Perintah Kontrol Gerak Kursi Roda Elektrik Menggunakan Sensor Elektromiograf

Paralysis is a disease that can limit the mobility of the sufferer. One solution that can help people with paralysis in carrying out their mobility is the use of an electric wheelchair. In this study, an electric wheelchair with specifications where the wheelchair motion control uses muscles on both arms, so that the electric wheelchair is very suitable for patients with paralysis in the legs and weak hand strength in turning the wheels from the wheelchair. The input of motion control commands is carried out through an electromyograph sensor mounted on the flexor muscle in both patients’ arms. The output of each sensor is given a threshold of 2 volts to distinguish control commands or not. When the sensor output is more than the same as the threshold, it is considered logic one and the other is considered logic zero. The method is used to interpret the output as a control command by impulse detection. The electric wheelchair movement that can be done is forward, turn right, and turn left

Evaluation of misclassification matrix method in validation of an assistive device for manual wheelchair propulsion

Classification accuracy is essential in the bio signal’s performance-based assistive devices. In this study, surface electromyography (SEMG) signals acquisition was extracted from 3 healthy right-handed participants. SEMG signal was processed, and Motor Unit Action Potential (MUAP) was determined. Accuracy, precision, sensitivity and specificity were calculated in real-time based on individual MUAP, critically compared with pattern and non-pattern recognition control methods by Misclassification Matrix inserted into Arduino MEGA 2560 Microcontroller. The results indicated that the performance of each control method is different for every participant and a comparison tool is a must to select the best out of it. It shows that the misclassification matrix filtered the best control method for participant 1 as Probability Density Function, no for participant 2 and Maximum Point Different (MPD) for participant 3 based on determined conditions

Simulador para treinamento de cadeirantes em ambiente virtual acionado por comandos musculares e/ou visuais

The act of driving an electric-powered wheelchair seems, at first glance, a simple task to perform. In reality, however, the ability to conduct a wheelchair independently requires specific motor, visual and cognitive abilities, and attempting to perform such an act without adequate preparation and knowledge may pose a risk, not only for the user in question, but also to the individuals at close proximity. In this perspective, the present work proposes a tool for training and adaptation of users of electric-powered wheelchairs, a simulator aimed mainly at users with little experience with such device, through use of Virtual Reality. By using this simulator, it might be possible to assist in the process of adapting to the new technology and in acquiring knowledge of how to act in some situations, effectively preparing the new user how to deal with different contexts while driving his electric-powered wheelchair. For the development of this work, a survey was first made with experienced users of electric-powered wheelchairs to discover the main needs and difficulties in driving a wheelchair. After that, a virtual environment was created using the Unity 3D tool, in which three distinct scenarios were initially designed based on the survey results. As of the form of control, three different forms were adapted, so that the simulator also could be used for individuals with severe motor disabilities. Finally, it was done a case study with four users in order to evaluate the tool, both in terms of realism and immersion, as well as in practical utility. In the long term, it is expected that the tool can be used by individuals to train their ability to drive electric-powered wheelchairs effectively and in a completely safe environment.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDissertação (Mestrado)O ato de conduzir uma cadeira de rodas motorizada parece, à primeira vista, uma atividade simples de se realizar. Na realidade, porém, a capacidade de conduzir uma cadeira de rodas de forma independente requer habilidades motoras, visuais e cognitivas específicas, sendo que a tentativa de realização de dito ato sem o preparo e conhecimento adequado pode vir a acarretar um risco, não só para o usuário em questão, mas também para os indivíduos ao redor do mesmo. Nessa perspectiva, o presente trabalho propõe uma ferramenta de treinamento e adaptação para usuários de cadeiras de rodas motorizadas, um simulador voltado principalmente para usuários com pouca experiência de uso com tal dispositivo, utilizando-se para isso de Realidade Virtual. Através do uso deste simulador, pode-se auxiliar no processo de adaptação da nova tecnologia e na aquisição de conhecimento de como agir em possíveis situações, efetivamente preparando o usuário para lidar com diferentes contextos durante a condução de sua cadeira de rodas motorizada. Para o desenvolvimento do trabalho, foi feita primeiro uma pesquisa com usuários experientes de cadeiras de rodas motorizadas para levantamento das principais necessidades e dificuldades na condução. Em seguida, foi criado um cenário virtual usando a ferramenta Unity 3D, no qual foram projetados inicialmente três cenários distintos baseados nos resultados da pesquisa. Como forma de controle, foram adaptadas três formas de controle distintas, para que o simulador pudesse ser utilizado também para indivíduos com deficiências motoras severas. Por fim, foi feito um estudo de caso com quatro usuários para avaliar a ferramenta, tanto em termos de realismo e imersão, quanto em utilidade prática. Em longo prazo, espera-se que a ferramenta possa ser utilizada por indivíduos para praticarem sua habilidade de condução de cadeiras de rodas motorizadas de forma efetiva e completamente segura

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