Hareket Yakalama ve Sanal Gerçeklik Teknolojileri Kullanarak Oyun Tabanlı Rehabilitasyon

İnsanların kas ve sinir sisteminin tahribatı ile ortaya çıkan hastalıklar hayat kalitesi üzerinde ciddi etkiler göstermektedir. Bu hastalıklardan en önemlilerden biri hemiplejidir. Hemipleji, diğer bir adıyla kısmi felç, vücudun sol ve sağ bölgelerini etkileyen sinir sistemi hastalığıdır. Söz konusu hastalıkta, bireylerin beyinlerinde meydana gelen hasarlardan dolayı hareket edememe veya hareket etmekte güçlük yaşanılması gibi sorunlar oluşmaktadır. Bu hastalıkta tedavi ve rehabilitasyon aşaması son derece önemlidir. Hastalığı erken teşhis ederek rehabilitasyon süreci hemen başlatılmalıdır. Diğer vücut fonksiyonlarına zarar verilmeden iyileşme sağlanması tedavinin temel amacıdır. Çalışmamızda, rehabilitasyon süreci aşamasındaki hastaların hareketlerini algılayarak, oyun tabanlı bir sanal gerçeklik uygulaması geliştirilmiştir. Hastanın parmaklarına 10 adet esneklik sensörü ve eklemlerine 13 adet MPU9250 eğim sensörü olmak üzere toplamda 23 adet sensör yerleştirilmiştir. Sensörlerden alınan veriler öncelikle kalibre edilmiştir. Kalibre edilmiş sensörlerden, sanal gerçeklik gözlüğüne gelen gerçek zamanlı veriler ile hemipleji hastalarının hareketleri algılanmıştır. Hemipleji hastalarına uzman fizyoterapistler tarafından verilen hareketlere uygun oyun modu tasarlanmıştır. Sanal gerçeklik gözlüğü takılı olan hasta, oyun moduna göre oyun oynayabilmektedir. Gözlükte gösterilen ve uygulanması istenilen oyun, fizyoterapistler tarafından belirlenmiş hareketlere bağlı bir oyundur. Çalışmanın hemipleji hastalarının iyileşme sürecine önemli katkı sağlayacağı düşünülmektedir

