MYOARM: prótesis robótica con sensado emg y entrenamiento con redes neuronales

El proyecto consiste en la creación de un brazo robótico controlado remotamente a través de un brazalete desarrollado por Thalmic Labs, el cual es capaz de leer los biopotenciales de los músculos residentes del muñón de los usuarios. Este proyecto, tiene como propósito la creación de una alternativa económica a las prótesis activas no invasivas que existen en la actualidad. Nuestra prótesis es capaz de realizar las mismas funciones, pero a un precio mucho más asequible. Para poder realizar todas las funciones que una articulación normal, el brazo cuenta con varios elementos: Cuerdas que simulan tendones y permiten el movimiento de los dedos, engranajes que permiten el giro de la muñeca y motores, los cuales son capaces de generar el movimiento en función de los datos extraídos del brazalete. El brazalete es el encargado de transmitir la información de la mano al brazo robótico a través de un módulo inalámbrico que lo conecta con el ordenador, donde la señal que extrae el brazalete pasa por un proceso de filtrado para quedarnos con la información que nos interesa y transmitirla mediante el puerto USB a un microcontrolador, el cual será el encargado de mover los motores según las señales que reciba. Para evitar errores en la medida de los sensores, la información recibida por el pc proveniente del brazalete pasa un proceso de entrenamiento mediante redes neuronales antes de ser enviada al brazo robótico.The project consists of the creation of a robotic arm controlled remotely through a brace developed by Thalmic Labs, which can read the biopotentials of the muscles of the limb of the users. This project aims to create an economic alternative to noninvasive active prostheses that exist today. Our prosthesis can perform the same functions but at a so much affordable price. To perform all the functions of a normal joint, the arm has several elements. Strings that simulate tendons and allow the movement of the fingers, gears that allow the rotation of the wrist and motors, which can generate movement based on the data extracted from the bracelet. The bracelet is responsible for transmitting information from the hand to the robotic arm through a wireless module that connects it with the computer, where the signal that extracts the bracelet goes through a filtering process to keep the information that interests us and Transmit it through the USB port to a microcontroller, which will be in charge of moving the engines according to the signals received. To avoid errors in the measurement of the sensors, the information received from the bracelet is trained in the computer using a Neural Network architecture before sending the information to the robotic arm.Universidad de Sevilla. TEP- 108: Robótica y Tecnología de Computadore

Electromyography Signal-Based Gesture Recognition for Human-Machine Interaction in Real-Time Through Model Calibration

In this work, we achieve up to 92% classification accuracy of electromyographic data between five gestures in pseudo-real-time. Most current state-of-the-art methods in electromyographical signal processing are unable to classify real-time data in a post-learning environment, that is, after the model is trained and results are analysed. In this work we show that a process of model calibration is able to lead models from 67.87% real-time classification accuracy to 91.93%, an increase of 24.06%. We also show that an ensemble of classical machine learning models can outperform a Deep Neural Network. An original dataset of EMG data is collected from 15 subjects for 4 gestures (Open-Fingers, Wave-Out, Wave-in, Close-fist) using a Myo Armband for measurement of forearm muscle activity. The dataset is cleaned between gesture performances on a per-subject basis and a sliding temporal window algorithm is used to perform statistical analysis of EMG signals and extract meaningful mathematical features as input to the learning paradigms. The classifiers used in this paper include a Random Forest, a Support Vector Machine, a Multilayer Perceptron, and a Deep Neural Network. The three classical classifiers are combined into a single model through an ensemble voting system which scores 91.93% compared to the Deep Neural Network which achieves a performance of 88.68%, both after calibrating to a subject and performing real-time classification (pre-calibration scores for the two being 67.87% and 74.27%, respectively)

Kvantitativna analiza pokreta u rehabilitaciji neuroloških poremećaja korišćenjem vizuelnih i nosivih senzora.

Neuroloska oboljenja, kao sto su Parkinsonova bolest i slog, dovode do ozbiljnih motornih poremecaja, smanjuju kvalitet zivota pacijenata i mogu da uzrokuju smrt. Rana dijagnoza i adekvatno lecenje su krucijalni faktori za drzanje bolesti pod kontrolom, kako bi se omogucio normalan svakodnevni zivot pacijenata. Lecenje neurolo skih bolesti obicno ukljucuje rehabilitacionu terapiju i terapiju lekovima, koje se prilagodavaju u skladu sa stanjem pacijenta tokom vremena. Tradicionalne tehnike evaluacije u dijagnozi i monitoringu neuroloskih bolesti oslanjaju se na klinicke evaluacione alate, tacnije specijalno dizajnirane klinicke testove i skale. Medutim, iako su korisne i najcesce koriscene, klinicke skale su sklone subjektivnim ocenama i nepreciznoj interpretaciji performanse pacijenta...Neurological disorders, such as Parkinson's disease (PD) and stroke, lead to serious motor disabilities, decrease the patients' quality of life and can cause the mortality. Early diagnosis and adequate disease treatment are thus crucial factors towards keeping the disease under control in order to enable the normal every-day life of patients. The treatment of neurological disorders usually includes the rehabilitation therapy and drug treatment, that are adapted based on the evaluation of the patient state over time. Conventional evaluation techniques for diagnosis and monitoring in neurological disorders rely on the clinical assessment tools i.e. specially designed clinical tests and scales. However, although benecial and commonly used, those scales are descriptive (qualitative), primarily intended to be carried out by a trained neurologist, and are prone to subjective rating and imprecise interpretation of patient's performance..

