Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals

Surface electromyography (sEMG) signals are crucial in developing human-machine interfaces, as they contain rich information about human neuromuscular activities. &lt;italic&gt;Objective:&lt;/italic&gt; The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. &lt;italic&gt;Methods:&lt;/italic&gt; This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. &lt;italic&gt;Results:&lt;/italic&gt; The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. &lt;italic&gt;Conclusion:&lt;/italic&gt; The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. &lt;italic&gt;Significance:&lt;/italic&gt; This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.</p

Current state of digital signal processing in myoelectric interfaces and related applications

This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.Peer reviewe

Patterns in Motion - From the Detection of Primitives to Steering Animations

In recent decades, the world of technology has developed rapidly. Illustrative of this trend is the growing number of affrdable methods for recording new and bigger data sets. The resulting masses of multivariate and high-dimensional data represent a new challenge for research and industry. This thesis is dedicated to the development of novel methods for processing multivariate time series data, thus meeting this Data Science related challenge. This is done by introducing a range of different methods designed to deal with time series data. The variety of methods re ects the different requirements and the typical stage of data processing ranging from pre-processing to post- processing and data recycling. Many of the techniques introduced work in a general setting. However, various types of motion recordings of human and animal subjects were chosen as representatives of multi-variate time series. The different data modalities include Motion Capture data, accelerations, gyroscopes, electromyography, depth data (Kinect) and animated 3D-meshes. It is the goal of this thesis to provide a deeper understanding of working with multi-variate time series by taking the example of multi-variate motion data. However, in order to maintain an overview of the matter, the thesis follows a basic general pipeline. This pipeline was developed as a guideline for time series processing and is the first contribution of this work. Each part of the thesis represents one important stage of this pipeline which can be summarized under the topics segmentation, analysis and synthesis. Specific examples of different data modalities, processing requirements and methods to meet those are discussed in the chapters of the respective parts. One important contribution of this thesis is a novel method for temporal segmentation of motion data. It is based on the idea of self-similarities within motion data and is capable of unsupervised segmentation of range of motion data into distinct activities and motion primitives. The examples concerned with the analysis of multi-variate time series re ect the role of data analysis in different inter-disciplinary contexts and also the variety of requirements that comes with collaboration with other sciences. These requirements are directly connected to current challenges in data science. Finally, the problem of synthesis of multi-variate time series is discussed using a graph-based example and examples related to rigging or steering of meshes. Synthesis is an important stage in data processing because it creates new data from existing ones in a controlled way. This makes exploiting existing data sets and and access of more condensed data possible, thus providing feasible alternatives to otherwise time-consuming manual processing.Muster in Bewegung - Von der Erkennung von Primitiven zur Steuerung von Animationen In den letzten Jahrzehnten hat sich die Welt der Technologie rapide entwickelt. Beispielhaft für diese Entwicklung ist die wachsende Zahl erschwinglicher Methoden zum Aufzeichnen neuer und immer größerer Datenmengen. Die sich daraus ergebenden Massen multivariater und hochdimensionaler Daten stellen Forschung wie Industrie vor neuartige Probleme. Diese Arbeit ist der Entwicklung neuer Verfahren zur Verarbeitung multivariater Zeitreihen gewidmet und stellt sich damit einer großen Herausforderung, welche unmittelbar mit dem neuen Feld der sogenannten Data Science verbunden ist. In ihr werden ein Reihe von verschiedenen Verfahren zur Verarbeitung multivariater Zeitserien eingeführt. Die verschiedenen Verfahren gehen jeweils auf unterschiedliche Anforderungen und typische Stadien der Datenverarbeitung ein und reichen von Vorverarbeitung bis zur Nachverarbeitung und darüber hinaus zur Wiederverwertung. Viele der vorgestellten Techniken eignen sich zur Verarbeitung allgemeiner multivariater Zeitreihen. Allerdings wurden hier eine Anzahl verschiedenartiger Aufnahmen von menschlichen und tierischen Subjekte ausgewählt, welche als Vertreter für allgemeine multivariate Zeitreihen gelten können. Zu den unterschiedlichen Modalitäten der Aufnahmen gehören Motion Capture Daten, Beschleunigungen, Gyroskopdaten, Elektromyographie, Tiefenbilder ( Kinect ) und animierte 3D -Meshes. Es ist das Ziel dieser Arbeit, am Beispiel der multivariaten Bewegungsdaten ein tieferes Verstndnis für den Umgang mit multivariaten Zeitreihen zu vermitteln. Um jedoch einen Überblick ber die Materie zu wahren, folgt sie jedoch einer grundlegenden und allgemeinen Pipeline. Diese Pipeline wurde als Leitfaden für die Verarbeitung von Zeitreihen entwickelt und ist der erste Beitrag dieser Arbeit. Jeder weitere Teil der Arbeit behandelt eine von drei größeren Stationen in der Pipeline, welche sich unter unter die Themen Segmentierung, Analyse und Synthese eingliedern lassen. Beispiele verschiedener Datenmodalitäten und Anforderungen an ihre Verarbeitung erläutern die jeweiligen Verfahren. Ein wichtiger Beitrag dieser Arbeit ist ein neuartiges Verfahren zur zeitlichen Segmentierung von Bewegungsdaten. Dieses basiert auf der Idee der Selbstähnlichkeit von Bewegungsdaten und ist in der Lage, verschiedenste Bewegungsdaten voll-automatisch in unterschiedliche Aktivitäten und Bewegungs-Primitive zu zerlegen. Die Beispiele fr die Analyse multivariater Zeitreihen spiegeln die Rolle der Datenanalyse in verschiedenen interdisziplinären Zusammenhänge besonders wider und illustrieren auch die Vielfalt der Anforderungen, die sich in interdisziplinären Kontexten auftun. Schließlich wird das Problem der Synthese multivariater Zeitreihen unter Verwendung eines graph-basierten und eines Steering Beispiels diskutiert. Synthese ist insofern ein wichtiger Schritt in der Datenverarbeitung, da sie es erlaubt, auf kontrollierte Art neue Daten aus vorhandenen zu erzeugen. Dies macht die Nutzung bestehender Datensätze und den Zugang zu dichteren Datenmodellen möglich, wodurch Alternativen zur ansonsten zeitaufwendigen manuellen Verarbeitung aufgezeigt werden