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments

Contributions to physical exercises monitoring with inertial measurement units

Resumen: La monitorización de movimientos trata de obtener información sobre su ejecución, siendo esencial en múltiples aplicaciones, como el seguimiento de terapias físicas. La monitorización tiene un doble objetivo esencial para lograr los beneficios de dichas terapias: asegurar la corrección en la ejecución de movimientos y mejorar la adherencia a los programas prescritos. Para lograr esta monitorización de forma remota y poco intrusiva, se necesitan recursos tecnológicos. Este trabajo se centra en las soluciones basadas en sensores inerciales. Esta tesis estudia los algoritmos de la literatura para el análisis de movimientos con sensores inerciales, determinando un parámetro anatómico requerido en diversas propuestas: la posición de las articulaciones respecto de los sensores, así como longitud de los segmentos anatómicos. En este trabajo se introducen dos algoritmos de calibración anatómica. El primero, basado en mínimos cuadrados, determina el punto o el eje medios de aceleración nula presente en las articulaciones fijas. El algoritmo está adaptado a los movimientos lentos dados en los miembros inferiores para estabilizar las articulaciones. El segundo, adaptado a la variación de la posición relativa del punto de aceleración nula respecto de los sensores a causa del característico tejido blando asociado al cuerpo humano, emplea las medidas inerciales como entradas en un filtro de Kalman extendido. Por otro lado, esta tesis aborda la falta de datos comunes para la evaluación y comparación de los algoritmos. Para ello, se diseña y crea una base de datos centrada en movimientos habituales en rutinas físicas, que se encuentra publicada en Zenodo. Esta base de datos contiene movimientos de calibración articular y de ejercicios de miembros inferiores y superiores ejecutados de forma correcta e incorrecta por 30 voluntarios de ambos sexos con un amplio rango de edades, grabados con cuatro sensores inerciales y un sistema de referencia óptico de alta precisión. Además, las grabaciones se encuentran etiquetadas acorde al tipo de ejercicio realizado y su evaluación. Finalmente, se estudia un segundo enfoque de monitorización de rutinas físicas, cuyo objetivo es reconocer y evaluar simultáneamente los ejercicios ejecutados, retos comúnmente estudiados individualmente. Se proponen tres sistemas que emplean las medidas de cuatro sensores inerciales y difieren en el nivel de detalle en las salidas del sistema. Para realizar las clasificaciones propuestas, se evalúan seis algoritmos de machine learning determinando el más adecuado.This thesis is framed in the field of remote motion monitoring, which aims to obtain information about the execution of movements. This information is essential in many applications, including the clinical ones, to measure the evolution of patients, to assess possible pathologies, such as motor or cognitive ones, and to follow up physical therapies. The monitoring of physical therapies has twofold purpose: to ensure the correct execution of movements and to improve adherence to the programs. Both purposes are essential to achieve the benefits associated with physical therapies. To accomplish this monitoring in a remote and non-intrusive way, technological resources such as the well-known inertial sensors are needed, which are commonly integrated into the so-called wearables. This work focuses on inertial-based solutions for monitoring physical therapy routines. However, the results of this work are not exclusive of this field, being able to be applied in other fields that require a motion monitoring. This work is intended to meet the needs of the monitoring systems found in the literature. In the review of previous proposals for remote monitoring of rehabilitation routines, we found two different main approaches. The first one is based on the analysis of movements, which estimates kinematic parameters, and the second one focuses on the qualitative characterization of the movements. From this differentiation, we identify and contribute to the limitations of each approach. With regard to the motion analysis for the estimation of kinematic parameters, we found an anatomical parameter required in various methods proposed in the literature. This parameter consists in the position of the joints with respect to the sensors, and sometimes these methods also require the length of the anatomical segments. The determination of these internal parameters is complex and is usually performed in controlled environments with optical systems or through palpation of anatomical landmarks by trained personnel. There is a lack of algorithms that determine these anatomical parameters using inertial sensors. This work introduces an algorithm for this anatomical calibration, which is based on the determination of the point of zero acceleration present in