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Random forest based Hough-voting techniques have been widely used in a variety of computer vision problems. As an ensemble learning method, the voting weights of leaf nodes in random forest play critical role to generate reliable estimation result. We propose to improve Hough-voting with random forest via simultaneously optimizing the weights of leaf votes and pruning unreliable leaf nodes in the forest. After constructing the random forest, the weight assignment problem at each tree is formulated as a L0-regularized optimization problem, where unreliable leaf nodes with zero voting weights are suppressed and trees are pruned to ignore sub-trees that contain only suppressed leaves. We apply our proposed techniques to several regression and classification problems such as hand gesture recognition, head pose estimation and articulated pose estimation. The experimental results demonstrate that by suppressing unreliable leaf nodes, it not only improves prediction accuracy, but also reduces both prediction time cost and model complexity of the random forest.

User ratings are the essence of recommender systems in e-commerce. Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of such systems and contribute to the well-known data sparsity and cold start problems. This article proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users’ experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments. A method is proposed to show how to leverage prior ratings in collaborative filtering. Experimental results indicate the effectiveness of prior ratings in improving predictive performance.

Hand pose tracking and gesture recognition are useful for human-computer interaction, while a major problem is the lack of discriminative features for compact hand representation. We present a robust hand parsing scheme to extract a high-level description of the hand from the depth image. A novel distance-adaptive selection method is proposed to get more discriminative depth-context features. Besides, we propose a Superpixel-Markov Random Field (SMRF) parsing scheme to enforce the spatial smoothness and the label co-occurrence prior to remove the misclassified regions. Compared to pixel-level filtering, the SMRF scheme is more suitable to model the misclassified regions. By fusing the temporal constraints, its performance can be further improved. Overall, the proposed hand parsing scheme is accurate and efficient. The tests on synthesized dataset show it gives much higher accuracy for single-frame parsing and enhanced robustness for continuous sequence parsing compared to benchmarks. The tests on real-world depth images of the hand and human body show the robustness to complex hand configurations of our method and its generalization power to different kinds of articulated objects.

Simulating a crowded scene like a busy shopping street requires tight packing of virtual characters. In such cases, collisions are likely to occur, and the choice in collision detection shape will influence how characters are allowed to intermingle. Full collision detection is too expensive for crowds, so simplifications are needed. The most common simplification, the fixed-width, pose-independent cylinder, does not allow intermingling of characters, as it will either cause too much empty space between characters or undetected penetrations. As a possible solution to this problem, we introduce the bounding cylinder hierarchy BCH, a bounding volume hierarchy that uses vertical cylinders as bounding shapes. Because the BCH is a generalization of the single cylinder, we expect that this representation can be easily integrated with existing crowd simulation systems. We compare our BCH with commonly used collision shapes, namely the single cylinder and oriented bounding box tree, in terms of query time, construction time, and represented volume. To get an indication of possible crowd densities, we investigate how close characters can be before collision is detected and finally propose a critical maximum depth for the BCH. Copyright © 2014 John Wiley & Sons, Ltd.

Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. However, it suffers from several inherent issues such as data sparsity and cold start. To address these problems, we propose a novel method called ''Merge'' to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations. Specifically, ratings of a user's trusted neighbors are merged to complement and represent the preferences of the user and to find other users with similar preferences (i.e., similar users). In addition, the quality of merged ratings is measured by the confidence considering the number of ratings and the ratio of conflicts between positive and negative opinions. Further, the rating confidence is incorporated into the computation of user similarity. The prediction for a given item is generated by aggregating the ratings of similar users. Experimental results based on three real-world data sets demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage.