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Deep Vision for Prosthetic Grasp

Ph. D. ThesisThe loss of the hand can limit the natural ability of individuals in grasping and manipulating objects and affect their quality of life. Prosthetic hands can aid the users in overcoming these limitations and regaining their ability. Despite considerable technical advances, the control of commercial hand prostheses is still limited to few degrees of freedom. Furthermore, switching a prosthetic hand into a desired grip mode can be tiring. Therefore, the performance of hand prostheses should improve greatly. The main aim of this thesis is to improve the functionality, performance and flexibility of current hand prostheses by augmentation of current commercial hand prosthetics with a vision module. By offering the prosthesis the capacity to see objects, appropriate grip modes can be determined autonomously and quickly. Several deep learning-based approaches were designed in this thesis to realise such a vision-reinforced prosthetic system. Importantly, the user, interacting with this learning structure, may act as a supervisor to accept or correct the suggested grasp. Amputee participants evaluated the designed system and provided feedback. The following objectives for prosthetic hands were met: 1. Chapter 3: Design, implementation and real-time testing of a semi-autonomous vision-reinforced prosthetic control structure, empowered with a baseline convolutional neural network deep learning structure. 2. Chapter 4: Development of advanced deep learning structure to simultaneously detect and estimate grasp maps for unknown objects, in presence of ambiguity. 3. Chapter 5: Design and development of several deep learning set-ups for concurrent depth and grasp map as well as human grasp type prediction. Publicly available datasets, consisting of common graspable objects, namely Amsterdam library of object images (ALOI) and Cornell grasp library were used within this thesis. Moreover, to have access to real data, a small dataset of household objects was gathered for the experiments, that is Newcastle Grasp Library.EPSRC, School of Engineering Newcastle University