fixed joints. We use one inertial sensor per joint in order to simplify the complexity of algorithms versus using xv xvi ABSTRACT more than one. Since the relative position of this point may vary due to soft tissue movements or joint motion, the mean null acceleration point for the calibration motion is estimated by least squares. This algorithm is adapted to slow movements occurring in the lower-limbs to meet the required joint stabilization. Moreover, it can be applied to both joint centers and axes, although the latter is more complex to determine. Since we are dealing with the calibration of a system as complex as the human body, we evaluate different movements and their relation to the accuracy of the proposed system. This thesis also proposes a second, more versatile calibration method, which is adapted to the characteristic soft tissue associated with the human body. This method is based on the measurements of one inertial sensors used as inputs of an extended Kalman filter. We test the proposal both in synthetic data and in the real scenario of hip center of rotation determination. In simulations it provides an accuracy of 3% and in the real scenario, where the reference is obtained with a high precision optical system, the accuracy is 10 %. In this way, we propose a novel algorithm that localizes the joints adaptively to the motion of the tissues. In addition, this work addresses another limitation of motion analysis which is the lack of common datasets for the evaluation of algorithms and for the development of new proposals of motion monitoring methods. For this purpose, we design and create a public database focused on common movements in rehabilitation routines. Its design takes into account the joint calibration that is usually considered for the monitoring of joint parameters, performing functional movements for it. We monitor lower and upper limb exercises correctly and incorrectly performed by 30 volunteers of both sexes and a wide range of ages. One of the main objectives to be fulfilled by this database is the validation of algorithms based on inertial systems. Thus, it is recorded by using four inertial systems placed on different body limbs and including a highly accurate reference system based on infrared cameras. In addition, the recorded movements are labeled according to their characterization, which is based on the type of exercise performed and their quality. We provide a total of 7 076 files of inertial kinematic data with a high-precision reference, characterized with respect to the kind of performed motion and their correctness in performance, together with a function for automatic processing. Finally, we focus on the analysis of the second approach of monitoring physical routines, whose objective is to obtain qualitative information of their execution. This work contributes to the characterization of movements including their recognition and evaluation, which are usually studied separately. We propose three classification systems which use four inertial sensors. The proposals differ in the distribution of data and, therefore, the level of detail in the system outputs. We evaluate six machine learning techniques for the proposed classification systems in order to determine the most suitable for each of them: Support Vector Machines, Decision Trees, Random Forest, xvii K Nearest Neighbors, Extreme Learning Machines and Multi-Layer Perceptron. The proposals result in accuracy, F1-value, precision and sensitivity above the 88 %. Furthermore, we achieve a system with an accuracy of 95% in the complete qualitative characterization of the motions, which recognizes the performed motion and evaluates the correctness of its performance. It is worth highlighting that the highest metrics are always obtained with Support Vector Machines, among all the methods evaluated. The proposed classifier that provides the highest metrics is the one divided into two stages, that first recognizes the exercises and then evaluates them, compared with the other proposals that perform both tasks in one single-stage classification. From our work, it can be concluded that inertial systems are appropriate for remote physical exercise monitoring. On the one hand, they are suitable for the calibration of human joints necessary for various methods of motion analysis using one inertial sensor per joint. These sensors allow to obtain the estimation of an average joint location as well as the average length of anatomical segments. Also, joint centers can be located in scenarios where joint-related sensor movements occur, associated with soft tissue movement. On the other hand, a complete characterization of the physical exercises performed can be achieved with four inertial sensors and the appropriate algorithms. In this way, anatomical information can be obtained, as well as quantitative and qualitative information on the execution of physical therapies through the use of inertial sensors