In this paper, we propose a method to achieve effective facial emotional expressivity for embodied conversational agents by considering two types of asymmetry when exploiting the valence-arousal-dominance representation of emotions. Indeed, the asymmetry of facial expressions helps to convey complex emotional feelings such as conflicting and/or hidden emotions due to social conventions. To achieve such a higher degree of facial expression in a generic way, we propose a new model for mapping the valence-arousal-dominance emotion model onto a set of 12 scalar facial part actions built mostly by combining pairs of antagonist action units from the Facial Action Coding System. The proposed linear model can automatically drive a large number of autonomous virtual humans or support the interactive design of complex facial expressions over time. By design, our approach produces symmetric facial expressions, as expected for most of the emotional spectrum. However, more complex ambivalent feelings can be produced when differing emotions are applied on the left and right sides of the face. We conducted an experiment on static images produced by our approach to compare the expressive power of symmetric and asymmetric facial expressions for a set of eight basic and complex emotions. Results confirm both the pertinence of our general mapping for expressing basic emotions and the significant improvement brought by asymmetry for expressing ambivalent feelings. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper we present a novel vision-based markerless hand pose estimation scheme with the input of depth image sequences. The proposed scheme exploits both temporal constraints and spatial features of the input sequence, and focuses on hand parsing and 3D fingertip localization for hand pose estimation. The hand parsing algorithm incorporates a novel spatial-temporal feature into a Bayesian inference framework to assign the correct label to each image pixel. The 3D fingertip localization algorithm adapts a recently developed geodesic extrema extraction method to fingertip detection with the hand parsing algorithm, a novel path-reweighting method and K-means clustering in metric space. The detected 3D fingertip locations are finally used for hand pose estimation with an inverse kinematics solver. Quantitative experiments on synthetic data show the proposed hand pose estimation scheme can accurately capture the natural hand motion. A simulated water-oscillator application is also built to demonstrate the effectiveness of the proposed method in human-computer interaction scenarios.

The recent proliferation of virtual reality (VR) technology applications in the autism therapy to promote learning and positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-assisted therapy has also become a topic of public and research interest for autism intervention and treatment. This paper will present an innovative design and development of a Virtual Dolphinarium for potential autism intervention. Instead of emulating the swimming with dolphins, our virtual dolphin interaction program will allow children with autism to act as dolphin trainers at the poolside and to learn (nonverbal) communication through hand gestures with the virtual dolphins. Immersive visualization and gesture-based interaction are implemented to engage children with autism within an immersive room equipped with a curved screen spanning a 320(°) and a high-end five-panel projection system. This paper will also report a pilot study to establish trial protocol of autism screening to explore the participants' readiness for the virtual dolphin interaction. This research will have two potential benefits in the sense of helping children with autism and protecting the endangered species.

Many on-line communities still rely on text messages as their privileged medium of communication and we believe it will remain so even if it can be complemented by images and video. Previous chapters have described how to infer emergent emotion dynamics from text corpus whereas we focus here on the instantaneous display of a detected emotion with a human avatar. The avatar acts as a new modality to provide an emotional visual stream of the messages through facial and body Non-Verbal Communication (NVC). In this chapter we concentrate on chat applications for which the time constraint is critical for preserving the natural flow of the discussion. We describe an efficient mapping of the Valence-Arousal-Dominance emotion model to real-time facial expressions. Within that framework, we stress the need for reconsidering the expressive power of asymmetric facial expression for better conveying a whole range of complex and ambivalent emotions. In the last section we briefly outline how the emotional avatar modality can be offered with recent software standards across systems and display environments.

Articulated hand pose estimation plays an important role in human-computer interaction. Despite the recent progress, the accuracy of existing methods is still not satisfactory, partially due to the difficulty of embedded high-dimensional and non-linear regression problem. Different from the existing discriminative methods that regress for the hand pose with a single depth image, we propose to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane. These multi-view heat-maps are then fused to produce final 3D hand pose estimation with learned pose priors. Experiments show that the proposed method largely outperforms state-of-the-art on a challenging dataset. Moreover, a cross-dataset experiment also demonstrates the good generalization ability of the proposed method., Comment: 9 pages, 9 figures, published at Computer Vision and Pattern Recognition (CVPR) 2016

