We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
On the other hand, there has been a lot of effort in developing methods for reducing the cognitive load on the user in 2D-GUIs. In @cite_77 , simple constraining of motions was effective in offloading some of the cognitive effort. The familiarity and widespread use of smartphones and tablets has also brought attention to touch-based control. In @cite_78 , a simple touch-based interface was aimed at reducing operator fatigue.
@cite_77 @cite_78
2592582799 2017919391
The interface is the main mechanism of communication between user and system features. In educational software, successful user interface designs minimize the cognitive load on users, thereby users can direct their efforts to maximize their understanding of the educational concepts being presented. We investigated whether a reduced interface make few cognitive demands on users in comparison to a complete interface. In this context, this research aims at analyzing a reduced and a complete interface of an interactive geometry software, and verify the educational benefits they provide. To this end, we designed the interfaces and carried out an experiment involving 69 undergraduate students. The experimental results indicate that an interface that hides advanced and extraneous features helps novice users to perform slightly better than novice users using a complete interface. After receiving proper training, however, a complete interface makes users more productive than a reduced interface. Touch-based verification --- the use of touch gestures (e.g., swiping, zooming, etc.) to authenticate users of touch screen devices --- has recently been widely evaluated for its potential to serve as a second layer of defense to the PIN lock mechanism. In all performance evaluations of touch-based authentication systems however, researchers have assumed naive (zero-effort) forgeries in which the attacker makes no effort to mimic a given gesture pattern. In this paper we demonstrate that a simple "Lego" robot driven by input gleaned from general population swiping statistics can generate forgeries that achieve alarmingly high penetration rates against touch-based authentication systems. Using the best classification algorithms in touch-based authentication, we rigorously explore the effect of the attack, finding that it increases the Equal Error Rates of the classifiers by between 339 and 1004 depending on parameters such as the failure-to-enroll threshold and the type of touch stroke generated by the robot. The paper calls into question the zero-effort impostor testing approach used to benchmark the performance of touch-based authentication systems. Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user’s touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A onemonth experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication. Human-Computer Interaction (HCI) exists ubiquitously in our daily lives. It is usually achieved by using a physical controller such as a mouse, keyboard or touch screen. It hinders Natural User Interface (NUI) as there is a strong barrier between the user and computer. There are various hand tracking systems available on the market, but they are complex and expensive. In this paper, we present the design and development of a robust marker-less hand finger tracking and gesture recognition system using low-cost hardware. We propose a simple but efficient method that allows robust and fast hand tracking despite complex background and motion blur. Our system is able to translate the detected hands or gestures into different functional inputs and interfaces with other applications via several methods. It enables intuitive HCI and interactive motion gaming. We also developed sample applications that can utilize the inputs from the hand tracking system. Our results show that an intuitive HCI and motion gaming system can be achieved with minimum hardware requirements. We describe two approaches to reducing human fatigue in interactive evolutionary computation (IEC). A predictor function is used to estimate the human user's score, thus reducing the amount of effort required by the human user during the evolution process. The fuzzy system and four machine learning classifier algorithms are presented. Their performance in a real-world application, the IEC-based design of a micromachine resonating mass, is evaluated. The fuzzy system was composed of four simple rules, but was able to accurately predict the user's score 77 of the time on average. This is equivalent to a 51 reduction of human effort compared to using IEC without the predictor. The four machine learning approaches tested were k-nearest neighbors, decision tree, AdaBoosted decision tree, and support vector machines. These approaches achieved good accuracy on validation tests, but because of the great diversity in user scoring behavior, were unable to achieve equivalent results on the user test data. We present a novel end-to-end solution for distributed multirobot coordination that translates multitouch gestures into low-level control inputs for teams of robots. Highlighting the need for a holistic solution to the problem of scalable human control of multirobot teams, we present a novel control algorithm with provable guarantees on the robots’ motion that lends itself well to input from modern tablet and smartphone interfaces. Concretely, we develop an iOS application in which the user is presented with a team of robots and a bounding box (prism). The user carefully translates and scales the prism in a virtual environment; these prism coordinates are wirelessly transferred to our server and then received as input to distributed onboard robot controllers. We develop a novel distributed multirobot control policy which provides guarantees on convergence to a goal with distance bounded linearly in the number of robots, and avoids interrobot collisions. This approach allows the human user to solve the cognitive tasks such as path planning, while leaving precise motion to the robots. Our system was tested in simulation and experiments, demonstrating its utility and effectiveness. Remote control robots are being found in an increasing number of application domains, including search and rescue, exploration, and reconnaissance. There is a large body of HRI research that investigates interface design for remote navigation, control, and sensor monitoring, while aiming for interface enhancements that benefit the remote operator such as improving ease of use, reducing operator mental load, and maximizing awareness of a robot's state and remote environment. Even though many remote control robots have multi-degree-of-freedom robotic manipulator arms for interacting with the environment, there is only limited research into easy-to-use remote control interfaces for such manipulators, and many commercial robotic products are still using simplistic interface technologies such as keypads or gamepads with arbitrary mappings to arm morphology. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly. Touch screens are an increasingly common feature on personal computing devices, especially smartphones, where size and user interface advantages accrue from consolidating multiple hardware components (keyboard, number pad, etc.) into a single software definable user interface. Oily residues, or smudges, on the touch screen surface, are one side effect of touches from which frequently used patterns such as a graphical password might be inferred. In this paper we examine the feasibility of such smudge attacks on touch screens for smartphones, and focus our analysis on the Android password pattern. We first investigate the conditions (e.g., lighting and camera orientation) under which smudges are easily extracted. In the vast majority of settings, partial or complete patterns are easily retrieved. We also emulate usage situations that interfere with pattern identification, and show that pattern smudges continue to be recognizable. Finally, we provide a preliminary analysis of applying the information learned in a smudge attack to guessing an Android password pattern. Tabletop systems have become quite popular in recent years, during which there was considerable enthusiasm for the development of new interfaces. In this paper, we establish a comparison between touch and tangible interfaces. We set up an experiment involving several actions like translation and rotation. We recruited 40 participants to take part in a user study and we present our results with a discussion on the design of touch and tangible interfaces. Our contribution is an empirical study showing that overall, the tangible interface is much faster but under certain conditions, the touch interface could gain the upper hand. Users often struggle to enter text accurately on touchscreen keyboards. To address this, we present a flexible decoder for touchscreen text entry that combines probabilistic touch models with a language model. We investigate two different touch models. The first touch model is based on a Gaussian Process regression approach and implicitly models the inherent uncertainty of the touching process. The second touch model allows users to explicitly control the uncertainty via touch pressure. Using the first model we show that the character error rate can be reduced by up to 7 over a baseline method, and by up to 1.3 over a leading commercial keyboard. Using the second model we demonstrate that providing users with control over input certainty reduces the amount of text users have to correct manually and increases the text entry rate. We introduce C-Slate, a new vision-based system, which utilizes stereo cameras above a commercially available tablet technology to support remote collaboration. The horizontally mounted tablet provides the user with high resolution stylus input, which is augmented by multi-touch interaction and recognition of untagged everyday physical objects using new stereo vision and machine learning techniques. This provides a novel and interesting interactive tabletop arrangement, capable of supporting a variety of fluid multi-touch interactions, including symmetric and asymmetric bimanual input, coupled with the potential for incorporating tangible objects into the user interface. When used in a remote context, these features are combined with the ability to see visual representations of remote users' hands and remote physical objects placed on top of the surface. This combination of bimanual and tangible interaction and sharing of remote gestures and physical objects provides a new way to collaborate remotely, complementing existing channels such as audio and video conferencing. This paper shows that the location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smartphones and tablets, and silently monitor the user's inputs, such as keyboard presses and icon taps. While precise tap detection is nontrivial, requiring machine learning algorithms to identify fingerprints of closely spaced keys, sensitive sensors on modern devices aid the process. We present TapPrints, a framework for inferring the location of taps on mobile device touch-screens using motion sensor data combined with machine learning analysis. By running tests on two different off-the-shelf smartphones and a tablet computer we show that identifying tap locations on the screen and inferring English letters could be done with up to 90 and 80 accuracy, respectively. By optimizing the core tap detection capability with additional information, such as contextual priors, we are able to further magnify the core threat. The lack of dedicated multitasking interface features in smartphones has resulted in users attempting a sequential form of multitasking via frequent app switching. In addition to the obvious temporal cost, it requires physical and cognitive effort which increases multifold as the back and forth switching becomes more frequent. We propose porous interfaces, a paradigm that combines the concept of translucent windows with finger identification to support efficient multitasking on small screens. Porous interfaces enable partially transparent app windows overlaid on top of each other, each of them being accessible simultaneously using a different finger as input. We design porous interfaces to include a broad range of multitasking interactions with and between windows, while ensuring fidelity with the existing smartphone interactions. We develop an end-to-end smartphone interface that demonstrates porous interfaces. In a qualitative study, participants found porous interfaces intuitive, easy, and useful for frequent multitasking scenarios. In this paper, we present a novel acoustic touch sensing technique called Touch & Activate. It