Review of three-dimensional human-computer interaction with focus on the leap motion controller

Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given

Classification of Myopotentials of Hand's Motion to Control Applications

Import 23/08/2017V této diplomové práci je realizován systém pro klasifikaci myopotenciálů gest ruky. Prvním cílem bylo vytvořit hardware, který by byl schopen přenést nezarušený a správně zesílený signál myopotenciálů svalů ke zpracování do PC. Druhým cílem bylo naprogramovat algoritmus, který myopotenciály klasifikuje do určených gest ruky. Kombinací filtrů 2. řádu a správného zesílení byl vytvořen hardwarový prototyp obsahující čtyři měřící kanály pro snímání myopotenciálů. Z důvodu použití aktivních elektrod je uživatel galvanicky oddělen od zdroje. Pro digitalizaci a přenos dat byl vybrán mikrokontrolér Arduino Nano a naprogramován dle vytvořeného komunikačního protokolu. Programování počítačové aplikace je realizováno v jazyce C#. Zpracování signálu a grafické zobrazení měřeného signálu probíhá v reálném čase. Dle algoritmu adaptivní segmentace je zjišťována hranice provedeného gesta. Pomocí navržených fuzzy množin a systému váhování je určeno jedno z pěti (nebo žádné) gest ruky, které bylo provedeno.Realization of the system for classification of hand’s gestures is described in this master’s thesis. The first goal was to create hardware that would be able to measure signal of myopotentials for computer analysis without external noise and with right amplification. The second goal was to program an algorithm which could classify specific gestures of hand. Hardware prototype of four measuring channels was created by combination of 2nd order filters and right amount amplification. The user is isolated from the power source using galvanic isolation because of usage of active electrodes. For digitizing the data, the Arduino Nano microcontroller was selected and programed using defined communication protocol. The computer software is programed in C# programming language. Signal processing and drawing to user interface is in real time. The one of five possible gestures that user made is chosen using fuzzy logic and designed system of scaling.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Formulation of a new gradient descent MARG orientation algorithm: case study on robot teleoperation

We introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computa- tional efficiency. Analytic and experimental results demonstrate faster convergence for multiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is pro- ven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom (DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography (MMG) muscle sensing to control robot arm movement and grasp simultaneously, demon- strating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measure- ment and MMG in HMI. We believe the new algorithm holds the potential to impact a very wide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation for robotic teleoperation systems with necessary robustness for use in the field

Skills Assessment in Arthroscopic Surgery by Processing Kinematic, Force, and Bio-signal Data

Arthroscopic surgery is a type of Minimally Invasive Surgery (MIS) performed in human joints, which can be used for diagnostic or treatment purposes. The nature of this type of surgery makes it such that surgeons require extensive training to become experts at performing surgical tasks in tight environments and with reduced force feedback. MIS increases the possibility of erroneous actions, which could result in injury to the patient. Many of these injuries can be prevented by implementing appropriate training and skills assessment methods. Various performance methods, including Global Rating Scales and technical measures, have been proposed in the literature. However, there is still a need to further improve the accuracy of surgical skills assessment and improve its ability to distinguish fine variations in surgical proficiency. The main goal of this thesis is to enhance surgical, and specifically, arthroscopic skills assessment. The optimal assessment method should be objective, distinguish between subjects with different levels of expertise, and be computationally efficient. This thesis proposes a new method of investigating surgical skills by introducing energy expenditure metrics. To this end, two main approaches are pursued: 1) evaluating the kinematics of instrument motion, and 2) exploring the muscle activity of trainees. Mechanical energy expenditure and work are investigated for a variety of laparoscopic and arthroscopic tasks. The results obtained in this thesis demonstrate that expert surgeons expend less energy than novice trainees. The different forms of mechanical energy expenditure were combined through optimization methods and machine learning algorithms. An optimum two-step optimization method for classifying trainees into detailed levels of expertise is proposed that demonstrates an enhanced ability to determine the level of expertise of trainees compared to other published methods. Furthermore, performance metrics are proposed based on electromyography signals of the forearm muscles, which are recorded using a wearable device. These results also demonstrate that the metrics defined based on muscle activity can be used for arthroscopic skills assessment. The energy-based metrics and the muscle activity metrics demonstrated the ability to identify levels of expertise, with accuracy levels as high as 95% and 100%, respectively. The primary contribution of this thesis is the development of novel metrics and assessment methods based on energy expenditure and muscle activity. The methods presented advance our knowledge of the characteristics of dexterous performance and add another perspective to quantifying surgical proficiency