Stereophotogrammetry in human movement analysis: novel methods for the quality assurance, biomechanical analysis and clinical interpretation of gait analysis

The study of movement has always fascinated artists, photographers and researchers. Across the years, several attempts to capture, freeze, study and reproduce motion were made. Nowadays, motion capture plays an important role within many fields, from graphical animation, filmmaking, virtual reality, till medicine. In fact, movement analysis allows to measure kinematic and kinetic performance of the human body. The quantitative data obtained from measurements may support the diagnosis and treatment of many pathologies, allowing to take clinical decisions and supporting the follow-up of treatments or rehabilitation. This approach is nowadays named evidence based medicine. In this work, motion capture techniques and advanced signal processing techniques were exploited in order to: (i) develop a protocol for the validation and quality assurance of the clinical strength measurements, (ii) develop an algorithm for clinical gait analysis data interpretation and identification of pathological patterns, and (iii) design user-friendly software tools to help clinicians using the novel data processing algorithms and reporting the results of measurements. This work was divided into three sections: Part 1 contains a survey about the history of motion analysis and a review of the earliest experiments in biomechanics. The review covered the first historical attempts, that were mainly based on photography, till the state-of-the-art technology used today, i.e. the optoelectronic system. The working principle of optoelectronic system was reviewed as well as its applications and modern setups in the clinical practice. Some modern functional evaluation protocols, aimed to the quantitative evaluation of physical performance and clinical diagnosis of motor disorders, were also reviewed. Special attention was paid to the most common motion analysis exam that is nowadays worldwide standardized, i.e. the Gait Analysis. Examples of Gait Analysis studies on subjects with pathology and follow-up were reviewed. Part 2 concerns the design of an experimental setup, involving motion analysis, for the quality assurance of clinical strength measurements. Measurements of force are popular in the clinical practice as they allow to evaluate the muscle weakness, health status of patients and the effects of therapies. A variety of protocols was proposed to conduct such measurements, implying the acquisition of forces, angles and angular velocities when the maximum voluntary force is exerted. Hand held dynamometry (HHD), based on single component load cell, was extensively used in clinical practice; however, several shortcomings were identified. The most relevant were related to the operator’s ability. This work was aimed to investigate the inherent inaccuracy sources in knee strength measurements when are conducted by a single component load cell. The analysis was conducted by gathering the outputs of a compact six-component load cell, comparable in dimension and mass to clinical HHDs, and an optoelectronic system. Quality of measurements was investigated in terms of quantifying, by an ad-hoc metrics, the effects induced in the overall inaccuracy by: (i) the operator’s ability to place and to hold still the HHD and (ii) ignoring the transversal components of the force exchanged between the patient and the experimenter. The main finding was that the use of a single component HHD induced an overall inaccuracy of 5% in the strength measurements, when operated by a trained clinician and angular misplacements are kept within the values found in this work (≤15°) and with a knee ROM ≤ 22°. Even if the measurement outputs were reliable and accurate enough for both knee flexion and extension, extension trials were the most critical due to the higher force exerted, i.e. 249.4±27.3 N vs. 146.4±23.9 N of knee flexion. The most relevant source of inaccuracy was identified in the angular displacement of HHD on the horizontal plane. A dedicated software, with graphical user interface, was designed and implemented. The purposes of this software were to: (i) speed up data processing, (ii) allow user to select the proper processing workflow, and (iii) provide clinicians with a tool for quick data processing and reporting. Part 3 concerns the research study about gait analysis on subjects with pathology. Gait analysis is often used for the assessment of the gait abilities in children with cerebral palsy and to quantify improvements/variations after a treatment. To simplify GA interpretation and to quantify deviation from normality, some synthetic descriptors were developed in literature, such as the Movement Analysis Profile (MAP) and the Linear Fit Method (LFM). The aims of this work were: (i) to use synthetic descriptors in order to quantify gait variations in subjects with Cerebral Palsy that underwent surgery involving bone repositioning and muscle/tendon lengthening at the level of the femur and hamstring group (SEMLS); (ii) test the effectiveness of a recently proposed index, i.e. the LFM, on such patients; (iii) design and implement a novel index that may overcome the limitations of the previous methods. Gait Analysis exams of 10 children with Cerebral Palsy, pre and post treatment, were collected. Data were analysed by means of MAP and LFM indices. To overcome the limitations observed for the methods, another index was designed as a modified version of the MAP, namely the OC-MAP. It took into account the effect on deviation due to offset and allowed to compute the deviation from normality on tracks purified by the offset. An overall improvement of the gait pattern was observed for most of the subjects after surgery. The highest effect was observed for the knee flexion/extension angle. Patients who had initial high deviations also had the largest improvements. Worsening in the kinematics of the pelvis could be explained as a consequence of SEML involving a lengthening of hamstring group. Pre-post differences were higher than the Minimally Clinical Important Difference for all parameters, except hip flexion. An improvement towards normality was observed for all the parameters, with exception of pelvic tilt for which a worsening was observed. LFM provided results similar to OC-MAP offset analysis but could not be considered reliable due to intrinsic limitations. As offset in gait features played an important role in gait deviation, OC-MAP synthetic analysis is recommended to study gait pattern of subjects with Cerebral Palsy. A dedicated software, with graphical user interface, was designed and implemented. The purpose of this software was to compute the synthetic descriptors on a large amount of data, to speedup data processing and to provide clinicians with a quick access to the result

