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We propose to use social networking data to validate mobility models for pervasive mobile ad-hoc networks (MANETs) and delay tolerant networks (DTNs). The Random Waypoint (RWP) and Erdos-Renyi (ER) models have been a popular choice among researchers for generating mobility traces of nodes and relationships between them. Not only RWP and ER are useful in evaluating networking protocols in a simulation...
The availability of massive network and mobility data from diverse domains has fostered the analysis of human behaviors and interactions. This data availability leads to challenges in the knowledge discovery community. Several different analyses have been performed on the traces of human trajectories, such as understanding the real borders of human mobility or mining social interactions derived from...
We propose an online method for grasp motion learning using the Gaussian Process Dynamic Model (GPDM). Given human grasp motion data (in the form of position and orientation trajectories of the fingertips and palm), from approach to final grasp pose, a GPDM is trained with this data, and then used to generate new grasping motions even when the path to the object is partially blocked by obstacles....
As a certain case in the domain of human actions, hand gestures can be expressed by the motion of user's hand to provide nature interaction in many applications. In this paper we proposed a real-time hand gesture recognition system based on robust hand tracking from depth image sequences. Using hidden markov models (HMM) with varying states, gesture models are trained online along with user's feedback,...
Multimedia applications like retrieval, copy detection etc. can gain from saliency detection, which is essentially a method to identify areas in images and videos that capture the attention of the human visual system. In this paper, we propose a new spatiotemporal saliency framework for videos based on sparse representation. For temporal saliency, we model the movement of the target patch as a reconstruction...
Human gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present...
The main objective of this work is to study the degree of engagement levels of human subjects in completing a series of tasks based on their physiological signals. The tasks are in the form of tracking a set of trajectories on the computer screen by using a mouse… The subjects are chosen randomly based on both sexes, aged from 20 to 40 years old. After completing the required tasks in a series of...
In this paper, an exoskeleton robotic device worn by human operator is developed mainly for support and rehabilitation, as well as improvement of human capabilities. In order to enhance the device ability to finely interact with a human limb, it is necessary to develop a control interface for exoskeletons, which would guarantee comfort and safety, as well as efficiency and robustness. The developed...
Reaching to an object is a fundamental skill to a robot. The purpose of robot reaching aims at bringing the robot hands to the object location without obstacle collision. Since implementing such robot reaching movement is a tedious task, this paper proposes a method to synthesize robot reaching movement by human demonstration. However, human demonstrations are inconsistent, this paper adopts Gaussian...
Since the structure and D.O.F. of human and machine are different, the motion of human and machine is essentially different. This study defines "Pseudo Human Motion" as approximated machine's motions referring to the standard human motion. If the pseudo human motion is realized, the operation of complex machine becomes easy, because the motion of complex machine becomes human-like motion...
This paper aims at stimulating the discussion on the future of Intelligent Vehicles. It is a position paper, indicating converging technologies that, in our opinion, will have to be used in future Intelligent Vehicles. We present a vision according to which Intelligent Vehicles will evolve into Human-peer Robots, here called Co-Drivers. Co-Drivers will be able to “understand” human drivers and to...
Lane change is one of the most principle driving behaviors on structure roads. It frequently happens in daily driving. A key issue in lane change technique is trajectory planning, where a set of trajectories describing possible vehicle motions are generated by applying a parametric function, and by uniformly sampling the end states in configuration space; the trajectories are then examined to find...
The bipedal spring-mass model embodies important characteristics of human walking, and therefore serves as an important starting point in studying human-like walking for robots. In this paper, we propose to extend the bipedal spring-mass model with variable leg stiffness and exploit the potential of this model in order to mimic the human capability to continuously adapt the leg stiffness to different...
To investigate fast human reaching movements in 3D, we asked 11 right-handed persons to catch a tennis ball while we tracked the movements of their arms. To ensure consistent trajectories of the ball, we used a catapult to throw the ball from three different positions. Tangential velocity profiles of the hand were in general bell-shaped and hand movements in 3D coincided with well known results for...
Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. In this paper, we address such problems by proposing a robust part-based tracking-by-detection framework. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Our approach learns part-based person-specific SVM...
We present a novel method for analyzing social behavior. Continuous videos are segmented into action ‘bouts’ by building a temporal context model that combines features from spatio-temporal energy and agent trajectories. The method is tested on an unprecedented dataset of videos of interacting pairs of mice, which was collected as part of a state-of-the-art neurophysiological study of behavior. The...
We present a novel sensing paradigm of measuring human gait. The goal of the research is to achieve low-cost gait biometrics systems with minimum data throughput for various sensing modalities. The binary measurements of the system are achieved by using both (1) periodic and (2) pseudo-random sampling structures. As a result, either static or dynamic gait features can be estimated from a one-bit data...
In this paper, an integrated framework with multiple sensory information for analysing human hand motions is proposed, and it consists of components of system integration, signal preprocessing, correlation study of sensory information and human motion recognition based on manipulation intention. Three types of sensors are employed in the framework to simultaneously capture the finger angle trajectory,...
While activity recognition is a current focus of research the challenging problem of fine-grained activity recognition is largely overlooked. We thus propose a novel database of 65 cooking activities, continuously recorded in a realistic setting. Activities are distinguished by fine-grained body motions that have low inter-class variability and high intra-class variability due to diverse subjects...
This paper proposes a method for learning the kicking motion of an inverted-pendulum mobile robot from the observation of a human player's demonstration. An inverted-pendulum mobile robot with upper and lower body links observes the human demonstration with a motion capture system and estimates the posture of each human links. The robot maps the links to its own two links and estimates link posture...
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