Kianoush Nazarpour

Bio: Kianoush Nazarpour is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 22, co-authored 143 publications receiving 1779 citations. Previous affiliations of Kianoush Nazarpour include Newcastle University & Tarbiat Modares University.

Combined influence of forearm orientation and muscular contraction on EMG pattern recognition

Abstract: Multiple dynamic factors can significantly degrade the accuracy of EMG pattern recognition.The impact of many of these factors has been studied in isolation.We investigated the combined effect of forearm orientation and muscle contraction levels.Twelve intact-limbed and one bilateral trans-radial amputee participated in the experiment.Features that quantify the angular similarity can mitigate the problem. The performance of intelligent electromyogram (EMG)-driven prostheses, functioning as artificial alternatives to missing limbs, is influenced by several dynamic factors including: electrode position shift, varying muscle contraction level, forearm orientation, and limb position. The impact of these factors on EMG pattern recognition has been previously studied in isolation, with the combined effect of these factors being understudied. However, it is likely that a combination of these factors influences the accuracy. We investigated the combined effect of two dynamic factors, namely, forearm orientation and muscle contraction levels, on the generalizability of the EMG pattern recognition. A number of recent time- and frequency-domain EMG features were utilized to study the EMG classification accuracy. Twelve intact-limbed and one bilateral transradial (below-elbow) amputee subject were recruited. They performed six classes of wrist and hand movements at three muscular contraction levels with three forearm orientations (nine conditions). Results indicate that a classifier trained by features that quantify the angle, rather than amplitude, of the muscle activation patterns perform better than other feature sets across different contraction levels and forearm orientations. In addition, a classifier trained with the EMG signals collected at multiple forearm orientations with medium muscular contractions can generalize well and achieve classification accuracies of up to 91%. Furthermore, inclusion of an accelerometer to monitor wrist movement further improved the EMG classification accuracy. The results indicate that the proposed methodology has the potential to improve robustness of myoelectric pattern recognition.

Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements

Abstract: Myoelectric pattern recognition systems can decode movement intention to drive upper-limb prostheses. Despite recent advances in academic research, the commercial adoption of such systems remains low. This limitation is mainly due to the lack of classification robustness and a simultaneous requirement for a large number of electromyogram (EMG) electrodes. We propose to address these two issues by using a multi-modal approach which combines surface electromyography (sEMG) with inertial measurements (IMs) and an appropriate training data collection paradigm. We demonstrate that this can significantly improve classification performance as compared to conventional techniques exclusively based on sEMG signals. We collected and analyzed a large dataset comprising recordings with 20 able-bodied and two amputee participants executing 40 movements. Additionally, we conducted a novel real-time prosthetic hand control experiment with 11 able-bodied subjects and an amputee by using a state-of-the-art commercial prosthetic hand. A systematic performance comparison was carried out to investigate the potential benefit of incorporating IMs in prosthetic hand control. The inclusion of IM data improved performance significantly, by increasing classification accuracy (CA) in the offline analysis and improving completion rates (CRs) in the real-time experiment. Our findings were consistent across able-bodied and amputee subjects. Integrating the sEMG electrodes and IM sensors within a single sensor package enabled us to achieve high-level performance by using on average 4-6 sensors. The results from our experiments suggest that IMs can form an excellent complimentary source signal for upper-limb myoelectric prostheses. We trust that multi-modal control solutions have the potential of improving the usability of upper-extremity prostheses in real-life applications.

Deep learning-based artificial vision for grasp classification in myoelectric hands.

Abstract: OBJECTIVE Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. APPROACH We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at [Formula: see text] intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. MAIN RESULTS The classification accuracy in the offline tests reached [Formula: see text] for the seen and [Formula: see text] for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of [Formula: see text] in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to [Formula: see text]. In addition, we show that with training, subjects' performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. SIGNIFICANCE The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably.

Flexible Cortical Control of Task-Specific Muscle Synergies

Abstract: Correlation structure in the activity of muscles across movements is often interpreted as evidence for low-level, hardwired constraints on upper-limb function. However, muscle synergies may also emerge from optimal strategies to achieve high-level task goals within a redundant control space. To distinguish these contrasting interpretations, we examined the structure of muscle variability during operation of a myoelectric interface in which task constraints were dissociated from natural limb biomechanics. We found that, with practice, human subjects learned to shape patterns of covariation between arbitrary pairs of hand and forearm muscles appropriately for elliptical targets whose orientation varied on a trial-by-trial basis. Thus, despite arriving at the same average location in the effector space, performance was improved by buffering variability into those dimensions that least impacted task success. Task modulation of beta-frequency intermuscular coherence indicated that differential recruitment of divergent corticospinal pathways contributed to positive correlations among muscles. However, this feedforward mechanism could not account for negative correlations observed in the presence of visual feedback. A second experiment revealed the development of fast, target-dependent visual responses consistent with "minimum intervention" control correcting predominantly task-relevant errors. Together, these mechanisms contribute to the dynamic emergence of task-specific muscle synergies appropriate for a wide range of abstract task goals.

