A neural model for eye–head–arm coordination

The coordinated movement of the eyes, the head and the arm is an important ability in both animals and humanoid robots. To achieve this, the brain and the robot control system need to be able to perform complex non-linear sensory-motor transformations in the forward and inverse directions between many degrees of freedom. In this article, we apply an omnidirectional basis function neural network to this task. The proposed network can perform 3-D coordinated gaze shifts and 3-D arm reaching movements to a visual target. Particularly, it can perform direct sensory-motor transformations to shift gaze and to execute arm reach movements and can also perform inverse sensory-motor transformations in order to shift gaze to view the hand.     

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

Performing manipulation tasks interactively in real environments requires a high degree of accuracy and stability. At the same time, when one cannot assume a fully deterministic and static environment, one must endow the robot with the ability to react rapidly to sudden changes in the environment. These considerations make the task of reach and grasp difficult to deal with. We follow a Programming by Demonstration (PbD) approach to the problem and take inspiration from the way humans adapt their reach and grasp motions when perturbed. This is in sharp contrast to previous work in PbD that uses unperturbed motions for training the system and then applies perturbation solely during the testing phase. In this work, we record the kinematics of arm and fingers of human subjects during unperturbed and perturbed reach and grasp motions. In the perturbed demonstrations, the target’s location is changed suddenly after the onset of the motion. Data show a strong coupling between the hand transport and finger motions. We hypothesize that this coupling enables the subject to seamlessly and rapidly adapt the finger motion in coordination with the hand posture. To endow our robot with this competence, we develop a coupled dynamical system based controller, whereby two dynamical systems driving the hand and finger motions are coupled. This offers a compact encoding for reach-to-grasp motions that ensures fast adaptation with zero latency for re-planning. We show in simulation and on the real iCub robot that this coupling ensures smooth and “human-like” motions. We demonstrate the performance of our model under spatial, temporal and grasp type perturbations which show that reaching the target with coordinated hand–arm motion is necessary for the success of the task.     

Hand–eye calibration with epipolar constraints: Application to endoscopy

In this paper, we propose a new calibration method for a hand–eye system equipped with a camera undergoing radial distortion as a rigid endoscope. While classic methods propose either a separated estimation of the camera intrinsics and the hand–eye transform or a mixed non-linear estimation of both hand–eye and camera intrinsics assuming a pin-hole model, the proposed approach enables a simultaneous refinement of the hand–eye and the camera parameters including the distortion factor with only three frames of the calibrated pattern. We propose and compare two criteria to minimize: (i) the first one is based on the epipolar constraint between pairs of frames and (ii) the second one expresses the camera extrinsic with respect to hand–eye and world-grid transforms in the single frame reprojection error. We run bundle-adjustment on each criterion with respect to the distortion parameters, the camera intrinsics and the hand–eye transform. Our method allows us to recover more accurate 3D shapes when compared to state-of-the-art non-linear methods.     

Using focal point learning to improve human–machine tacit coordination

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tacit coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tacit coordination domains. Experiments have shown that humans are often able to coordinate with one another in communication-free games, by using focal points, “prominent” solutions to coordination problems. We integrate focal point rules into the machine learning process, by transforming raw domain data into a new hypothesis space. We present extensive empirical results from three different tacit coordination domains. The Focal Point Learning approach results in classifiers with a 40–80% higher correct classification rate, and shorter training time, than when using regular classifiers, and a 35% higher correct classification rate than classical focal point techniques without learning. In addition, the integration of focal points into learning algorithms results in agents that are more robust to changes in the environment. We also present several results describing various biases that might arise in Focal Point based coordination.     

Does fatigue exist in a quantitative measurement of eye movements?

Abstract

The measurement system for quantitative analysis of eye movements and distribution of eye fixation points was developed through the study. Experiments on physiological fatigue characteristics of eye movements were studied using the system. The subjects involved in the study were six young males. No significant change was quantitatively found in saccadic eye movements during and/or after five hours of rapid eye tracking tasks. The saccadic velocity of two subjects were found in binocular decreased temporarily. The maximum velocity of eye movements obtained in the present experiment was ascertained in order to produce a scale for various visual work as an ergonomic index.     

The application of polynomial analyses to detect global vegetation dynamics during 1982–2012

Use of the normalized difference vegetation index (NDVI) to build long-term vegetation trends is one of the most effective techniques for identifying global environmental change. Trend identification can be achieved by ordinary least squares (OLS) analysis or the Theil–Sen (TS) procedure with a Mann–Kendall (MK) significance test, and these linear regression approaches have been widely used. However, vegetation changes are not linear, and thus the response of vegetation to global climate change may follow non-linear trends. In this article, a polynomial trend-fitting method, which uses stepwise regression and expands on previous research, is presented. With an improved fitting ability, this procedure may reveal trends that were concealed by linear fitting methods. Globally, the traditional TS-MK method reveals significant greening trends for 37.27% of vegetated land, and significant browning trends for 7.98%. Using the polynomial analysis, 34.62% of pixels were fitted by high-order trends. The significant greening trends covered up to 30% of cultivated land, thus indicating that cultivated vegetation may be increasing faster than natural vegetation. Significant vegetation browning mostly occurred in sparse vegetation areas, which suggests that vegetation growth may be more sensitive to climate change in arid regions. Our results show that use of polynomial analysis can help further elucidate global NDVI trends.     