Humans are not rational beings. Deviations from rationality in human thinking are currently well documented [25] as non-reducible to rational pursuit of egoistic benefit or its occasional distortion with temporary emotional excitation, as it is often assumed. This occurs not only outside conceptual reasoning or rational goal realization but also subconsciously and often in certainty that they did not and could not take place 'in my case'. Non-rationality can no longer be perceived as a rare affective abnormality in otherwise rational thinking, but as a systemic, permanent quality, 'a design feature' of human cognition. While social psychology has systematically addressed non-rationality of human cognition (including its non-emotional aspects) for decades [63]. It is not the case for computer science, despite obvious relevance for individual and group behavior modeling. This paper proposes brief survey of work in computational disciplines related to human-like non-rationality modeling including: Social Signal Processing, Cognitive Architectures, Affective Computing, Human-Like Agents and Normative Multi-agent Systems. It attempts to establish a common terminology and conceptual frame for this extremely interdisciplinary issue, reveal assumptions about non-rationality underlying the discussed models and disciplines, their current limitations and potential in contributing to solution. Finally, it also presents ideas concerning possible directions of development, hopefully contributing to solution of this challenging issue.

This paper presents an immersive volleyball game, where a player plays not only against virtual opponents but also with support on his/her side of virtual teammates. This volleyball game has been implemented for several 3D displays such as a stereoscopic display, an autostereoscopic display, Oculus Rift glasses, and a 320o Immersive Room. In this paper, we also propose a user study of the relations between virtual humans and the sense of presence in the different 3D displays. We particularly study how surrounding virtual humans affect the sense of presence. Results show that users more significantly perceived spatial presence of virtual environment and social presence of virtual humans with the Oculus Rift and the Immersive Room.

Negative symptoms in schizophrenia are associated with significant burden and functional impairment, especially speech production. In clinical practice today, there are no robust treatments for negative symptoms and one obstacle surrounding its research is the lack of an objective measure. To this end, we explore non-verbal speech cues as objective measures. Specifically, we extract these cues while schizophrenic patients are interviewed by psychologists. We have analyzed interviews of 15 patients who were enrolled in an observational study on the effectiveness of Cognitive Remediation Therapy (CRT). The subject (undergoing CRT) and control group (not undergoing CRT) contains 8 and 7 individuals respectively. The patients were recorded during three sessions while being evaluated for negative symptoms over a 12-week follow-up period. In order to validate the non-verbal speech cues, we computed their correlation with the Negative Symptom Assessment (NSA-16). Our results suggest a strong correlation between certain measures of the two rating sets. Supervised prediction of the subjective ratings from the non-verbal speech features with leave-one-person-out cross-validation has reasonable accuracy of 53–80%. Furthermore, the non-verbal cues can be used to reliably distinguish between the subjects and controls, as supervised learning methods can classify the two groups with 80–93% accuracy.

We present an Augmented Reality solution to allow users to manipulate and inspect 3D virtual objects freely with their bare hands on wearable devices. To this end, we use a head-mounted depth camera to capture the RGB-D hand images from egocentric view, and propose a unified framework to jointly recover the 6D palm pose and recognize the hand gesture from the depth images. The random forest is utilized to regress for the palm pose and classify the hand gesture simultaneously via a spatial-voting framework. With a real-world annotated training dataset, the proposed method shows to predict the palm pose and gesture accurately. The output of the forest is used to render the 3D virtual objects, which are overlaid onto the hand region in input RGB images with camera calibration parameters to provide seamless virtual and real scene synthesis.

Research on no-reference image quality assessment IQA aims to develop a computational model simulating the human perception of image quality accurately and automatically without any prior information about the reference clean image signals. In this paper, we introduce a novel no-reference IQA metric, based on the analysis of structural degradation and luminance changes. Since the human visual system HVS is highly sensitive to structural distortion, we encode the image structural information as the local binary pattern LBP distribution. Besides, image quality is also affected by luminance changes, which cannot be captured properly by LBP threshold mechanism. Hence, the distribution of normalized luminance magnitudes is also included in the proposed IQA metric. Extensive experiments conducted on two large public image databases have demonstrated the effectiveness and robustness of the proposed metric in comparison with the relevant state-of-the-art metrics.

