THE POSSIBILITY OF CLASSIFYING V1 AND V2 SUB-TECHNIQUES OF A SINGLE IMU SENSOR THROUGH COMPARISON OF MOTION-SPECIFIC DATA(PITCH, YAW AND ROLL ANGLE VALUES-ORIENTATION ANGLE VALUE) IN XC SKI

The purpose of this study is to confirm whether the single IMU sensor module(LGE developing and providing for the experiments) that attached to the pelvis can distinguish the motion of the sub-techniques (V1, V2, V2A) with the accuracy of commercial XSENS(equipment consisting of 17 sensors) in freestyle(skate) xc skiing. Therefore, one elite male xc skier with eleven years experience was investigated by measuring the three-directional rotation angle for each of the three sub-techniques used in XC ski freestyle. Through this method, we could found not only the difference of motion patterns of each sub-techniques but also the possibility for replacement of multiple sensor system by a single IMU sensor module from LGE. Thus, it is expected that a single LGE IMU sensor module could be applied to repetitive and periodic sports such as XC ski

SMART EQUIPMENT DESIGN CHALLENGES FOR REAL TIME FEEDBACK SUPPORT IN SPORT

Smart equipment can support feedback in motor learning process. Smart equipment with integrated sensors can be used as a standalone system or complemented with body-attached wearable sensors. Our work focuses on real-time biofeedback system design, particularly on the application of a specific sensor selection. The main goal of our research is to prepare the technical conditions to prove efficiency and benefits of the real-time biofeedback when used in selected motion-learning processes. The most used wireless technologies that are used or are expected to be used in real-time biofeedback systems are listed. The tests performed on two prototypes, smart golf club and smart ski, show an appropriate sensor selection and feasibility of implementation of the real-time biofeedback concept in golf and skiing practice. We are confident that the concept can be expanded for use in other sports and rehabilitation. It has been learned that at this time none of the existing wireless technologies can satisfy all possible demands of different real-time biofeedback applications in sport

Análisis de la presión ejercida durante la acción de giro de esquí alpino a través de simulador

El Esquí Alpino es un deporte popular en todo el mundo y practicado por un amplio número de personas. El análisis biomecánico para el rendimiento en esta modalidad es de real importancia para la mejora y a su vez, complejo y desafiante desde el punto de vista físico, técnico y táctico. Especialmente, la presión ejercida durante los giros es de gran importancia, siendo esta la unidad básica de movimiento dentro del esquí alpino. La realización del estudio se llevó a cabo con los deportistas pertenecientes del Centro Especializado en Tecnificación de Deportes de Invierno (CETDI). Un total de 9 sujetos (n=5 chicas y n=4 chicos) participaron en este trabajo. Para el registro de las diferentes presiones ejercidas en los apoyos laterales se emplearon las plantillas instrumentales (Pedar® pad) y el software informático Novel – Pedar®. Se utilizó un ergómetro específico para reproducir la acción técnica de giro y determinar con precisión el punto de inicio de la acción de giro, las partes que lo componen y establecer los parámetros de fuerza aplicados durante la acción. La fuerza aplicada se obtuvo a partir del propio sistema de plantillas instrumentadas y se compararon los parámetros físicos de fuerza ejercidos a partir de un ejercicio básico de entrenamiento para la determinación de la máxima producción de potencia. De manera general, son en los apoyos a izquierda donde se dan los valores más altos, así como en la parte del antepié, predominando de manera general las chicas frente a los chicos. Se encuentran fuertes correlaciones en los apoyos de derecha (r = 0.8; siendo p <br /

Quantitative Gangparameter aus sensorbasierten Bewegungsanalysen als Progressionsmarker beim idiopathischen Parkinson-Syndrom

Ziel dieser Arbeit ist es, quantitative Gangparameter, die mittels am Körper getragener Bewegungssensoren bei Patienten mit idiopathischem Parkinsonsyndrom (IPS) unter Supervision objektiv erhoben wurden, im Hinblick auf ihren Zusammenhang mit Krankheitsdauer und –schwere zu untersuchen. Diese Arbeit liefert Hinweise darauf, dass quantitative Gangparameter aus tragbaren Bewegungssensoren in Zukunft bei der objektiven Beurteilung therapeutischer Interventionen oder in der Diagnostik hilfreiche Informationen liefern könnten

Potential of IMU Sensors in Performance Analysis of Professional Alpine Skiers

In this paper, we present an analysis to identify a sensor location for an inertial measurement unit (IMU) on the body of a skier and propose the best location to capture turn motions for training. We also validate the manner in which the data from the IMU sensor on the proposed location can characterize ski turns and performance with a series of statistical analyses, including a comparison with data collected from foot pressure sensors. The goal of the study is to logically identify the ideal location on the skier’s body to attach the IMU sensor and the best use of the data collected for the skier. The statistical analyses and the hierarchical clustering method indicate that the pelvis is the best location for attachment of an IMU, and numerical validation shows that the data collected from this location can effectively estimate the performance and characteristics of the skier. Moreover, placement of the sensor at this location does not distract the skier’s motion, and the sensor can be easily attached and detached. The findings of this study can be used for the development of a wearable device for the routine training of professional skiers

Potential of IMU Sensors in Performance Analysis of Professional Alpine Skiers

In this paper, we present an analysis to identify a sensor location for an inertial measurement unit (IMU) on the body of a skier and propose the best location to capture turn motions for training. We also validate the manner in which the data from the IMU sensor on the proposed location can characterize ski turns and performance with a series of statistical analyses, including a comparison with data collected from foot pressure sensors. The goal of the study is to logically identify the ideal location on the skier’s body to attach the IMU sensor and the best use of the data collected for the skier. The statistical analyses and the hierarchical clustering method indicate that the pelvis is the best location for attachment of an IMU, and numerical validation shows that the data collected from this location can effectively estimate the performance and characteristics of the skier. Moreover, placement of the sensor at this location does not distract the skier’s motion, and the sensor can be easily attached and detached. The findings of this study can be used for the development of a wearable device for the routine training of professional skiers

Human skill capturing and modelling using wearable devices

Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution