I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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Biomechanics And Motor Control Of Human Movement

Background
Physical therapy (PT) is one of the key disciplines in interdisciplinary stroke rehabilitation. The aim of this systematic review was to provide an update of the evidence for stroke rehabilitation interventions in the domain of PT.

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What is the evidence for physical therapy poststroke? A systematic review and meta-analysis.

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.

The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, 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.

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Surface Electromyography Signal Processing and Classification Techniques

A new tendon driven mechanism, embedded into a soft hand exoskeleton for rehabilitation and assistance, was proposed in this study. The proposed solution was a pulley flexion mechanism inspired by the human musculoskeletal system to enable a natural and comfortable finger flexion. A biomechanical constraint for the finger flexion motion states that the relation between the proximal interphalangeal joint angle of the finger should always be flexed around 1.5 times the distal interphalangeal joint angle. The study aimed to comply with this constraint, by simultaneously distributing the forces over the distal and middle finger phalanges. For evaluation, the voluntary and exoskeleton flexions were compared based on the relation between the proximal and distal interphalangeal joint angles. The results showed that during the exoskeleton flexion the relation between the interphalangeal joints complied with the biomechanical constraint, where the proximal interphalangeal joint angle was 1.5 times larger than the distal interphalangeal joint. This ensures that the mechanism flexes the finger comfortably. The proposed solution is therefore a promising design for a novel soft exoskeleton that will be used for training and assistance of patients with hand paralysis.

Bio-inspired tendon driven mechanism for simultaneous finger joints flexion using a soft hand exoskeleton

In the case of a hand amputation, the affected person can use a myoelectric prosthesis to substitute the missing limb and regain motor functions. Unfortunately, commercial methods for myoelectric control, although robust and simple, are unintuitive and cognitively taxing when applied to an advanced multi-functional prosthesis. The state-of-the-art methods developed in academia are based on machine learning and therefore require long training and suffer from a lack of robustness. This work presents a novel closed-loop multi-level amplitude controller (CMAC), which aims at overcoming these drawbacks. The CMAC implements three degrees-of-freedom (DoF) control by thresholding the muscle contraction intensity during wrist flexion and extension movements. Unique features of the controller are the vibrotactile feedback that communicates the state of the controller to the user and a scheme for proportional control. These components allow exploiting the full dexterity of the prosthesis using a simple two-channel myoelectric interface. The CMAC was compared to a commonly implemented pattern-recognition method (linear discriminant analysis – LDA) using clinically relevant tests in 12 able-bodied and 2 amputee subjects. The experimental assessment demonstrated that CMAC was similarly fast as LDA in dexterous tests (clothespin and cube manipulation), while it was somewhat slower than LDA during a simple, single DoF task (box and blocks). In addition, in all the tasks, LDA and CMAC resulted in a similarly low error rate. On the other hand, to an amputee that could not generate six distinguishable classes using LDA, the CMAC still enabled the control of all the prosthesis DoFs. Importantly, the overall setup and training time in CMAC were significantly lower compared to LDA. In conclusion, the novel method is convenient for clinical applications, and allows substantially higher control dexterity compared to what can be normally achieved using conventional two channel EMG. Therefore, CMAC provides performance comparable to advanced machine-learning algorithms and the robustness and ease of use that is characteristic for the simple two-channel myoelectric interface.

Closed-Loop Multi-Amplitude Control for Robust and Dexterous Performance of Myoelectric Prosthesis

Objective.The nervous system subconsciously estimates the state of the body as a weighted average of the information from various sensory sources, where the weights reflect the perceived reliability of each source. Loss of motor functions can be partially compensated using assistive systems (e.g. prostheses), which may also restore somatosensory feedback through tactile stimulation. Whether such artificial feedback is integrated in the neural state estimation process is not known.Approach.In this study, able-bodied subjects performed a grasp force matching task with supplementary non-invasive electrotactile stimulation with a frequency proportional to grasp force magnitude. Before the task, a brief training session taught the subjects to associate the sensation of electrotactile stimulation with the generated grasp force. In some trials, the force-frequency mapping was biased to introduce an unnoticeable mismatch between natural and electrotactile force feedback, thereby provoking the subject to subconsciously estimate the force as a compromise between the two sources of information.Main results.The outcome of this compromise revealed the weights assigned to each feedback type. The grasp forces were significantly affected by the biased mappings, as indicated by the average estimated relative weights (electrotactile: 0.69 ± 0.29; natural: 0.31 ± 0.29). Across subjects, this weight was correlatedr2=0.75) with the improvement in force matching precision when adding the unbiased electrotactile feedback to the natural force feedback, as predicted by maximum likelihood estimation. This shows that even after minimal training the nervous system adopts electrotactile stimulation as a highly reliable source of information that can improve the precision in the estimation of the grip force.Significance.This result has important implications for the restoration of sensory feedback in upper limb prostheses as it indicates that even non-invasive stimulation can be integrated naturally (i.e. subconsciously and effectively) in the motor controlloop.

Electrotactile feedback outweighs natural feedback in sensory integration during control of grasp force.

Nous presentons un outil pour la generation de patrons d'activation musculaire permettant de mouvoir le corps humain selon une trajectoire de reference. Ces patrons peuvent etre utilises pour controler artificiellement les muscles de membres paralyses (stimulation electrofonctionnelle (SEF)). Cet outil utilise une fonction d'optimisation dont la fonction de cout est l'erreur de poursuite de trajectoire et la fonction de penalite, le niveau d'activation musculaire adapte aux performances du patient. L'objectif est d'utiliser les patrons d'activation musculaire pour la reeducation de la marche de patients hemiplegiques par SEF.

Optiwalk. Un nouvel outil pour la conception et la simulation de lois de commande pour le contrôle de la marche de patients atteints de déficits moteurs

Functional electrical stimulation has been tested previously for the suppression of pathological tremor. Typically, the tremor is detected using motion sensors, and the stimulation is delivered supra motor threshold activating muscles to oppose the tremulous oscillations. Here we present a novel system that estimates tremor from the recorded electromyography signals and delivers stimulation below the motor threshold, activating sensory nerve fibers. The hypothesis (backed up by the previous studies) is that the generated sensory inflow will modulate the sensory feedback loops and/or project to the supraspinal centers acting on the true tremor sources/generators. The system was tested in two patients suffering from Parkinson disease and one Essential tremor patient, and the results were promising, i.e., tremor was significantly attenuated.

Strahinja Dosen

Papers

Bio-inspired tendon driven mechanism for simultaneous finger joints flexion using a soft hand exoskeleton

Closed-Loop Multi-Amplitude Control for Robust and Dexterous Performance of Myoelectric Prosthesis

Electrotactile feedback outweighs natural feedback in sensory integration during control of grasp force.

Optiwalk. Un nouvel outil pour la conception et la simulation de lois de commande pour le contrôle de la marche de patients atteints de déficits moteurs

Tremor Suppression Using Electromyography and Surface Sensory Electrical Stimulation