This is the first book to describe how Autonomous Virtual Humans and Social Robots can interact with real people, be aware of the environment around them, and react to various situations. Researchers from around the world present the main techniques for tracking and analysing humans and their behaviour and contemplate the potential for these virtual humans and robots to replace or stand in for their human counterparts, tackling areas such as awareness and reactions to real world stimuli and using the same modalities as humans do: verbal and body gestures, facial expressions and gaze to aid seamless human-computer interaction (HCI). The research presented in this volume is split into three sections: User Understanding through Multisensory Perception: deals with the analysis and recognition of a given situation or stimuli, addressing issues of facial recognition, body gestures and sound localization. Facial and Body Modelling Animation: presents the methods used in modelling and animating faces and bodies to generate realistic motion. Modelling Human Behaviours: presents the behavioural aspects of virtual humans and social robots when interacting and reacting to real humans and each other. Context Aware Human-Robot and Human-Agent Interaction would be of great use to students, academics and industry specialists in areas like Robotics, HCI, and Computer Graphics.

Traversing dense crowds can be challenging, but it is especially difficult in a virtual environment where the user is limited to simple input devices. Predicting near-future user movements with a hidden Markov model allows nearby autonomous agents to react politely, that is, to specifically avoid impeding the movement of the user. This predictive model and simple avoidance scheme are tested by six subjects in 360 short interactive games and by a 10-participant two-alternative forced choice questionnaire. Polite agents are found to significantly improve the player's ability to navigate quickly and without collision, and the resulting character movements showed increased naturalness for two of the three game scenarios. Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents an interactive multi-agent system based on a fully immersive virtual environment. A user can interact with the virtual characters in real time via an avatar by changing their moving behavior. Moreover, the user is allowed to select any character as the avatar to be controlled. A path planning algorithm is proposed to address the problem of dynamic navigation of individual and groups of characters in the multi-agent system. A natural interface is designed for the interaction between the user and the virtual characters, as well as the virtual environment, based on gesture recognition. To evaluate the efficiency of the dynamic navigation method, performance results are provided. The presented system has the potential to be used in the training and evaluation of emergency evacuation and other real-time applications of crowd simulation with interaction.

Computer-based simulations of human hip joints generally include investigating contacts happening among soft or hard tissues during hip movement. In many cases, hip movement is approximated as rotation about an estimated hip center. In this paper, we investigate the effect of different methods used for estimating hip joint center of rotation on the results acquired from hip simulation. For this reason, we use three dimensional models of hip tissues reconstructed from MRI datasets of 10 subjects, and estimate their center of rotation by applying five different methods (including both predictive and functional approaches). Then, we calculate the amount of angular and radial penetrations that happen among three dimensional meshes of cartilages, labrum, and femur bone, when hip is rotating about different estimated centers of rotation. The results indicate that hip simulation can be highly affected by the method used for estimating hip center of rotation. However, under some conditions (e.g. when Adduction or External Rotation are considered) we can expect to have a more robust simulation. In addition, it was observed that applying some methods (e.g. the predictive approach based on acetabulum) may result in less robust simulation, comparing to the other methods.

In this paper, we present an action selection mechanism to simulate the human behavior in realistic and believable virtual humans according to their personality and affective state taking into account their beliefs, desires, intentions, and the level of intensity of the events they perceive from their environment.

We present a visualization framework for exploring and analyzing data sets from biomechanical and neuromuscular simulations. These data sets describe versatile information related to the different stages of a motion analysis. In studying these data using a 3D visualization approach, interactive exploring is important, especially for supporting spatial analysis. Moreover, as these data contain many various but related elements, numerical analysis of neuromuscular simulations is complicated. Visualization techniques enhance the analysis process, thus improving the effectiveness of the experiments. Our approach allows convenient definitions of relationships between numerical data sets and 3D objects. Scientific simulation data sets appropriate for this style of analysis are present everywhere motion analysis is performed and are predominant in many clinical works. In this paper, we outline the functionalities of the framework as well as applications embedded within the OpenSim simulation platform. These functionalities form an effective approach specifically designed for the investigation of neuromuscular simulations. This claim is supported by evaluation experiments where the framework was used to analyze gaits and crouch motions.

