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Computer-aided analyses of motion capture data require an effective and efficient concept of motion similarity. Traditional methods generally compare motion sequences by applying time-warping techniques to high-dimensional trajectories of joints. An increasing effectiveness of machine-learning techniques, such as deep convolutional neural networks, brings new possibilities for similarity comparison...
The paper presents an approach to localize human body joints in 3D coordinates based on a single low resolution depth image. First a framework to generate a database of 80k realistic depth images from a 3D body model is described. Then data preprocessing and normalization procedure, and DNN and MLP artificial neural networks architectures and training are presented. The robustness against camera distance...
This paper details the design process and features of a novel upper limb rehabilitation exoskeleton named CLEVER (Compact, Low-weight, Ergonomic, Virtual/Augmented Reality Enhanced Rehabilitation) ARM. The research effort is focused on designing a lightweight and ergonomic upper-limb rehabilitation exoskeleton capable of producing diverse and perceptually rich training scenarios. To this end, the...
Engaging in physical exercises such as weight training and physiotherapy stretching exercises at home requires proper execution and awareness of the exercises. One of the main problems in engaging exercises at home is that there is no proper guidance and feedback provided to align the exercises to the correct movements due to the absence of a physical trainer. This paper discusses how a system, namely...
A Human Action recognition system is proposed using the Skeletal Tracking from Kinect. The angular information of the joints helps in handling scaling errors. Vectors are generated using the joint coordinates and the angles of each joint are used as features for key pose recognition. A rotational compensation is included in the feature to handle rotational errors. The key poses are recognized using...
Recently, the synthesis of 3D dynamic expressions has become an important concern in computer graphics, facial recognition, etc. In this study, we propose a regression based joint subspace learning method for the automatic synthesis of 3D dynamic expression images. This method synthesizes 3D dynamic expression images from a single 2D facial image. We use two subspaces (the view subspace and the frame...
In the rehabilitation training and assessment of upper limbs, the conventional kinematic model treats the arm as a serial manipulator and maps the rotations in the joint space to movements in the Cartesian space. While this model brings simplicity and convenience, and thus has been overwhelming used, its accuracy is limited, especially for the distal parts of the upper limb that execute dexterous...
The rehabilitation exoskeleton robot is more and more used in the assisting stroke patients in implementing rehabilitation training. In this paper, a novel exoskeleton hand robot which was driven by cable has been proposed to aim at helping varieties of paralyses patients recover motor function. This exoskeleton hand rehabilitation robot system mainly consists of exoskeleton hand robot, EEG system,...
After-stroke rehabilitation of patients with shoulder disabilities traditionally requires repeated and progressive training exercises. Complete recovery is usually costly and lengthy. Aimed at reducing the cost and time of shoulder rehabilitation, powered exoskeletons are developed to provide automatic and steady rehabilitation training. Our previously developed shoulder exoskeleton is small-size,...
Hyperspectral imaging entails data typically spanning hundreds of contiguous wavebands in a certain spectral range. Each spatial point in hyperspectral images is therefore represented by a vector whose entries correspond to the intensity on each spectral band. Hyperspectral imaging enables object and feature detection, classification, or identification based on their spectral characteristics. Novel...
Human action recognition plays a vital role in the field of human-robot interaction and is widely researched for its potential applications. In this paper we propose a human action recognition framework for human robot interaction in industrial applications. First, a set of key descriptors are learned from a collection of weak spatio-temporal skeletal joint descriptors using random forests, which...
Temporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network...
Whole body vibration (WBV) techniques are increasingly applied in rehabilitation processes to improve patients' functional mobility. Therapeutic usage of WBV reported enhanced muscular strength, power or even bone density. Therefore, the purpose of our study was to investigate immediate response of single WBV training unit on mobility of elderly patients with neurological disorders. Nineteen patients...
Dynamic time warping (DTW) measures similarity between two data sequences by minimizing an accumulated distance between two sequence samples at each iteration and a cost is computed to assess the level of the similarity. The DTW cost may then be used to assign a sequence to a class if the problem is a classification problem. In machine learning, classification problems are solved using features with...
In practice, multiple objects in images are located by consecutively applying one detector for each class and taking the best confident score. In this work, we propose to show the advantage of grouping similar object classes into a hierarchical structure. While this approach has found interest in image classification, it is not analyzed for the object detection task. Each node in the hierarchy represents...
Surveillance systems require advanced algorithms able to make decisions without a human operator or with minimal assistance from human operators. In this paper we propose a novel approach for dynamic topic modeling to detect abnormal behaviour in video sequences. The topic model describes activities and behaviours in the scene assuming behaviour temporal dynamics. The new inference scheme based on...
This paper proposes a probabilistic generative model of a sequence of vectors called the latent trajectory hidden Markov model (HMM). While a conventional HMM isonly capable of describing piecewise stationary sequences of data vectors, the proposed model is capable of describing continuously time-varying sequences of data vectors, governed by discrete hidden states. This feature is noteworthy in that...
Robust visual tracking is a challenging problem due to pose variance, occlusion and cluttered backgrounds. No single feature can be robust to all possible scenarios in a video sequence. However, exploiting multiple features has demonstrated its effectiveness in overcoming challenging situations in visual tracking. We propose a new framework for multi-modal fusion at both the feature level and decision...
Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the network but analysis is often centralized or partly centralized. These systems are not scalable and suffer from the single point of failure, i.e. attackers only need...
Haptic guidance in robot-assisted therapy does not only relieve the therapist from physical work load and increase the possible training intensity. Haptic guidance can also be used to adapt the task difficulty level to the patient's abilities, which is assumed to maximize the learning rate. Hereby, task difficulty can address either the patient's physical ability (strength) or his/her coordination...
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