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Drawing upon the motivational affordance theory, this paper aims to investigate how gamification design and human motivational needs are associated in extant literature.

The authors conducted a literature analysis of 60 journal articles that studied motivational influences of gamification in information technology design. Content analysis was used to identify game design features and motivation variables studied in prior literature, and correspondence analysis was used to show the co-occurrence of game design features and basic motivational needs.

The results showed that four types of game design features and eight basic motivational needs are studied in this pool of literature. Correspondence analysis indicates some interesting associations between game design features and basic human needs.

This research used a motivational affordance perspective to interpret the impact of game design features and suggested directions for future investigations. It is limited due to its sample size and considered as an exploratory study.

This research provided suggestions for technology designers that game design features vary in their motivational influence, and therefore, game design features should be used accordingly to meet users’ motivational needs.

This research is one of initial studies which explored the association between game design features and basic motivational needs. The findings of this study provide the groundwork for guidelines and strategies to facilitate motivational design in information technology.

Nowadays, a large amount of data related to aero engine in various types can be created in a single day and it is very important to well organize and store these data. The paper aims to discuss this issue.

This paper puts forward the problem of data management with the fast development of aero engine and sets the compression system as an example to see the inner relationships of data from the initial design procedure to the final operation and maintenance part. There are five principles, namely digitization, accuracy, normative, integrality and validity, involved in managing the data effectively.

These data resources arranged according to the five principles can be well organized and better used.

At the end, the top design of aero engine data sharing platform is investigated and five layers including data layer, data access layer, communication layer, business logic layer and application layer are designed and presented to support the platform.

The purpose of this paper is to provide a novel training-responding controlling approach for human–robot interaction. The approach is inspired by the processes of muscle memory and conditioned reflex. The approach is significant for dealing with the problems of robot’s redundant movements and operator’s fatigue in human–robot interaction system.

This paper presented a directional double clustering algorithm (DDCA) to achieve the training process. The DDCA ensured that the initial clustering centers uniformly distributed in every desired cluster. A minimal resource allocation network was used to construct a memory responding algorithm (MRA). When the human–robot interaction system needed to carry out a task for more than one time, the desired movements of the robot were given by the MRA without repeated training. Experimentally demonstrated results showed the proposed training-responding controlling approach could successfully accomplish human–robot interaction tasks.

The training-responding controlling approach improved the robustness and reliability of the human–robot interaction system, which presented a novel controlling method for the operator.

This approach has significant commercial applications, as a means of controlling for human–robot interaction could serve to point to the desired target and arrive at the appointed positions in industrial and household environment.

This work presented a novel training-responding human-robot controlling method. The human-robot controlling method dealt with the problems of robot’s redundant movements and operator’s fatigue. To the authors’ knowledge, the working processes of muscle memory and conditioned reflex have not been reported to apply to human-robot controlling.

The aim of this paper is to propose a grasping method based on intelligent perception for implementing a grasp task with human conduct.

First, the authors leverage Kinect to collect the environment information including both image and voice. The target object is located and segmented by gesture recognition and speech analysis and finally grasped through path teaching. To obtain the posture of the human gesture accurately, the authors use the Kalman filtering (KF) algorithm to calibrate the posture use the Gaussian mixture model (GMM) for human motion modeling, and then use Gaussian mixed regression (GMR) to predict human motion posture.

In the point-cloud information, many of which are useless, the authors combined human’s gesture to remove irrelevant objects in the environment as much as possible, which can help to reduce the computation while dividing and recognizing objects; at the same time to reduce the computation, the authors used the sampling algorithm based on the voxel grid.

The authors used the down-sampling algorithm, kd-tree algorithm and viewpoint feature histogram algorithm to remove the impact of unrelated objects and to get a better grasp of the state.

The purpose of this paper is to provide a novel methodology based on two-level protection for ensuring safety of the moving human who enters the robot’s workspace, which is significant for dealing with the problem of human security in a human-robot coexisting environment.

In this system, anyone who enters the robot’s working space is detected by using the Kinect and their skeletons are calculated by the interval Kalman filter in real time. The first-level protection is mainly based on the prediction of the human motion, which used Gaussian mixture model and Gaussian Mixture Regression. However, even in cases where the prediction of human motion is incorrect, the system can still safeguard the human by enlarging the initial bounding volume of the human as the second-level early warning areas. Finally, an artificial potential field with some additional avoidance strategies is used to plan a path for a robot manipulator.

Experimental studies on the GOOGOL GRB3016 robot show that the robot manipulator can accomplish the predetermined tasks by circumventing the human, and the human does not feel dangerous.

This study presented a new framework for ensuring human security in a human-robot coexisting environment, and thus can improve the reliability of human-robot cooperation.