In the recent years, usage of the third-person perspective (3PP) in virtual training methods has become increasingly viable and despite the growing interest in virtual reality and graphics underlying third-person perspective usage, not many studies have systematically looked at the dynamics and differences between the third and first-person perspectives (1PPs). The current study was designed to quantify the differences between the effects induced by training participants to the third-person and first-person perspectives in a ball catching task. Our results show that for a certain trajectory of the stimulus, the performance of the participants post3PP training is similar to their performance postnormal perspective training. Performance post1PP training varies significantly from both 3PP and the normal perspective.

This paper presents a novel concept: a graphical representation of human emotion extracted from text sentences. The major contributions of this paper are the following. First, we present a pipeline that extracts, processes, and renders emotion of 3D virtual human (VH). The extraction of emotion is based on data mining statistic of large cyberspace databases. Second, we propose methods to optimize this computational pipeline so that real-time virtual reality rendering can be achieved on common PCs. Third, we use the Poisson distribution to transfer database extracted lexical and language parameters into coherent intensities of valence and arousal—parameters of Russell’s circumplex model of emotion. The last contribution is a practical color interpretation of emotion that influences the emotional aspect of rendered VHs. To test our method’s efficiency, computational statistics related to classical or untypical cases of emotion are provided. In order to evaluate our approach, we applied our method to diverse areas such as cyberspace forums, comics, and theater dialogs.

We introduce a new method for computerized real-time evaluation of femoroacetabular impingement (FAI). In contrast to previously presented stress analyses, this method is based on two types of predictions of penetration depths for two rotating bodies: curvilinear and radial penetration depth. This intuitive method allows the analysis of both bony and soft tissue structures (such as cartilage and acetabular labrum) in real time. Characteristic penetration depth patterns were found for different subtypes of FAI, such as cam and pincer pathologies. In addition, correlation between the penetration depths (estimated by applying this method) and the existing contact stresses (estimated by applying the finite element method) of various hip morphologies were found. A strong correlation with predicted stress values existed, with a mean correlation coefficient of 0.91 for the curvilinear and 0.80 for the radial penetration method. The results show that the penetration depth method is a promising, fast, and accurate method for quantification and diagnosis of FAI.

In this paper, we propose to increase realism and complexity of haptic applications for Virtual Reality and CAD applications. To achieve this goal, we use a Haptic Workstation which allows to acquire the posture and position of both hands, and to apply forces on the fingertips and wrists. We propose techniques to calibrate and improve the comfort of these kinds of devices in order to integrate them into Virtual Environments. However, a two-handed haptic device does not present only advantages. Indeed, It is much more complicated to compute forces on two hand models, than on a single point or fingertip. For this reason, we propose a framework to optimize this computation.

Real-time crowd motion planning requires fast, realistic methods for path planning as well as obstacle avoidance. In a previous work (Morini et al. in Cyberworlds International Conference, pp. 144–151, 2007), we introduced a hybrid architecture to handle real-time motion planning of thousands of pedestrians. In this article, we present an extended version of our architecture, introducing two new features: an improved short-term collision avoidance algorithm, and simple efficient group behavior for crowds. Our approach allows the use of several motion planning algorithms of different precision for regions of varied interest. Pedestrian motion continuity is ensured when switching between such algorithms. To assess our architecture, several performance tests have been conducted, as well as a subjective test demonstrating the impact of using groups. Our results show that the architecture can plan motion in real time for several thousands of characters.

In this paper, we introduce a european re- search pro ject, interactive media with personal networked devices (INTERMEDIA) in which we seek to progress beyond home and device-centric convergence toward truly user-centric convergence of multimedia. Our vision is The User as Multimedia Central: the user as the point at which multimedia services and the means for interact- ing with them converge. This paper proposes the main research goals so that users can be provided with a per- sonalized interface and content independently of physical networked devices, space and time. As a case study, we describe an indoors, mobile mixed reality guide system: Chloe@University. With a see-through head-mounted dis- play (HMD) connected to a small wearable computing device, Chloe@University provides users with an efficient way of guiding in a building. A 3D virtual character in front of the user guides him/her to the required destina- tion.

