We introduce RadarHand, a wrist-worn wearable with millimetre wave radar that detects on-skin touch-based proprioceptive hand gestures. Radars are robust, private, small, penetrate materials, and require low computation costs. We first evaluated the proprioceptive and tactile perception nature of the back of the hand and found that tapping on the thumb is the least proprioceptive error of all the finger joints, followed by the index finger, middle finger, ring finger, and pinky finger in the eyes-free and high cognitive load situation. Next, we trained deep-learning models for gesture classification. We introduce two types of gestures based on the locations of the back of the hand: generic gestures and discrete gestures. Discrete gestures are gestures that start at specific locations and end at specific locations at the back of the hand, in contrast to generic gestures, which can start anywhere and end anywhere on the back of the hand. Out of 27 gesture group possibilities, we achieved 92% accuracy for a set of seven gestures and 93% accuracy for the set of eight discrete gestures. Finally, we evaluated RadarHand’s performance in real-time under two interaction modes: Active interaction and Reactive interaction. Active interaction is where the user initiates input to achieve the desired output, and reactive interaction is where the device initiates interaction and requires the user to react. We obtained an accuracy of 87% and 74% for active generic and discrete gestures, respectively, as well as 91% and 81.7% for reactive generic and discrete gestures, respectively. We discuss the implications of RadarHand for gesture recognition and directions for future works.

Gestures are a promising candidate as an input modality for ambient computing where conventional input modalities such as touchscreens are not available. Existing works have focused on gesture recognition using image sensors. However, their cost, high battery consumption, and privacy concerns made cameras challenging as an always-on solution. This paper introduces an efficient gesture recognition technique using a miniaturized 60 GHz radar sensor. The technique recognizes four directional swipes and an omni-swipe using a radar chip (6.5 × 5.0 mm) integrated into a mobile phone. We developed a convolutional neural network model efficient enough for battery powered and computationally constrained processors. Its model size and inference time is less than 1/5000 compared to an existing gesture recognition technique using radar. Our evaluations with large scale datasets consisting of 558,000 gesture samples and 3,920,000 negative samples demonstrated our algorithm’s efficiency, robustness, and readiness to be deployed outside of research laboratories.

Uncertainty in observations about the state of affairs is unavoidable, and generally undesirable, so we are motivated to try to minimize its effect on data analysis. Detection of anomalies in data has become an important research area. In this paper, we propose a novel approach to anomaly detection based on the Variational Autoencoder method with a Mish activation function and a Negative Log-Likelihood loss function. The proposed method is validated with ten standard datasets, comparing performance on each of the various activation functions and loss functions. Experimental results show that our proposed method offers an improvement over existing methods. Statistical properties (i.e., F1 score, AUC, and ROC) of the method are also examined in light of the experimental results.

We introduce UniAuth, a set of mechanisms for streamlining authentication to devices and web services. With UniAuth, a user first authenticates himself to his UniAuth client, typically his smartphone or wearable device. His client can then authenticate to other services on his behalf. In this paper, we focus on exploring the user experiences with an early iPhone prototype called Knock x Knock. To manage a variety of accounts securely in a usable way, Knock x Knock incorporates features not supported in existing password managers, such as tiered and location-aware lock control, authentication to laptops via knocking, and storing credentials locally while working with laptops seamlessly. In two field studies, 19 participants used Knock x Knock for one to three weeks with their own devices and accounts. Our participants were highly positive about Knock x Knock, demonstrating the desirability of our approach. We also discuss interesting edge cases and design implications.

Designing robots with cognition and consciousness resembling that of human or animal has become an important application of intelligent autonomous robot, in order to achieve a more effective human-robot interaction. On the grounds of dynamic system, the primary structure of a behavioral-emotional model of the robots consists of three processes: recognition process, cognition process and behavioral-emotional expression process. Therefore, in this paper, we propose a behavioral-emotional selection model for a conscious behavior robot (Conbe-I) based on the Self-Organizing Map (SOM) learning and the discrete stochastic state-space mathematical model (Markov model) that mainly consider the issues of an autonomous action selection corresponds to the emotional state transition and the emotional expression of an autonomous robot. Finally, the experimental results show the efficiency of the proposed system.

This paper aims to develop the research based on a pet robot and its artificial consciousness. We propose the animal behavior and emotion using the artificial neurotransmitter and motivation. This research still implements the communication between human and a pet robot respecting to a social cognitive and interaction. Thus, the development of cross-creature communication is crucial for friendly companionship. This system focuses on three points. The first that is the organization of the behavior and emotion model regarding the phylogenesis. The second is the method of the robot that can have empathy with user expression. The third is how the robot can socially perform its expression to human for encouragement or being delighted based on its own emotion and the human expression. This paper eventually presents the performance and the experiment that the robot using cross-perception and cross-expression between animal robot and social interaction of human communication based on the consciousness based architecture (CBA).