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important to improve the quality of the daily life of the patients with motion disorders. Although neuroprosthetic devices restore partially the lost functionality, the user acceptance is low, possibly due to the artificial and unnatural operation of the devices. This thesis addresses this problem in reach-to-grasp motions with the development of shared control approaches that enable a seamless and more natural operation of hand prosthesis. In the first part, we focus on the identification of the grasping intention during the reach-to-grasp motion with able-bodied individuals. We propose an Electromyographic (EMG)-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e. before the final grasp/hand pre-shape takes place. In this approach, the utilization of Echo State Networks encloses efficiently the dynamics of the muscle activation enabling a fast identification of the grasp type in real-time. We also examine the impact of different object distance and speed on the detection time and accuracy of the classifier. Although the distance from the object has no significant effect, fast motions influence significantly the performance. In the second part, we evaluate and extend our approach on four real end-users, i.e. individuals with below the elbow amputation. For addressing the variability of the EMG signals, we separate the reach-to-grasp motion into three phases, with respect to the arm extension. A multivariate analysis of variance on the muscle activity reveals significant differences among the motion phases. Additionally, we examine the classification performance on these phases and compare the performance of different pattern recognition methods. An on-line evaluation with an upper-limb prosthesis shows that the inclusion of the reaching motion in the training of the classifier improves importantly classification accuracy. In the last part of the thesis, we explore further the concept of motion phases on the EMG signals and its potentials on addressing the variability of the signals. We model the dynamic muscle contractions of each class with Gaussian distributions over the different phases of the overall motion. We extend our previous analysis providing insights on the LDA projection and quantifying the similarity of the distributions of the classes (i.e grasp types) with the Hellinger distance. We notice larger values of the Helinger distance and, thus, smaller overlaps among the classes with the segmentation to motion phases. A Linear Discriminant Analysis classifier with phase segmentation affects positively the classification accuracy

EMG-to-Speech: Direct Generation of Speech from Facial Electromyographic Signals

The general objective of this work is the design, implementation, improvement and evaluation of a system that uses surface electromyographic (EMG) signals and directly synthesizes an audible speech output: EMG-to-speech

Robust and reliable hand gesture recognition for myoelectric control

Surface Electromyography (sEMG) is a physiological signal to record the electrical activity of muscles by electrodes applied to the skin. In the context of Muscle Computer Interaction (MCI), systems are controlled by transforming myoelectric signals into interaction commands that convey the intent of user movement, mostly for rehabilitation purposes. Taking the myoeletric hand prosthetic control as an example, using sEMG recorded from the remaining muscles of the stump can be considered as the most natural way for amputees who lose their limbs to perform activities of daily living with the aid of prostheses. Although the earliest myoelectric control research can date back to the 1950s, there still exist considerable challenges to address the significant gap between academic research and industrial applications. Most recently, pattern recognition-based control is being developed rapidly to improve the dexterity of myoelectric prosthetic devices due to the recent development of machine learning and deep learning techniques. It is clear that the performance of Hand Gesture Recognition (HGR) plays an essential role in pattern recognition-based control systems. However, in reality, the tremendous success in achieving very high sEMG-based HGR accuracy (≥ 90%) reported in scientific articles produced only limited clinical or commercial impact. As many have reported, its real-time performance tends to degrade significantly as a result of many confounding factors, such as electrode shift, sweating, fatigue, and day-to-day variation. The main interest of the present thesis is, therefore, to improve the robustness of sEMG-based HGR by taking advantage of the most recent advanced deep learning techniques to address several practical concerns. Furthermore, the challenge of this research problem has been reinforced by only considering using raw sparse multichannel sEMG signals as input. Firstly, a framework for designing an uncertainty-aware sEMG-based hand gesture classifier is proposed. Applying it allows us to quickly build a model with the ability to make its inference along with explainable quantified multidimensional uncertainties. This addresses the black-box concern of the HGR process directly. Secondly, to fill the gap of lacking consensus on the definition of model reliability in this field, a proper definition of model reliability is proposed. Based on it, reliability analysis can be performed as a new dimension of evaluation to help select the best model without relying only on classification accuracy. Our extensive experimental results have shown the efficiency of the proposed reliability analysis, which encourages researchers to use it as a supplementary tool for model evaluation. Next, an uncertainty-aware model is designed based on the proposed framework to address the low robustness of hand grasp recognition. This offers an opportunity to investigate whether reliable models can achieve robust performance. The results show that the proposed model can improve the long-term robustness of hand grasp recognition by rejecting highly uncertain predictions. Finally, a simple but effective normalisation approach is proposed to improve the robustness of inter-subject HGR, thus addressing the clinical challenge of having only a limited amount of data from any individual. The comparison results show that better performance can be obtained by it compared to a state-of-the-art (SoA) transfer learning method when only one training cycle is available. In summary, this study presents promising methods to pursue an accurate, robust, and reliable classifier, which is the overarching goal for sEMG-based HGR. The direction for future work would be the inclusion of these in real-time myoelectric control applications