Abstract and proportional myoelectric control for multi-fingered hand prostheses.

Abstract: Powered hand prostheses with many degrees of freedom are moving from research into the market for prosthetics In order to make use of the prostheses’ full functionality, it is essential to study efficient ways of high dimensional myoelectric control Human subjects can rapidly learn to employ electromyographic (EMG) activity of several hand and arm muscles to control the position of a cursor on a computer screen, even if the muscle-cursor map contradicts directions in which the muscles would act naturally But can a similar control scheme be translated into real-time operation of a dexterous robotic hand? We found that despite different degrees of freedom in the effector output, the learning process for controlling a robotic hand was surprisingly similar to that for a virtual two-dimensional cursor Control signals were derived from the EMG in two different ways, with a linear and a Bayesian filter, to test how stable user intentions could be conveyed through them Our analysis indicates that without visual feedback, control accuracy benefits from filters that reject high EMG amplitudes In summary, we conclude that findings on myoelectric control principles, studied in abstract, virtual tasks can be transferred to real-life prosthetic applications

Machine learning with Python

Abstract: This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Upper limb prosthesis use and abandonment: A survey of the last 25 years:

Abstract: This review presents an analytical and comparative survey of upper limb prosthesis acceptance and abandonment as documented over the past 25 years, detailing areas of consumer dissatisfaction and ongoing technological advancements. English-language articles were identified in a search of Ovid, PubMed, and ISI Web of Science (1980 until February 2006) for key words upper limb and prosthesis. Articles focused on upper limb prostheses and addressing: (i) Factors associated with abandonment; (ii) Rejection rates; (iii) Functional analyses and patterns of wear; and (iv) Consumer satisfaction, were extracted with the exclusion of those detailing tools for outcome measurement, case studies, and medical procedures. Approximately 200 articles were included in the review process with 40 providing rates of prosthesis rejection. Quantitative measures of population characteristics, study methodology, and prostheses in use were extracted from each article. Mean rejection rates of 45% and 35% were observed in the literature for body-powered and electric prostheses respectively in pediatric populations. Significantly lower rates of rejection for both body-powered (26%) and electric (23%) devices were observed in adult populations while the average incidence of non-wear was similar for pediatric (16%) and adult (20%) populations. Documented rates of rejection exhibit a wide range of variance, possibly due to the heterogeneous samples involved and methodological differences between studies. Future research should comprise of controlled, multifactor studies adopting standardized outcome measures in order to promote comprehensive understanding of the factors affecting prosthesis use and abandonment. An enhanced understanding of these factors is needed to optimize prescription practices, guide design efforts, and satiate demand for evidence-based measures of intervention.

Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

Abstract: Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when control- ling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.

The Integrative Action of the Nervous System.

Abstract: In this book Professor Holmes discusses some of the evidence relating to one of the most baffling problems yet recognized by biologists-the factors involved in the regulation of growth and form. A wide range of possible influences, from enzymes to cellular competition, is considered. Numerous experiments and theories are described, with or without bibliographic citation. There is a list of references for each chapter and an index. The subject from a scientific standpoint is an exceedingly difficult one, for the reason that very little indeed is understood regarding such phenomena as differentiation. It follows that the problem offers fine opportunities for intellectual jousting by mechanists and vitalists, that hypotheses and theories must often be the weapons of choice, and philosophy the armor. Professor Holmes gives us a good seat from which to watch the combats, explains clearly what is going on, and occasionally slips away to enter the lists himself. This stereoscopic atlas of anatomy was designed as an aid in teaching neuro-anatomy for beginning medical students and as a review for physicians taking Board examinations in Psychiatry and Neurology. Each plate consists of a pair of stereoscopic photographs and a labelled diagram of the important parts seen in the photograph. Perhaps in this day of scarcity of materials, particularly of human brains hardened for dissection, photographs of this kind conceivably can be used as a substitute. Successive stages of dissection are presented in such a fashion that, used in conjunction with the dissecting manual, a student should be able to identify most of the important components of the nervous system without much outside help. The area covered is limited to the gross features of the brain and brain stem and perhaps necessarily does not deal with any of the microscopic structure. So much more can be learned from the dissection of the actual brain that it is doubtful if this atlas would be useful except where brains are not available. A good deal of effort has been spent on the preparation of this atlas, with moderately successful results.

Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position

Abstract: Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.