Wearable devices such as Microsoft Hololens and Google glass are highly popular in recent years. As traditional input hardware is difficult to use on such platforms, vision-based hand pose tracking and gesture control techniques are more suitable alternatives. This demo shows the possibility to interact with 3D contents with bare hands on wearable devices by two Augmented Reality applications, including virtual teapot manipulation and fountain animation in hand. Technically, we use a head-mounted depth camera to capture the RGB-D images from egocentric view, and adopt the random forest to regress for the palm pose and classify the hand gesture simultaneously via a spatial-voting framework. The predicted pose and gesture are used to render the 3D virtual objects, which are overlaid onto the hand region in input RGB images with camera calibration parameters for seamless virtual and real scene synthesis.

In this chapter, we introduce a concept of shared object manipulation between real and virtual humans. The shared object manipulation allows both real and virtual humans to collaborate in real-time. These can be applied to 3D telepresence applications such as computer-aided design and virtual simulation and training. However, it is still far from achieving the expected results from this area because it consists of three different and complex research domains. Firstly, we need to consider a virtual object grasping method for intuitive and convenient virtual object manipulation. Secondly, human-like animation is required for virtual object manipulation by virtual humans. Thirdly, consistency management of shared object manipulation is required to avoid conflicts from multiple simultaneous inputs. After review of state of the art, solution approaches and their limitations are introduced. We conclude with a discussion of future directions for the shared object manipulation between real and virtual humans.

In this chapter, a nonverbal way of communication for human–robot interaction by understanding human upper body gestures will be addressed. The human–robot interaction system based on a novel combination of sensors is proposed. It allows one person to interact with a humanoid social robot with natural body language. The robot can understand the meaning of human upper body gestures and express itself by using a combination of body movements, facial expressions, and verbal language. A set of 12 upper body gestures is involved for communication. Human–object interactions are also included in these gestures. The gestures can be characterized by the head, arm, and hand posture information. CyberGlove II is employed to capture the hand posture. This feature is combined with the head and arm posture information captured from Microsoft Kinect. This is a new sensor solution for human-gesture capture. Based on the body posture data, an effective and real-time human gesture recognition method is proposed. For experiments, a human body gesture dataset was built. The experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Autonomous virtual humans need to be able to interact between each others in virtual environments. These interactions are essentials for the generation of realistic behaviours from virtual humans. This chapter presents a review about interactions between real and multiple virtual humans, as well as between themselves. After presenting the problematics and approaches raised by virtual humans interactions, different methods for simulating such interactions are discussed. Interactions between real and multiple virtual humans are presented first with a focus on virtual assistants and social phobia examples. Interactions between virtual humans are then adressed, particularly gaze attention of other characters and navigation interactions between multiple virtual humans.

This paper proposes a real time sociometric system to analyze social behavior from audio-visual recordings of two-person face-to-face conversations in English. The novelty of the proposed system lies in this automatic inference of ten social indicators in real time. The system comprises of a Microsoft kinect device that captures RGB and depth data to compute visual cues and microphones to capture speech cues from an on-going conversation. With these non-verbal cues as features, machine learning algorithms are implemented in the system to extract multiple indicators of social behavior including empathy, confusion and politeness. The system is trained and tested on two carefully annotated corpora that consist of two person dialogs. Based on leave-one-out cross-validation test, the accuracy range of developed algorithms to infer social behaviors is 50% – 86% for audio corpus, and 62% – 92% for audio-visual corpus.

Heading perception of an aircraft becomes uneasy under disturbing spatial disorientation for a single pilot performing the daily tasks inherent to a long term flight. Sleepiness, movements and other activities introduced by vital function necessity reduce the attention and the awareness of the current aircraft situation. This paper presents the development of a system which aims at decreasing the attention needs for maintaining an aircraft’s attitude and take corrective action when the autopilot goes off bound. An embedded system has been integrated in the pilot’s clothing. It sends vibro-tactile feedback to the pilot when his aircraft becomes off balance. The system also dynamically localizes the position of the actuators in order to insure a feedback constant in space independently form the pilot posture and movements. A series of tests have been conducted to validate the interest of this localization by showing a slight improvement in the response time needed to take corrective action. By increasing the pilot’s own feeling about his plane’s orientation, the system provides a complementary tool to improve exhausting long flight conditions.