Development and optimization of a low-cost myoelectric upper limb prosthesis

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), 2022, Universidade de Lisboa, Faculdade de CiênciasIn recent years, the increase in the number of accidents, chronic diseases, such as diabetes, and the impoverishment of certain developing countries have contributed to a significant increase in prostheses users. The loss of a particular limb entails numerous changes in the daily life of each user, which are amplified when the user loses their hand. Therefore, replacing the hand is an urgent necessity. Developing upper limb prostheses will allow the re-establishment of the physical and motor functions of the upper limb as well as reduction of the rates of depression. Therefore, the prosthetic industry has been reinventing itself and evolving. It is already possible to control a prosthesis through the user's myoelectric signals, control known as pattern recognition control. In addition, additive manufacturing technologies such as 3D printing have gained strength in prosthetics. The use of this type of technology allows the product to reach the user much faster and reduces the weight of the devices, making them lighter. Despite these advances, the rejection rate of this type of device is still high since most prostheses available on the market are slow, expensive and heavy. Because of that, academia and institutions have been investigating ways to overcome these limitations. Nevertheless, the dependence on the number of acquisition channels is still limiting since most users do not have a large available forearm surface area to acquire the user’s myoelectric signals. This work intends to solve some of these problems and answer the questions imposed by the industry and researchers. The main objective is to test if developing a subject independent, fast and simple microcontroller is possible. Subsequently, we recorded data from forty volunteers through the BIOPAC acquisition system. After that, the signals were filtered through two different processes. The first was digital filtering and the application of wavelet threshold noise reduction. Later, the signal was divided into smaller windows (100 and 250 milliseconds) and thirteen features were extracted in the temporal domain. During all these steps, the MatLab® software was used. After extraction, three feature selection methods were used to optimize the classification process, where machine learning algorithms are implemented. The classification was divided into different parts. First, the classifier had to distinguish whether the volunteer was making some movement or was at rest. In the case of detected movement, the classifier would have to, on a second level, try to understand if they were moving only one finger or performing a movement that involved the flexion of more than one finger (grip). If the volunteer was performing a grip on the third level, the classifier would have to identify whether the volunteer was performing a spherical or triad grip. Finally, to understand the influence of the database on the classification, two methods were used: cross-validation and split validation. After analysing the results, the e-NABLE Unlimbited arm was printed on The Original Prusa i3 MK3, where polylactic acid (PLA) was used. This dissertation showed that the results obtained in the 250-millisecond window were better than the obtained ones in a 100-millisecond window. In general, the best classifier was the K-Nearest Neighbours (KNN) with k=2, except for the first level that was LDA. The best results were obtained for the first classification level, with an accuracy greater than 90%. Although the results obtained for the second and third levels were close to 80%, it was concluded that it was impossible to develop a microcontroller dependent only on one acquisition channel. These results agree with the anatomical characteristics since they are originated from the same muscle group. The cross-validation results were lower than those obtained in the training-test methodology, which allowed us to conclude that the inter variability that exists between the subjects significantly affects the classification performance. Furthermore, both the dominant and non-dominant arms were used in this work, which also increased the discrepancy between signals. Indeed, the results showed that it is impossible to develop a microcontroller adaptable to all users. Therefore, in the future, the best path will be to opt for the