Nebraska Biomechanics Core Facilty 2008 Annual Report, Issue 7

April 2008 - March 2009 This issue features Announcements, Featured News, Nonlinear Workshop, Projects, Collaborations, Visitors, Awards, Publications, and Support.https://digitalcommons.unomaha.edu/nbcfnewsletter/1006/thumbnail.jp

Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control

Instrumented wheelchair operates based on surface electromyography (sEMG) is one of alternative to assist impairment person for mobility. SEMG is chosen due to good in accuracy and easier preparation to place the electrodes. Motor neuron transmit electrical potential to muscle fibre to perform isometric, concentric or eccentric contraction. These electrical changes that is called Motor Unit Action Potential (MUAP) can be acquired and amplified by electrodes located on targeted muscles changes can be recorded and analysed using sEMG devices. But, sEMG device cost up to USD 2,100 for a sEMG data acquisition device that available on market is one of the drawback to be used by impairment person that most of them has financial problem due to unable to work like before. In addition, it is a closed source system that cannot be modified to improve the accuracy and adding more features. Open source system such as Arduino has limitation of specifications that makes able to apply nonpattern recognition control methods which is simpler and easier compared to pattern recognition. However, classification accuracy is lower than pattern recognition and it cannot be applied to higher number participants from different background and gender. This research aims are to develop an open-source Arduino based sEMG data acquisition device by formulating hybrid automata algorithm to differentiate MUAP activity during wheelchair propulsion. Addition of hybrid automata algorithm to run pattern and non-pattern recognition based control methods is an advantage to increase accuracy in differentiating forward stroke or hand return activity. Electrodes are placed on Biceps (BIC), Triceps (TRI), Extensor (EXT), Flexor (FIX) and MUAP activity recorded for 30 healthy persons. Then, experiment result was validated with simulation result using OpenSim biomedical modelling software. Mean, standard deviation (SD), confidence interval (CI) and maximum point different (MPD) of MUAP were calculated and to be used as thresholds for non-pattern recognition control method in method selection experiment. Meanwhile, pattern recognition is using Probability Density Function (PDF) to determine MUAP according to type of activities. Total of ten control methods determined from population and individual data were tested against another 10 healthy persons to evaluate the algorithm performance. Assessment of each control method done by misclassification matrix looking at True Positive (TP) and False Negative (FN) of power assist system activation period. Developed sEMG data acquisition device that is operated by Arduino MEGA 2560 and Myoware muscle sensors with sampling rate of above 400Hz successfully recorded MUAP from four arm muscles. Furthermore, 2.5 ms of average data latency for device to record, analyse, validate and creating commands to activate the power assist system. Data obtained from the device shows that most active muscle during wheelchair propulsion is TRI, followed by BIC and matched to OpenSim simulation result. In method selection experiment, 96.28% of average accuracy was achieved and different control methods were selected by misclassification matrix for each of persons. This method would be a control method to activate power assist system and selected based on conditions set in the algorithm. These findings indicated that open source Arduino board is capable of running real time pattern, non-pattern recognition based control methods by producing classification accuracy up to 99.48% even though it is known as just a microcontroller that has limitation to run complex classifiers. At the same time, a device that cost less than USD200 has 400Hz of sampling rate is as good as closed source device that is come with expensive price tag to own it. Based on algorithm evaluation, it shows that one control method couldn’t fit to all persons as per proven in method selection experiment. Different person has different control method that suit them the most. Lastly, BIC and TRI can be reference muscles to activate assistive device in instrumented wheelchair that is using propulsion as indication

P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors

Includes bibliographical references.2016 Fall.Brain-computer interfaces (BCIs) allow interactions between human beings and comput- ers without using voluntary muscle. Enormous research effort has been employed in the last few decades to design convenient and user-friendly interfaces. The aim of this study is to provide the people with severe neuromuscular disorders a new augmentative communication technology so that they can express their wishes and communicate with others. The research investigates the capability of Emotiv EPOC+ headset to capture and record one of the BCIs signals called P300 that is used in several applications such as the P300 speller. The P300 speller is a BCI system used to enable severely disabled people to spell words and convey their thoughts without any physical effort. In this thesis, the effects of matrix size, flash duration, and colors were studied. Data are collected from five healthy subjects in their home environments. Different programs are used in this experiment such as OpenViBE platform and MATLAB to pre-process and classify the EEG data. Moreover, the Linear Discriminate Analysis (LDA) classification algorithm is used to classify the data into target and non-target samples

A novel EMG interface for individuals with tetraplegia to pilot robot hand grasping

International audienceThis article introduces a new human-machine interface for individuals with tetraplegia. We investigated the feasibility of piloting an assistive device by processing supra-lesional muscle responses online. The ability to voluntarily contract a set of selected muscles was assessed in five spinal cord-injured subjects through electromyographic (EMG) analysis. Two subjects were also asked to use the EMG interface to control palmar and lateral grasping of a robot hand. The use of different muscles and control modalities was also assessed. These preliminary results open the way to new interface solutions for high-level spinal cord-injured patients

