The Design and Implementation of a Kinect-Based Rehabilitation Exercise Monitoring and Guidance System

In preventive and rehabilitative healthcare, physical exercise is a powerful intervention. However, a program may require in the range of thousands of practice repetitions, and many people do not adhere to the program or perform their home exercises incorrectly, making the exercise ineffective, or even dangerous. This thesis research aims to develop a Kinect-based system for rehabilitation exercises monitoring and guidance. In the first step, a feasibility study was carried out on using Kinect for realtime monitoring of rehabilitation exercises while a multi-camera motion tracking system was used to establish the ground truth. In the second step, a Unity-based system was developed to provide realtime monitoring and guidance to patients. The Unity framework was chosen because it enables us to use virtual reality techniques to demonstrate detailed movements to the patient, and to facilitate examination of the quality and quantity of the patient sessions by the clinician. The avatar-based rendering of motion also preserves the privacy of the patients, which is essential for healthcare system

The Design and Implementation of a Kinect-Based Rehabilitation Exercise Monitoring and Guidance System

In preventive and rehabilitative healthcare, physical exercise is a powerful intervention. However, a program may require in the range of thousands of practice repetitions, and many people do not adhere to the program or perform their home exercises incorrectly, making the exercise ineffective, or even dangerous. This thesis research aims to develop a Kinect-based system for rehabilitation exercises monitoring and guidance. In the first step, a feasibility study was carried out on using Kinect for realtime monitoring of rehabilitation exercises while a multi-camera motion tracking system was used to establish the ground truth. In the second step, a Unity-based system was developed to provide realtime monitoring and guidance to patients. The Unity framework was chosen because it enables us to use virtual reality techniques to demonstrate detailed movements to the patient, and to facilitate examination of the quality and quantity of the patient sessions by the clinician. The avatar-based rendering of motion also preserves the privacy of the patients, which is essential for healthcare system

Determinación de rangos de normalidad en movimientos de miembro inferior utilizando el Kinect 2 para ejercicios de rehabilitación de reconstrucción de ligamento cruzado anterior

Actualmente son utilizadas múltiples tecnologías y procedimientos relacionados con los desarrollos realizados en áreas de ingeniería electrónica, mecánica, sistemas y bioingeniería como parte integral y de apoyo al área de fisioterapia a los procesos de recuperación de personas con lesiones en sus miembros inferior, superior o limitaciones de movilidad ya sea por lesiones causadas debido a deportes, malas posturas o problemas de salud relacionados con articulaciones y músculos, que limitan la función motora normal del ser humano. Algunos sistemas desarrollos como técnicas de apoyo son por ejemplo Biofeedback electromiógráfico [6], para identificar patrones de movimientos musculares [2] y establecer los progresos en el proceso de recuperación de lesiones, enfocado principalmente en la respuesta eléctrica que presentan los músculos al ejecutar ciertas actividades, los cuales sirven de patrón de seguimiento para los especialistas al momento de evaluar la recuperación del paciente. Además de tener actualmente desarrollos de aplicativos de interfaz gráfica de apoyo, que buscan guiar al paciente en ciertas actividades que debe realizar para su proceso de recuperación de una forma amigable, ya que se aplican métodos lúdicos de plantear los ejercicios como una especie de juego en el que el paciente deberá cumplir las actividades correctamente para llegar a la “meta”. En el caso específico del proceso de rehabilitación post-quirúrgico de la reconstrucción de ligamento cruzado anterior (LCA) [9] , la cual es una lesión común en deportistas, se lleva a cabo un proceso de recuperación que va desde la etapa de entrenamiento estático, hasta las fases de entrenamiento y adaptación dinámica con cargas progresivas [10], todo esto con el fin de que el paciente pueda regresar a sus actividades cotidianas y deportivas con una plena recuperación de su lesión de ligamento. En cada fase de recuperación post-quirúrgico de LCA se realizan ejercicios de adaptación de movimiento para la rodilla a distintos ángulos específicos en cada fase de rehabilitación [11] y así estimular a que los ligamentos se adapten progresivamente a las flexiones y extensiones de la rodilla, ya que con el proceso de reconstrucción se genera cicatrización que tiende a limitar la capacidad de flexión y extensión del tejido, [12]

Analysis of derived features for the motion classification of a passive lower limb exoskeleton

Analysis of Derived Features for the Motion Classification of a PassiveLowerLimbExoskeleton The recognition of human motion intentions is a fundamental requirement to control efficiently an exoskeleton system. The exoskeleton control can be enhanced or subsequent motions can be predicted, if the current intended motion is known. At H2T research has been carried out with a classification system based on Hidden Markov Models (HMMs) to classify the multi-modal sensor data acquired from a unilateral passive lower-limb exoskeleton. The training data is formed of force vectors, linear accelerations and Euler angles provided by 7 3D-force sensors and 3 IMUs. The recordings consist of data of 10 subjects performing 14 different types of daily activities, each one carried out 10 times. This master thesis attempts to improve the motion classification by using physical meaningful derived features from the raw data aforementioned. The knee vector moment and the knee and ankle joint angles, which respectively give a kinematic and dynamic description of a motion, were the derived features considered. Firstly, these new features are analysed to study their patterns and the resemblance of the data among different subjects is quantified in order to check their consistency. Afterwards, the derived features are evaluated in the motion classification system to check their performance. Various configurations of the classifier were tested including different preprocessors of the data employed and the structure of the HMMs used to represent each motion. Some setups combining derived features and raw data led to good results (e.g. norm of the moment vector and IMUs got 89.39% of accuracy), but did not improve the best results of previous works (e.g. 2 IMUs and 1 Force Sensor got 90.73% of accuracy). Although the classification results are not improved, it is proved that these derived features are a good representation of their primary features and a suitable option if a dimensional reduction of the data is pursued. At the end, possible directions of improvement are suggested to improve the motion classification concerning the results obtained along the thesis.Outgoin