Explaining the Ergonomic Assessment of Human Movement in Industrial Contexts

Manufacturing processes are based on human labour and the symbiosis between human operators and machines. The operators are required to follow predefined sequences of movements. The operations carried out at assembly lines are repetitive, being identified as a risk factor for the onset of musculoskeletal disorders. Ergonomics plays a big role in preventing occupational diseases. Ergonomic risk scores measure the overall risk exposure of operators however these methods still present challenges: the scores are often associated to a given workstation, being agnostic to the variability among operators. Observation methods are most often employed yet require a significant amount of effort, preventing an accurate and continuous ergonomic evaluation to the entire population of operators. Finally, the risk’s results are rendered as index scores, hindering a more comprehensive interpretation by occupational physicians. This dissertation developed a solution for automatic operator risk exposure in assembly lines. Three main contributions were presented: (1) an upper limb and torso motion tracking algorithm which relies on inertial sensors to estimate the orientation of anatomical joints; (2) an adjusted ergonomic risk score; (3) an ergonomic risk explanation approach based on the analysis of the angular risk factors. Throughout the research, two experimental assessments were conducted: laboratory validation and field evaluation. The laboratory tests enabled the creation of a movements’ dataset and used an optical motion capture system as reference. The field evaluation dataset was acquired on an automotive assembly line and serve as the basis for an ergonomic risk evaluation study. The experimental results revealed that the proposed solution has the potential to be applied in a real environment. Through direct measures, the ergonomic feedback is fastened, and consequently, the evaluation can be extended to more operators, ultimately preventing, in long-term, work-related injuries