Exploiting distinctive visual landmark maps in pan–tilt–zoom camera networks

Pan–tilt–zoom (PTZ) camera networks have an important role in surveillance systems. They have the ability to direct the attention to interesting events that occur in the scene. One method to achieve such behavior is to use a process known as sensor slaving: one (or more) master camera monitors a wide area and tracks moving targets so as to provide the positional information to one (or more) slave camera. The slave camera can thus point towards the targets in high resolution.In this paper we describe a novel framework exploiting a PTZ camera network to achieve high accuracy in the task of relating the feet position of a person in the image of the master camera, to his head position in the image of the slave camera. Each camera in the network can act as a master or slave camera, thus allowing the coverage of wide and geometrically complex areas with a relatively small number of sensors.The proposed framework does not require any 3D known location to be specified, and allows to take into account both zooming and target uncertainties. Quantitative results show good performance in target head localization, independently from the zooming factor in the slave camera. An example of cooperative tracking approach exploiting with the proposed framework is also presented.     

An object‐oriented methodology to detect oil spills

A new automated methodology for oil spill detection is presented, by which full synthetic aperture radar (SAR) high‐resolution image scenes can be processed. The methodology relies on the object‐oriented approach and profits from image segmentation techniques to detected dark formations. The detection of dark formations is based on a threshold definition that is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas are developed that also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations as oil spills or look‐alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments that affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The accuracy of the method for the 12 SAR images used is 99.5% for the class of oil spills, and 98.8% for that of look‐alikes. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.     

Analysis of vertical eye movements in Parkinson’s disease and its potential for diagnosis

Applied Intelligence - It is well known that eye movements are highly affected by Parkinson’s disease. The majority of studies related to effects of Parkinson’s disease on eye movements...     

A geometric approach to solving the stable workspace of quadruped bionic robot with hand–foot-integrated function

This paper discusses stable workspace of a hand–foot-integrated quadruped walking robot, which is an important issue for stable operation of the robot. This robot was provided with combined structure of parallel and serial mechanisms, whose stable workspace was the subspace of the workspace in which the system was considered stable. The reachable region was formed under structural conditions, while the stable space was formed by the overall conditions of stability which changed with the robot's pose and the mass of grabbed object. In this paper, based on the robot's main structure, the key issues in solving the robot's workspace are discussed in detail, including searching steady conditions of operation of the robot. To research the robot's workspace, working leg's motion curve needed to be solved by kinematics analysis. Due to the redundant drive, it was problematic to deal analytically with the kinematics of the quadruped walking robot. A geometric method of kinematic analysis was proposed as well. Based on the geometric method, the workspace of the robot under varying postures was analyzed by the method of grid partition and in combination with Matlab, VB and Solidworks software programs. An automated computational system of the stable workspace was developed and an example was given to illustrate the whole process in detail. The theory and analysis procedures were also verified by simulation of the robot and its actual grabbing of an object.     

A mobile-agent-based approach to software coordination in the HOOPE system

Software coordination is central to the construction of large-scale high-performance distributed applications with software services scattered over the decentralized Internet. In this paper, a new mobile-agent-based architecture is proposed for the utilization and coordination of geographically distributed computing resources. Under this architecture, a user application is built with a set of software agents that can travel across the network autonomously. These agents utilize the distributed resources and coordinate with each other to complete their task. This approach' s advantages include the natural expression and flexible deployment of the coordination logic, the dynamic adaptation to the network environment and the potential of better application performance. This coordination architecture, together with an object-oriented hierarchical parallel application framework and a graphical application construction tool, is implemented in the HOOPE environment, which provides a systematic support for the de     

A novel coupled and self-adaptive under-actuated multi-fingered hand with gear–rack–slider mechanism

