RESUMEN
Current clinical practice of measuring hand joint range of motion relies on a goniometer as it is inexpensive, portable, and easy to use, but it can only measure the static angle of a single joint at a time. To measure dynamic hand motion, a camera-based system that can perform markerless hand pose estimation is attractive, as the system is ubiquitous, low-cost, and non-contact. However, camera-based systems require line-of-sight, and tracking accuracy degrades when the joint is occluded from the camera view. Thus, we propose a multi-view setup using a readily available color camera from a single mobile phone, and plane mirrors to create multiple views of the hand. This setup eliminates the complexity of synchronizing multiple cameras and reduce the issue of occlusion. Experimental results show that the multi-view setup could help to reduce the error in measuring the flexion angle of finger joints. Dynamic hand pose estimation with object interaction is also demonstrated.
Asunto(s)
Articulaciones de los Dedos , Mano , Movimiento (Física) , Rango del Movimiento ArticularRESUMEN
Semantic segmentation is an important step for hand and object tracking as subsequent tracking algorithms depend heavily on the accuracy of the segmented hand and object. However, current methods for hand and object segmentation are limited in the number of semantic labels, and lack of a large scale annotated dataset to train an end-to-end deep neural network for semantic segmentation. Thus, in this work, we present a framework for generating a publicly available synthetic dataset, that is targeted for upper limb rehabilitation involving hand-object interaction and uses it to train our proposed deep neural network. Experimental results show that even though the network is trained on synthetic depth images, it is able to achieve a mean intersection over union (mIoU) of 70.4% when tested on real depth images. Furthermore, the inference time of the proposed network takes around 6 ms on a GPU, thus making it suitable for real-time applications.
Asunto(s)
Mano , Procesamiento de Imagen Asistido por Computador , Redes Neurales de la Computación , Algoritmos , Fenómenos Biomecánicos , Humanos , Movimiento , SemánticaRESUMEN
Synchronous forelimb-hindlimb gait pattern is important to facilitate natural walking behavior of an injured rat with total transection. Since our ultimate research goal is to build a rehabilitation robotic system to simulate the natural walking pattern for spinalized rats, this research aims to address an immediate goal of automating the inference of the rat's hindlimb trajectory from its own forelimb movement. Our proposed method uses unsupervised learning to extract independent forelimb and hinblimb phases. From the phase information, a relationship between forelimb and hindlimb trajectory can then be calculated. Results show that the proposed method has the potential to be used in a rehabilitation robotic system.
Asunto(s)
Miembro Anterior , Marcha , Robótica , Animales , Automatización , Miembro Posterior , Locomoción , Ratas , Extremidad Superior , CaminataRESUMEN
The motor control of human locomotion is still an open issue, and it may be the leading cause of the low effectiveness of lower limbs rehabilitation therapies. Locomotion motor control has proved to be fundamentally different from the upper limbs reaching task strategies, which have been used for the development of current motor control computational models used to define rehabilitation protocols. The main difference between these two tasks is the relevance of the environmental dynamics in task planning and execution. Reaching movements are dominated by the intrinsic impedance of the human body. On the other hand, locomotion is determined by the interaction between the human body and Earth's gravity. The dynamic primitives have been recently proposed to explain how humans account for the environmental dynamics during motor control; however, it is not yet possible to explain how the nervous system combines the information. This paper proposes and validates with human data that the brain controls locomotion to have the centre of mass moving between the two legs as a harmonic oscillator. This finding has enabled us to propose a control architecture that can explain how the motor primitives can be described as a special type of dynamics primitives.
Asunto(s)
Encéfalo , Actividad Motora , Caminata , Encéfalo/fisiología , Ambiente , Gravitación , Humanos , Extremidad Inferior , Actividad Motora/fisiología , Caminata/fisiologíaRESUMEN
Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods.
Asunto(s)
Movimiento , Radioterapia Asistida por Computador , Respiración , Algoritmos , Planificación de la Radioterapia Asistida por Computador , Máquina de Vectores de Soporte , Factores de TiempoRESUMEN
A novel device, which looks like a mug, has been proposed for measuring the impedance of human hand. The device is designed to have convenient size and light weight similar to an ordinary coffee mug. It contains a 2-axis inertia sensor to monitor vibration and a small motor to carry an eccentric mass (m=100 gr, r=2 cm, rpm=600). The centrifugal force due to the rotating mass applies a dynamic force to the hand that holds the mug. Correlation of the acceleration signals with the perturbing force gives the geometrical mechanical impedance. Experimental results on a healthy subject shows that impedance is posture dependant while it changes with the direction of the applied perturbing force. For nine postures the geometrical impedance is obtained all of which have elliptical shapes. The method can be used for assessment of spasticity and monitoring stability in patients with stroke or similar problems.