customization of the prototype. In order to test the implementation of a microcontroller in the printed model, it was necessary to design a support structure in Solidworks that would support the motors used to flex the fingers and Arduino to control the motors. Consequently, the e-NABLE model was re adapted, making it possible to develop a clinical training prototype. Even though it is a training prototype, it is lighter than those on the market and cheaper. The objectives of this work have been fulfilled and many answers have been given. However, there is always space for improvement. Although, this dissertation has some limitations, it certainly contributed to clarify many of the doubts that still exist in the scientific community. Hopefully, it will help to further develop the prosthetic industry.Nos últimos anos, o aumento do número de acidentes por doenças crónicas, como, por exemplo, a diabetes, e o empobrecimento de determinados países em desenvolvimento têm contribuído para um aumento significativo no número de utilizadores de próteses. A perda de um determinado membro acarreta inúmeras mudanças no dia-a-dia de cada utilizador. Estas são amplificadas quando a perda é referente à mão ou parte do antebraço. A mão é uma ferramenta essencial no dia-a-dia de cada ser humano, uma vez que é através dela que são realizadas as atividades básicas, como, por exemplo, tomar banho, lavar os dentes, comer, preparar refeições, etc. A substituição desta ferramenta é, portanto, uma necessidade, não só porque permitirá restabelecer as funções físicas e motoras do membro superior, como, também, reduzirá o nível de dependência destes utilizadores de outrem e, consequentemente, das taxas de depressão. Para colmatar as necessidades dos utilizadores, a indústria prostética tem-se reinventado e evoluído, desenvolvendo próteses para o membro superior cada vez mais sofisticadas. Com efeito, já é possível controlar uma prótese através da leitura e análise dos sinais mioelétricos do próprio utilizador, o que é denominado por muitos investigadores de controlo por reconhecimento de padrões. Este tipo de controlo é personalizável e permite adaptar a prótese a cada utilizador. Para além do uso de sinais elétricos provenientes do musculo do utilizador, a impressão 3D, uma técnica de manufatura aditiva, têm ganho força no campo da prostética. Por conseguinte, nos últimos anos os investigadores têm impresso inúmeros modelos com diferentes materiais que vão desde o uso de termoplásticos, ao uso de materiais flexíveis. A utilização deste tipo de tecnologia permite, para além de uma rápida entrega do produto ao utilizador, uma diminuição no tempo de construção de uma prótese tornando-a mais leve e barata. Além do mais, a impressão 3D permite criar protótipos mais sustentáveis, uma vez que existe uma redução na quantidade de material desperdiçado. Embora já existam inúmeras soluções, a taxa de rejeição deste tipo de dispositivos é ainda bastante elevada, uma vez que a maioria das próteses disponíveis no mercado, nomeadamente as mioelétricas, são lentas, caras e pesadas. Ainda que existam alguns estudos que se debrucem neste tipo de tecnologias, bem como na sua evolução científica, o número de elétrodos utilizados é ainda significativo. Desta forma, e, tendo em conta que a maioria dos utilizadores não possuí uma área de superfície do antebraço suficiente para ser feita a aquisição dos sinais mioelétricos, o trabalho feito pela academia não se revelou tão contributivo para a indústria prostética como este prometia inicialmente. Este trabalho pretende resolver alguns desses problemas e responder às questões mais impostas pela indústria e investigadores, para que, no futuro, o número de utilizadores possa aumentar, assim como o seu índice de satisfação relativamente ao produto. Para tal, recolheram-se os sinais mioelétricos de quarenta voluntários, através do sistema de aquisição BIOPAC. Após a recolha, filtraram-se os sinais de seis voluntários através de dois processos diferentes. No primeiro, utilizaram-se filtros digitais e no segundo aplicou-se a transformada de onda para a redução do ruído. De seguida, o sinal foi segmentado em janelas mais pequenas de 100 e 250 milissegundos e extraíram-se treze features no domínio temporal. Para que o processo de classificação fosse otimizado, foram aplicados três métodos de seleção de features. A classificação foi dividida em três níveis diferentes nos quais dois algoritmos de aprendizagem automática foram