The recent recession provides us a good window to reveal fiscal problems and inadequate preparations of state and local governments. To address the fiscal crisis, and more importantly, to prepare for the potential economic downturns, this paper designs a framework of necessary tools to combat fiscal crisis. With matching policies of revenue diversification and counter-cyclical fiscal policy (CCFP), debt financing can be used as an effective tool to help state and local governments pull through fiscal crises. A brief example of local governments in Georgia proves the possibility and effectiveness of the counter-cyclical debt policy. It will be much better to institutionalize these policies to avoid the moral hazards of governments and politicians, which, in a great sense, requires engaging and educating the public and, finally, obtaining support from them.

Due to the strategic, economic, and social significance of information and communication technology development and use, a better understanding of factors that contribute to technology acceptance and use decisions can be extremely important. In this chapter, we posit that one of the fundamental reasons that people utilize technology is to support their well-being by fulfiling their various needs. Taking this motivational perspective, we suggest that the purposes and utilities of information and communication technology should support various human needs. Using a motivational approach to study technology design is intended to be positive. We revisit some fundamentals that may have been forgotten and we unearth the intrinsic drive of technology development and use. As a first step toward a design theory, we propose ten design principles to achieve high motivating information and communication technology.

The purpose of this paper is to present a markerless human–manipulators interface which maps the position and orientation of human end-effector (EE, the center of the palm) to those of robot EE so that the robot could copy the movement of the operator hand.

The tracking system of this human–manipulators interface comprises five Leap Motions (LMs) which not only makes up the narrow workspace drawback of one LM but also provides redundancies to improve the data precision. However, because of the native noises and tracking errors of the LMs, the measurement errors increase over time. To address this problem, two filter tools are integrated to obtain the relatively accurate estimation of the human EE, that is, Particle Filter for position estimation and Kalman Filter for orientation estimation. Because the operator has inherent perceptive limitations, the motions of the manipulator may be out of sync with the hand motions, so that it is hard to complete with the high performance manipulation. Therefore, in this paper, an over-damping method is adopted to improve reliability and accuracy.

A series of human–manipulators interaction experiments were carried out to verify the proposed system. Compared with the markerless and contactless methods(Kofman et al., 2007; Du and Zhang, 2015), the method described in this study is more accurate and efficient.

The proposed method would not hinder most natural human limb motion and allows the operator to concentrate on his/her own task, making it perform high-precision manipulations efficiently.

This paper aims to develop a wearable-based human-manipulator interface which integrates the interval Kalman filter (IKF), unscented Kalman filter (UKF), over damping method (ODM) and adaptive multispace transformation (AMT) to perform immersive human-manipulator interaction by interacting the natural and continuous motion of the human operator’s hand with the robot manipulator.

The interface requires that a wearable watch is tightly worn on the operator’s hand to track the continuous movements of the operator’s hand. Nevertheless, the measurement errors generated by the sensor error and tracking failure signicantly occur several times, which means that the measurement is not determined with sufficient accuracy. Due to this fact, IKF and UKF are used to compensate for the noisy and incomplete measurements, and ODM is established to eliminate the influence of the error signals like data jitter. Furthermore, to be subject to the inherent perceptive limitations of the human operator and the motor, AMT that focuses on a secondary treatment is also introduced.

Experimental studies on the GOOGOL GRB3016 robot show that such a wearable-based interface that incorporates the feedback mechanism and hybrid filters can operate the robot manipulator more flexibly and advantageously even if the operator is nonprofessional; the feedback mechanism introduced here can successfully assist in improving the performance of the interface.

The interface uses one wearable watch to simultaneously track the orientation and position of the operator’s hand; it is not only avoids problems of occlusion, identification and limited operating space, but also realizes a kind of two-way human-manipulator interaction, a feedback mechanism can be triggered in the watch to reflect the system states in real time. Furthermore, the interface gets rid of the synchronization question in posture estimation, as hybrid filters work independently to compensate the noisy measurements respectively.

This paper proposes a hybrid biogeography‐based optimization (BBO) with simplex method (SM) algorithm (HSMBBO).

BBO is a new intelligent optimization algorithm. The global optimization ability of BBO is better than that of genetic algorithm (GA) and particle swarm optimization (PSO), but BBO also easily falls into local minimum. To improve BBO, HSMBBO combines BBO and SM, which makes full use of the high local search ability of SM. In HSMBBO, BBO is used firstly to obtain the current global solution. Then SM is searched to acquire the optimum solution based on that global solution. Due to the searching of SM, the search range is expanded and the speed of convergence is faster. Meanwhile, HSMBBO is applied to motion estimation of video coding.

In total, six benchmark functions with multimodal and high dimension are tested. Simulation results show that HSMBBO outperforms GA, PSO and BBO in converging speed and global search ability. Meanwhile, the application results show that HSMBBO performs better than GA, PSO and BBO in terms of both searching precision and time‐consumption.

The proposed algorithm improves the BBO algorithm and provides a new approach for motion estimation of video coding.