Aiming to overcome the serious disadvantages of two kinds of under-actuated fingers: coupled finger and self-adaptive finger, this paper proposed a novel grasping mode, called Coupled and Self-Adaptive (COSA) grasping mode, which includes two stages: first coupled and self-adaptive grasping. A 2-joint COSA finger with a double gear–rack–slider mechanism (called COSA-GRS finger), is developed based on the COSA grasping mode: at the beginning, the 2-joint finger bends with coupled mode, two joints of the finger rotate simultaneously with a fixed ratio until the proximal phalanx touches the grasped object, then the finger will automatically decouple and rotate with self-adaptive mode, the distal phalanx quickly rotates until it touches the object. The new finger unit has the advantages of coupled fingers and self-adaptive fingers. The finger is not only able to rotate all joints simultaneously to pre-shape before grasping objects, but also able to self-adapt different sizes and shapes of objects. Using the same mechanism as the 2-joint finger, a 3-joint COSA finger is designed. Force analyses and a structure optimization rule of the new finger are given and discussed. The simulation results show that the finger unit is effective: it can successfully realize coupling and decoupling and it can stably grasp objects. An under-actuated humanoid robot hand is developed, called the COSA-GRS Hand. The hand has 5 fingers, 15 joints and 6 motors. All fingers of the hand are COSA fingers. The hand is more similar to human hand in appearance and actions, able to grasp different objects more dexterously and stably than traditional coupled or self-adaptive under-actuated hands.     

Exploring speed–accuracy tradeoff in reaching movements: a neurocomputational model

Neural Computing and Applications - The tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace...     

Exploiting users’ social relations to forward data in opportunistic networks: The HiBOp solution

Opportunistic networks, in which nodes opportunistically exploit any pair-wise contact to identify next hops towards the destination, are one of the most interesting technologies to support the pervasive networking vision. Opportunistic networks allow content sharing between mobile users without requiring any pre-existing Internet infrastructure, and tolerate partitions, long disconnections, and topology instability in general. In this paper we propose a context-aware framework for routing and forwarding in opportunistic networks. The framework is general, and able to host various flavors of context-aware routing. In this work we also present a particular protocol, HiBOp, which, by exploiting the framework, learns and represents through context information, the users’ behavior and their social relations, and uses this knowledge to drive the forwarding process. The comparison of HiBOp with reference to alternative solutions shows that a context-aware approach based on users’ social relations turns out to be a very efficient solution for forwarding in opportunistic networks. We show performance improvements over the reference solutions both in terms of resource utilization and in terms of user perceived QoS.     

Adaptive control based on IF–THEN rules for grasping force regulation with unknown contact mechanism

An industrial gripping application with unknown contact mechanism is considered as a class of unknown nonlinear discrete-time systems. The control scheme is developed by an adaptive network called multi-input fuzzy rules emulated network (MiFREN) within discrete-time domain. The network structure is directly constructed regarding to IF–THEN rules related to gripper and contact mechanism properties. All adjustable parameters require only the on-line learning phase to improve the closed loop performance. The time varying learning rate is devised for gradient reach with the proof of stability analysis. Furthermore, the estimated sensitivity of system dynamic is directly considered within the parameter adaptation. The experimental system with an industrial parallel grip model WSG-50 validates the performance of the proposed controller.     

Human–computer interaction using vision-based hand gesture recognition systems: a survey

Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified into two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human--computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.     

A TCPN based approach to model the coordination in virtual manufacturing organizations☆

The recent developments of communication technologies and network computing have enabled the occurrence of the advanced manufacturing strategy—agile manufacturing. The essence of the agile manufacturing strategy is to form a virtual manufacturing organization (VMO), which integrates the core competencies of member enterprises in order to respond to the global market and increasing customer requirements rapidly. In a VMO, the manufacturing processes are distributed. So modeling and coordination of these processes are very complex. In this paper, we present a Petri Net based approach to model these distributed manufacturing processes and interaction relations among them. A workflow based manufacturing process execution environment is also established to support the implementation of the distributed manufacturing processes.     

The influence of lumbar extensor muscle fatigue on lumbar–pelvic coordination during weightlifting

Lumbar muscle fatigue is a potential risk factor for the development of low back pain. In this study, we investigated the influence of lumbar extensor muscle fatigue on lumbar–pelvic coordination patterns during weightlifting. Each of the 15 male subjects performed five repetitions of weightlifting tasks both before and after a lumbar extensor muscle fatiguing protocol. Lumbar muscle electromyography was collected to assess fatigue. Trunk kinematics was recorded to calculate lumbar–pelvic continuous relative phase (CRP) and CRP variability. Results showed that fatigue significantly reduced the average lumbar–pelvic CRP value (from 0.33 to 0.29 rad) during weightlifting. The average CRP variability reduced from 0.17 to 0.15 rad, yet this change ws statistically not significant. Further analyses also discovered elevated spinal loading during weightlifting after the development of lumbar extensor muscle fatigue. Our results suggest that frequently experienced lumbar extensor muscle fatigue should be avoided in an occupational environment.

Practitioner Summary: Lumbar extensor muscle fatigue generates more in-phase lumbar–pelvic coordination patterns and elevated spinal loading during lifting. Such increase in spinal loading may indicate higher risk of back injury. Our results suggest that frequently experienced lumbar muscle fatigue should be avoided to reduce the risk of LBP.