ZATLAB : recognizing gestures for artistic performance interaction

Most artistic performances rely on human gestures, ultimately resulting in an elaborate interaction between the performer and the audience. Humans, even without any kind of formal analysis background in music, dance or gesture are typically able to extract, almost unconsciously, a great amount of relevant information from a gesture. In fact, a gesture contains so much information, why not use it to further enhance a performance? Gestures and expressive communication are intrinsically connected, and being intimately attached to our own daily existence, both have a central position in our (nowadays) technological society. However, the use of technology to understand gestures is still somehow vaguely explored, it has moved beyond its first steps but the way towards systems fully capable of analyzing gestures is still long and difficult (Volpe, 2005). Probably because, if on one hand, the recognition of gestures is somehow a trivial task for humans, on the other hand, the endeavor of translating gestures to the virtual world, with a digital encoding is a difficult and illdefined task. It is necessary to somehow bridge this gap, stimulating a constructive interaction between gestures and technology, culture and science, performance and communication. Opening thus, new and unexplored frontiers in the design of a novel generation of multimodal interactive systems. This work proposes an interactive, real time, gesture recognition framework called the Zatlab System (ZtS). This framework is flexible and extensible. Thus, it is in permanent evolution, keeping up with the different technologies and algorithms that emerge at a fast pace nowadays. The basis of the proposed approach is to partition a temporal stream of captured movement into perceptually motivated descriptive features and transmit them for further processing in Machine Learning algorithms. The framework described will take the view that perception primarily depends on the previous knowledge or learning. Just like humans do, the framework will have to learn gestures and their main features so that later it can identify them. It is however planned to be flexible enough to allow learning gestures on the fly. This dissertation also presents a qualitative and quantitative experimental validation of the framework. The qualitative analysis provides the results concerning the users acceptability of the framework. The quantitative validation provides the results about the gesture recognizing algorithms. The use of Machine Learning algorithms in these tasks allows the achievement of final results that compare or outperform typical and state-of-the-art systems. In addition, there are also presented two artistic implementations of the framework, thus assessing its usability amongst the artistic performance domain. Although a specific implementation of the proposed framework is presented in this dissertation and made available as open source software, the proposed approach is flexible enough to be used in other case scenarios, paving the way to applications that can benefit not only the performative arts domain, but also, probably in the near future, helping other types of communication, such as the gestural sign language for the hearing impaired.Grande parte das apresentações artísticas são baseadas em gestos humanos, ultimamente resultando numa intricada interação entre o performer e o público. Os seres humanos, mesmo sem qualquer tipo de formação em música, dança ou gesto são capazes de extrair, quase inconscientemente, uma grande quantidade de informações relevantes a partir de um gesto. Na verdade, um gesto contém imensa informação, porque não usá-la para enriquecer ainda mais uma performance? Os gestos e a comunicação expressiva estão intrinsecamente ligados e estando ambos intimamente ligados à nossa própria existência quotidiana, têm uma posicão central nesta sociedade tecnológica actual. No entanto, o uso da tecnologia para entender o gesto está ainda, de alguma forma, vagamente explorado. Existem já alguns desenvolvimentos, mas o objetivo de sistemas totalmente capazes de analisar os gestos ainda está longe (Volpe, 2005). Provavelmente porque, se por um lado, o reconhecimento de gestos é de certo modo uma tarefa trivial para os seres humanos, por outro lado, o esforço de traduzir os gestos para o mundo virtual, com uma codificação digital é uma tarefa difícil e ainda mal definida. É necessário preencher esta lacuna de alguma forma, estimulando uma interação construtiva entre gestos e tecnologia, cultura e ciência, desempenho e comunicação. Abrindo assim, novas e inexploradas fronteiras na concepção de uma nova geração de sistemas interativos multimodais . Este trabalho propõe uma framework interativa de reconhecimento de gestos, em tempo real, chamada Sistema Zatlab (ZtS). Esta framework é flexível e extensível. Assim, está em permanente evolução, mantendo-se a par das diferentes tecnologias e algoritmos que surgem num ritmo acelerado hoje em dia. A abordagem proposta baseia-se em dividir a sequência temporal do movimento humano nas suas características descritivas e transmiti-las para posterior processamento, em algoritmos de Machine Learning. A framework descrita baseia-se no facto de que a percepção depende, principalmente, do conhecimento ou aprendizagem prévia. Assim, tal como os humanos, a framework terá que aprender os gestos e as suas principais características para que depois possa identificá-los. No entanto, esta está prevista para ser flexível o suficiente de forma a permitir a aprendizagem de gestos de forma dinâmica. Esta dissertação apresenta também uma validação experimental qualitativa e quantitativa da framework. A análise qualitativa fornece os resultados referentes à aceitabilidade da framework. A validação quantitativa fornece os resultados sobre os algoritmos de reconhecimento de gestos. O uso de algoritmos de Machine Learning no reconhecimento de gestos, permite a obtençãoc¸ ˜ao de resultados finais que s˜ao comparaveis ou superam outras implementac¸ ˜oes do mesmo g´enero. Al ´em disso, s˜ao tamb´em apresentadas duas implementac¸ ˜oes art´ısticas da framework, avaliando assim a sua usabilidade no dom´ınio da performance art´ıstica. Apesar duma implementac¸ ˜ao espec´ıfica da framework ser apresentada nesta dissertac¸ ˜ao e disponibilizada como software open-source, a abordagem proposta ´e suficientemente flex´ıvel para que esta seja usada noutros cen´ arios. Abrindo assim, o caminho para aplicac¸ ˜oes que poder˜ao beneficiar n˜ao s´o o dom´ınio das artes performativas, mas tamb´em, provavelmente num futuro pr ´oximo, outros tipos de comunicac¸ ˜ao, como por exemplo, a linguagem gestual usada em casos de deficiˆencia auditiva

Applied Biomechanics: Sport Performance and Injury Prevention

This Special Issue had, as its main objective, the compilation of biomechanical studies on sports performance and its relationship with musculoskeletal injuries. It is a collection of research on eight different sports (soccer, volleyball, swimming, cycling, skiing, golf, athletics, and hockey) considering injuries in general and specific injuries such as hamstring muscle injury, anterior cruciate ligament of the knee, and pain of the pubic symphysis. Additionally, it is noteworthy that most of the studies considered both men and women. Classical biomechanical tools have been used, such as 2D and 3D motion analysis, force platforms, and electromyography