±5.6(SD) mV, or 32±18%, (paired t(17)=-5.7, p<;0.0001). A significant positive linear correlation was seen between CMAP amplitude and EDL mass both before TA/EHL excision (r=0.68, n=18, p<;0.01) and after TA/EHL excision (r=0.79, n=18, p<;0.0001). EDL mass did not correlate with differences in CMAP amplitude or area caused by TA/EHL excision. Monopolar needle EMG recordings from the EDL muscle are significantly, but predictively, contaminated by concomitant muscular contractions in the anterior compartment of the rat hind limb. Further investigation of strategies to reduce this signal interference, including electrode choice or configuration, use of bioelectrical insulators, and filtering methods, is warranted to promote high-fidelity signal acquisition for prosthetic control.

An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning

Background

The information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers. The iPCA pre-processing implies an optimization stage which has not yet been deeply explored.

Methods

The present study considers two factors in the iPCA stage: namely A (the fitness function), and B (the search algorithm). The A factor comprises two levels, namely A₁ (the classification error) and A₂ (the correlation factor). Otherwise, the B factor has four levels, specifically B₁ (the Sequential Forward Selection, SFS), B₂ (the Sequential Floating Forward Selection, SFFS), B₃ (Artificial Bee Colony, ABC), and B₄ (Particle Swarm Optimization, PSO). This work evaluates the incidence of each one of the eight possible combinations between A and B factors over the classification error of the MCS.

Results

A two factor ANOVA was performed on the computed classification errors and determined that: (1) the interactive effects over the classification error are not significative (F_0.01,3,72 = 4.0659 > f_AB = 0.09), (2) the levels of factor A have significative effects on the classification error (F_0.02,1,72 = 5.0162 < f_A = 6.56), and (3) the levels of factor B over the classification error are not significative (F_0.01,3,72 = 4.0659 > f_B = 0.08).

Conclusions

Considering the classification performance we found a superiority of using the factor A₂ in combination with any of the levels of factor B. With respect to the time performance the analysis suggests that the PSO algorithm is at least 14 percent better than its best competitor. The latter behavior has been observed for a particular configuration set of parameters in the search algorithms. Future works will investigate the effect of these parameters in the classification performance, such as length of the reduced size vector, number of particles and bees used during optimal search, the cognitive parameters in the PSO algorithm as well as the limit of cycles to improve a solution in the ABC algorithm.