implementados, individualmente. No primeiro nível, o objetivo foi a distinção entre os momentos em que o voluntário fazia movimento ou que estava em repouso. Caso o output do classificador fosse a classe movimento, este teria de, num segundo nível, tentar perceber se o voluntário estaria a mexer apenas um dedo ou a realizar um movimento que envolvesse a flexão de mais de que um dedo (preensão). No caso de uma preensão, passava-se ao terceiro nível onde o classificador teria de identificar se o voluntário estaria a realizar a preensão esférica ou em tríade. Para todos os níveis de classificação, obtiveram-se resultados para o método de validação cruzada e o método de teste e treino, sendo que neste, 70% dos dados foram utilizados como conjunto de treino e 30% como teste. Efetuada a análise dos resultados, escolheu-se um dos modelos da comunidade e-NABLE. O modelo foi impresso na impressora The Original Prusa i3 MK3S e o material escolhido foi o ácido poliláctico (PLA). Para que fosse possível testar a implementação de um microcontrolador num modelo que originalmente depende da flexão do cotovelo realizada pelo utilizador, foi necessário desenhar uma estrutura de suporte que suportasse, não só os motores utilizados para flexionar os dedos, como, também, o Arduíno. O suporte desenhado foi impresso com o mesmo material e com a mesma impressora. Os resultados obtidos mostraram que a janela de 250 milissegundo foi a melhor e que, regra geral, o melhor classificador é o K-Nearest Neighbors (KNN) com k=2, com exceção do primeiro nível, em que o melhor classificador foi o Linear Discriminant Analysis (LDA). Os melhores resultados obtiveram-se no primeiro nível de classificação onde a accuracy foi superior a 90%. Embora os resultados obtidos para o segundo e terceiro nível tenham sido próximos de 80%, concluiu-se que não era possível desenvolver um microcontrolador dependente apenas de um canal de aquisição. Tal era expectável, uma vez que os movimentos estudados são originados pelo mesmo grupo muscular e a intervariabilidade dos sujeitos um fator significativo. Os resultados da validação cruzada foram menos precisos do que os obtidos para a metodologia de treino-teste, o que permitiu concluir que a intervariabilidade existente entre os voluntários afeta significativamente o processo de classificação. Para além disso, os voluntários utilizaram o braço dominante e o braço não dominante, o que acabou por aumentar a discrepância entre os sinais recolhidos. Com efeito, os resultados mostraram que não é possível desenvolver um microcontrolador que seja adaptável a todos os utilizadores e, portanto, no futuro, o melhor caminho será optar pela personalização do protótipo. Tendo o conhecimento prévio desta evidência, o protótipo desenvolvido neste trabalho apenas servirá como protótipo de treino para o utilizador. Ainda assim, este é bem mais leve que os existentes no mercado e muito mais barato. Nele é ainda possível testar e controlar alguns dos componentes que no futuro irão fazer parte da prótese completa, prevenindo acidentes. Não obstante o cumprimento dos objetivos deste trabalho e das muitas respostas que por ele foram dadas, existe sempre espaço para melhorias. Dado à limitação de tempo, não foi possível testar o microcontrolador em tempo-real nem efetuar testes mecânicos de flexibilidade e resistência dos materiais da prótese. Deste modo, seria interessante no futuro fazer testes de performance em tempo real e submeter a prótese a condições extremas, para que a tensão elástica e a tensão dos pins sejam testadas. Para além disso, testar os mecanismos de segurança da prótese quando o utilizador tem de fazer muita força é fundamental. O teste destes parâmetros evitará a ocorrência de falhas que poderão magoar o utilizador, bem como estragar os objetos com os quais a prótese poderá interagir. Por fim, é necessário melhorar o aspeto cosmético das próteses. Para que isso aconteça, poderão ser utilizados polímeros com uma coloração próxima do tom da pele do utilizador. Uma outra forma de melhorar este aspeto, seria fazer o scanning do braço saudável do utilizador e usar materiais flexíveis para as articulações e dedos que, juntamente com uma palma de termoplásticos resistentes e um microcontrolador, permitissem um movimento bastante natural próximo do biológico. Em suma, apesar de algumas limitações, este trabalho contribuiu para o esclarecimento de muitas das dúvidas que ainda existiam na comunidade científica e ajudará a desenvolver a indústria prostética