recognizes a rich context of touches including grasp on existing objects by attaching only a vibration speaker and a piezo-electric microphone paired as a sensor. It provides easy hardware configuration for prototyping interactive objects that have touch input capability. We conducted a controlled experiment to measure the accuracy and trade-off between the accuracy and number of training rounds for our technique. From its results, per-user recognition accuracies with five touch gestures for a plastic toy as a simple example and six hand postures for the posture recognition as a complex example were 99.6 and 86.3 , respectively. Walk up user recognition accuracies for the two applications were 97.8 and 71.2 , respectively. Since the results of our experiment showed a promising accuracy for the recognition of touch gestures and hand postures, Touch & Activate should be feasible for prototype interactive objects that have touch input capability. We present a mobile user interface which dynamically reformulates the layout based on the touch input pattern of users. By analyzing the touch input, it infers users' physical characteristics such as handedness, finger length, or usage habits, thereby calculates the optimal touch area for the user. The user interface is gradually adapted to each user by automatically rearranging graphic objects such as application icons to the most easy-to-touch positions. To compute the optimal touch area, we designed software architecture and implemented an Android application which analyzes touch input and determines the touch frequency in specific screen areas, the handedness and hand size of users. As proof of concept, this research prototype shows acceptable performance and accuracy. To decide which items should be placed in the optimal touch area, we plan to integrate our machine learning algorithm which prioritizes applications according to the context of users into the proposed system. Touchscreens continue to advance including progress towards sensing fingers proximal to the display. We explore this emerging pre-touch modality via a self-capacitance touchscreen that can sense multiple fingers above a mobile device, as well as grip around the screen's edges. This capability opens up many possibilities for mobile interaction. For example, using pre-touch in an anticipatory role affords an "ad-lib interface" that fades in a different UI--appropriate to the context--as the user approaches one-handed with a thumb, two-handed with an index finger, or even with a pinch or two thumbs. Or we can interpret pre-touch in a retroactive manner that leverages the approach trajectory to discern whether the user made contact with a ballistic vs. a finely-targeted motion. Pre-touch also enables hybrid touch + hover gestures, such as selecting an icon with the thumb while bringing a second finger into range to invoke a context menu at a convenient location. Collectively these techniques illustrate how pre-touch sensing offers an intriguing new back-channel for mobile interaction. Direct manipulation has been lauded as a good form of interface design, and some interfaces that have this property have been well received by users. In this article we seek a cognitive account of both the advantages and disadvantages of direct manipulation interfaces. We identify two underlying phenomena that give rise to the feeling of directness. One deals with the information processing distance between the user's intentions and the facilities provided by the machine. Reduction of this distance makes the interface feel direct by reducing the effort required of the user to accomplish goals. The second phenomenon concerns the relation between the input and output vocabularies of the interface language. In particular, direct manipulation requires that the system provide representations of objects that behave as if they are the objects themselves. This provides the feeling of directness of manipulation. Vision-based interfaces, such as those made popular by the Microsoft Kinect, suffer from the Midas Touch problem: every user motion can be interpreted as an interaction. In response, we developed an algorithm that combines facial features, body pose and motion to approximate a user's intention to interact with the system. We show how this can be used to determine when to pay attention to a user's actions and when to ignore them. To demonstrate the value of our approach, we present results from a 30-person lab study conducted to compare four engagement algorithms in single and multi-user scenarios. We found that combining intention to interact with a 'raise an open hand in front of you' gesture yielded the best results. The latter approach offers a 12 improvement in accuracy and a 20 reduction in time to engage over a baseline 'wave to engage' gesture currently used on the Xbox 360. We present an experimental comparison of multi-touch and tangible user interfaces for basic interface actions. Twelve participants completed manipulation and acquisition tasks on an interactive surface in each of three conditions: tangible user interface; multi-touch; and mouse and puck. We found that interface control objects in the tangible condition were easiest to acquire and, once acquired, were easier more accurate to manipulate. Further qualitative analysis suggested that in the evaluated tasks tangibles offer greater adaptability of control and specifically highlighted a problem of exit error that can undermine fine-grained control in multi-touch interactions. We discuss the implications of these findings for interface design. We explore techniques for hand-held devices that leverage the multimodal combination of touch and motion. Hybrid touch + motion gestures exhibit interaction properties that combine the strengths of multi-touch with those of motion-sensing. This affords touch-enhanced motion gestures, such as one-handed zooming by holding one's thumb on the screen while tilting a device. We also consider the reverse perspective, that of motion-enhanced touch, which uses motion sensors to probe what happens underneath the surface of touch. Touching the screen induces secondary accelerations and angular velocities in the sensors. For example, our prototype uses motion sensors to distinguish gently swiping a finger on the screen from 'Sdrags with a hard onset' - to enable more expressive touch interactions.