Co-adaptive control strategies in assistive Brain-Machine Interfaces

A large number of people with severe motor disabilities cannot access any of the available control inputs of current assistive products, which typically rely on residual motor functions. These patients are therefore unable to fully benefit from existent assistive technologies, including communication interfaces and assistive robotics. In this context, electroencephalography-based Brain-Machine Interfaces (BMIs) offer a potential non-invasive solution to exploit a non-muscular channel for communication and control of assistive robotic devices, such as a wheelchair, a telepresence robot, or a neuroprosthesis. Still, non-invasive BMIs currently suffer from limitations, such as lack of precision, robustness and comfort, which prevent their practical implementation in assistive technologies. The goal of this PhD research is to produce scientific and technical developments to advance the state of the art of assistive interfaces and service robotics based on BMI paradigms. Two main research paths to the design of effective control strategies were considered in this project. The first one is the design of hybrid systems, based on the combination of the BMI together with gaze control, which is a long-lasting motor function in many paralyzed patients. Such approach allows to increase the degrees of freedom available for the control. The second approach consists in the inclusion of adaptive techniques into the BMI design. This allows to transform robotic tools and devices into active assistants able to co-evolve with the user, and learn new rules of behavior to solve tasks, rather than passively executing external commands. Following these strategies, the contributions of this work can be categorized based on the typology of mental signal exploited for the control. These include: 1) the use of active signals for the development and implementation of hybrid eyetracking and BMI control policies, for both communication and control of robotic systems; 2) the exploitation of passive mental processes to increase the adaptability of an autonomous controller to the user\u2019s intention and psychophysiological state, in a reinforcement learning framework; 3) the integration of brain active and passive control signals, to achieve adaptation within the BMI architecture at the level of feature extraction and classification

Desarrollo de un sistema para la evaluación cinesiológica de la propulsión en usuarios de silla de ruedas

Programa de Doctorado en Alto Rendimiento DeportivoIntroducción: Las personas con lesión medular utilizan una silla de ruedas como su medio de locomoción, la propulsión implica movimientos repetidos con una demanda muscular alta lo que aumenta el riesgo de lesiones en las extremidades superiores, esto hace que pierdan independencia y movilidad. La patología por sobreuso es una de las complicaciones más frecuentes. Es necesario un análisis de las variables mecánicas de la propulsión para promover una intervención oportuna, asertiva y evitar lesiones. Objetivo: Diseñar y construir un sistema que evalúe desde el punto de vista cinesiológico la propulsión de silla de ruedas manuales. Material y Métodos: Se diseñó un sistema basado en un ergómetro sincronizado con la medición de torque y velocidad. Un modelo biomecánico de miembros torácicos instrumentado con sensores inerciales para obtener la cinemática, así como los patrones de propulsión, y se adquirió la señal de EMG al mismo tiempo. Se midió la postura y la configuración de la silla. Se programó una interfaz para sincronizar, adquirir, desplegar y almacenar los datos. Se midieron 5 sujetos sanos y 18 sujetos con lesión medular, cada sujeto utilizó su propia silla de ruedas, de estos 6 sujetos fueron incluidos en un programa de fortalecimiento isotónico, a los que se les midieron los cambios antes y después de la intervención. Resultados: Se identificaron los cuatro patrones de propulsión reportados en la literatura, lazo simple (50%); doble lazo (28%); semicircular (14%); arco (11%). Se identificó el tipo de postura de los usuarios de silla de ruedas y se encontró una correlación entre el peso, el tipo de postura y el tiempo de evolución. No se encontraron correlaciones entre el tipo de patrón y la postura. Respecto del EMG el tríceps y el deltoides anterior presentaron la mayor activación durante el empuje, y el bíceps durante la recuperación, del pectoral la mayor activación fue al momento de terminar la fase de recuperación e iniciar la fase de empuje. Conclusiones: Se adquirieron y sincronizaron las variables cinemáticas, cinéticas y las de EMG, y se obtuvieron los parámetros biomecánicos de la propulsión a través de un modelo biomecánico que permite calcular la cinemática de las articulaciones y los segmentos del brazo. Por otro lado, como parte de la validación se encontraron diferencias en los parámetros medidos por el sistema antes y después de una intervención al someter a los sujetos a un programa de entrenamiento de fuerza isotónica por lo que el sistema fue capaz de medir diferencias en el antes y después de la intervención.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformáticaPostprin

Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning

Gesture recognition using low-resolution instantaneous HD-sEMG images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the data variability between inter-session and inter-subject scenarios presents a great challenge. The existing approaches employed very large and complex deep ConvNet or 2SRNN-based domain adaptation methods to approximate the distribution shift caused by these inter-session and inter-subject data variability. Hence, these methods also require learning over millions of training parameters and a large pre-trained and target domain dataset in both the pre-training and adaptation stages. As a result, it makes high-end resource-bounded and computationally very expensive for deployment in real-time applications. To overcome this problem, we propose a lightweight All-ConvNet+TL model that leverages lightweight All-ConvNet and transfer learning (TL) for the enhancement of inter-session and inter-subject gesture recognition performance. The All-ConvNet+TL model consists solely of convolutional layers, a simple yet efficient framework for learning invariant and discriminative representations to address the distribution shifts caused by inter-session and inter-subject data variability. Experiments on four datasets demonstrate that our proposed methods outperform the most complex existing approaches by a large margin and achieve state-of-the-art results on inter-session and inter-subject scenarios and perform on par or competitively on intra-session gesture recognition. These performance gaps increase even more when a tiny amount (e.g., a single trial) of data is available on the target domain for adaptation. These outstanding experimental results provide evidence that the current state-of-the-art models may be overparameterized for sEMG-based inter-session and inter-subject gesture recognition tasks

A multifunctional brain-computer interface intended for home use: An evaluation with healthy participants and potential end users with dry and gel-based electrodes

Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis