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Error feedback is critical for supporting motor adaptation in rehabilitation, sports, piloting, and skilled manual tasks. Error augmentation interventions, in which participants' errors are amplified with either visual or haptic feedback during training has shown success over repetitive practice. Here we show that the statistical tendencies arising from free movement exploration can improve error...
We consider a problem of identifying people based on their styles in performing actions from an arbitrary predefined set of action types. We present a generative model describing the action instance creation process and derive a probabilistic identity inference scheme, which implicitly includes action type inference as one of its components. Our experiments validate the power of the approach. We report...
Human pose estimation from depth data has made significant progress in recent years and commercial sensors estimate human poses in real-time. However, state-of-the-art methods fail in many situations when the humans are partially occluded by objects. In this work, we introduce a semantic occlusion model that is incorporated into a regression forest approach for human pose estimation from depth data...
In this paper we present an approach to hand pose estimation that combines both discriminative and modelbased methods to overcome the limitations of each technique in isolation. A Randomised Decision Forests (RDF) is used to provide an initial estimate of the regions of the hand. This initial segmentation provides constraints to which a 3D model is fitted using Rigid Body Dynamics. Model fitting is...
Face recognition technology is widely used in law enforcement agencies. Face photo-sketch recognition is one of possible ways to identify suspects. We propose a method using joint dictionary learning for face photo-sketch recognition. Our method bypasses the image synthesis procedure used by previous joint dictionary learning based methods. Compared with other methods such as coupled dictionary learning...
This work investigates a gesture segmentation and recognition scheme that employs a random forest classification model. Our method trains a random forest model to recognize gestures from a given vocabulary, as presented in a training dataset of video plus 3D body joint locations, as well as out-of-vocabulary (non-gesture) instances. Given an input video stream, our trained model is applied to candidate...
Falls and fall-related fractures are common in the elderly and lead to negative health outcomes. Muscle function and balance impairment are important risk factors for falls. Traditional exercise improves muscle mass and function as well as balance. However, older adults often do not exercise regularly because of physical and cognitive limitations or lack of adherence. Whole body vibration promises...
This paper proposes a new approach to model arm pose configuration from color images based on the learned features and arm part structure constraints. It aims to model human arm pose without assuming of a particular clothing style, action category and background. It uses an energy model that describes the dependence relationships among arm joints and parts. A joint convolutional neural network (J-CNN)...
This paper presents a method for shared control where real-time bursts of optimal control assistance are applied by an observer on-demand to aid a simulated figure in maintaining balance. The proposed Assistive Controller (AC) calculates the optimal burst control fast, in real time, while accounting for nonlinearities of the dynamic model. The short duration of the AC signals allows a rapid transfer...
Injury to the Anterior Cruciate Ligament (ACL) can lead to inadequate movement during sport and daily life activities, leading to increased risk of reinjury or dropouts from any form of physical activity. Thus, it is important to detect such movement problems so that they can be prevented through focused rehabilitation programmes. This paper proposes a method to seek out differences of movement patterns...
This paper presents a method to classify chronic Low Back Pain subject by analyzing the muscular fatigue. A new signal characteristic is introduced: the spatial distribution of the Median Frequency slope. Because of the high number of sensors relative to the number of subjects tested, the classification method used is the Naive Bayesian classifier. The low back muscular fatigue is measured by the...
Robotic devices have been shown to be efficacious in the delivery of therapy to treat upper limb motor impairment following stroke. However, the application of this technology to other types of neurological injury has been limited to case studies. In this paper, we present a multi degree of freedom robotic exoskeleton, the MAHI Exo II, intended for rehabilitation of the upper limb following incomplete...
Detecting and interpreting contacts is a crucial aspect of physical Human-Robot Interaction. In order to discriminate between intended and unintended contact types, we derive a set of linear and non-linear features based on physical contact model insights and from observing real impact data that may even rely on proprioceptive sensation only. We implement a classification system with a standard non-linear...
Inspired by achievements in rehabilitation, motor learning, and neuroscience, therapeutic robots are aiming to provoke neuromotor plasticity and improve recovery after stroke and mobility impairments. Human sensorimotor system is specialized with position and velocity sensory fibers and exhibits variant muscle impedance in accordance with the ongoing task. The virtually interfaced robotic ankle and...
Robotic rehabilitation devices are attractive to physical therapists. Various leg exoskeletons have been developed during the past decade and have been used in gait training. Traditional exoskeletons usually have a complex structure and add extra inertia to the wearer's leg, which may change their natural gait. In this paper, we present the design of a cable-driven active leg exoskeleton (C-ALEX)...
The One Class Auto Associative Neural Network (AANN) has been investigated for solving various problems. Nonetheless, it is sensitive to the presence of outliers in the training set, which is known problem for one-class classifiers. For this, attempts have been done via proposing the use of efficient kernel and ensemble method to reduce the effect of outliers for one class support vector machine classifier...
Robot calibration is to improve the accuracy of the robot model so as to achieve better positioning accuracy within the robot work cell. Model based calibration approaches are in general limited to compensating for geometric errors and are unable to compensate for error sources that do not fit within the proposed robot model. In order to compensate for the unmodeled error sources, a Radial Basis Function...
In real-world video surveillance applications, one often needs to recognize face images from a very long distance. Such recognition tasks are very challenging, since such images are typically with very low resolution (VLR). However, if one simply downsamples high-resolution (HR) training images for recognizing the VLR test inputs, or if one directly upsamples the VLR inputs for matching the HR training...
A learning system is presented which uses feedforward control to improve the accuracy of standard position controlled robots. The method is executed on joint level since in this case there are less couplings than in the cartesian space. On the other side the main goal is to reduce the maximal deviation from a given cartesian path. This requires extended algorithms which are derived and examined using...
This paper presents a general methodology of controller design by the hybrid neuro-inverse control with the knowledge-based nonlinear separation for industrial nonlinear systems. In industrial nonlinear systems, various kinds of uncertainties may cause serious deterioration of system performances. Unfortunately, these uncertainties are usually difficult to identify and compensate from the entire system...
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