We introduce a body-based identification system that leverages individual differences in body segment lengths and hand waving gesture patterns. The system identifies users based on a two-second hand waving gesture captured by a Microsoft Kinect. To evaluate our system, we collected 8640 gesture measurements from 75 participants through two lab studies and a field study. In the first lab study, we evaluated the feasibility of our concept and basic properties of features to narrow down the design space. In the second lab study, our system achieved a 1% equal error rate in user identification among seven registered users after two weeks following initial registration. We also found that our system was robust even when lower body segments could not be measured because of occlusions. In the field study, our system achieved 0.5 to 1.6% equal error rates, demonstrating that the system also works well in ecologically valid situations. Lastly, throughout the studies, our participants were positive about the system.

We are attempting to develop a robot with a “consciousness” resembling that of a human or an animal in order to enhance an intellectual activity of the robot. In our previous study, we have constructed a model of mechanism of consciousness and behaviors based on the relationship between the action strategies and consciousness levels, this software architecture is called Consciousness-Based Architecture (CBA) module. In addition, the robot should select the behavior and express the emotion by itself, which depends on its surrounding environment. Therefore, in this paper, we have examined the intrinsic motivation and the external situations of robot are used to analyze and classify the behavior and the emotion by a Self-Organizing Map (SOM) that is trained using unsupervised learning, and we attempt to describe the integration of a behavior map and an emotion map into a CBA module. The conscious behavior robot (Conbe-I) has been implemented to show the effectiveness of the proposed system.

We explore how well the intersection between our own everyday memories and those captured by our smartphones can be used for what we call autobiographical authentication-a challenge-response authentication system that queries users about day-to-day experiences. Through three studies-two on MTurk and one field study-we found that users are good, but make systematic errors at answering autobiographical questions. Using Bayesian modeling to account for these systematic response errors, we derived a formula for computing a confidence rating that the attempting authenticator is the user from a sequence of question-answer responses. We tested our formula against five simulated adversaries based on plausible real-life counterparts. Our simulations indicate that our model of autobiographical authentication generally performs well in assigning high confidence estimates to the user and low confidence estimates to impersonating adversaries.

People use mobile devices to search, locate and discover local information around them. Mobile local search is frequently a social activity. This paper presents the results of a survey and an exploratory user study of collaborative mobile local search. The survey results show that people frequently search with others and that these searches often involve the use of more than one mobile device. We prototyped a collaborative mobile search app, which we used as a tool to investigate users' collaborative mobile search behavior. Our study results provide insights into how users collaborate while performing search. We also provide design considerations to inform future mobile local search technologies.

We introduce context-aware scalable authentication (CASA) as a way of balancing security and usability for authentication. Our core idea is to choose an appropriate form of active authentication (e.g., typing a PIN) based on the combination of multiple passive factors (e.g., a user's current location) for authentication. We provide a probabilistic framework for dynamically selecting an active authentication scheme that satisfies a specified security requirement given passive factors. We also present the results of three user studies evaluating the feasibility and users' receptiveness of our concept. Our results suggest that location data has good potential as a passive factor, and that users can reduce up to 68% of active authentications when using an implementation of CASA, compared to always using fixed active authentication. Furthermore, our participants, including those who do not using any security mechanisms on their phones, were very positive about CASA and amenable to using it on their phones.

Passwords are the most common authentication scheme today. However, it is difficult for people to memorize strong passwords, such as random sequences of characters. Additionally, passwords do not provide protection against phishing attacks. This paper introduces WebTicket, a low cost, easy-to-use and reliable web account management system that uses "tickets", which are tokens that contain a two-dimensional barcode that can be printed or stored on smartphones. Users can log into accounts by presenting the barcodes to webcams connected to computers. Through two lab studies and one field study consisting of 59 participants in total, we found that WebTicket can provide reliable authentication and phishing resilience.

Time is a difficult concept for parents to communicate with young children. We developed TimeBlocks, a novel tangible, playful object to facilitate communication about concepts of time with young children. TimeBlocks consists of a set of cubic blocks that function as a physical progress bar. Parents and children can physically manipulate the blocks to represent the concept of time. We evaluated TimeBlocks through a field study in which six families tried TimeBlocks for four days at their homes. The results indicate that TimeBlocks played a useful role in facilitating the often challenging task of time-related communication between parents and children. We also report on a range of observed insightful novel uses of TimeBlocks in our study.

This paper focuses on the development of interaction between humans and robots, we decided to give the robotic arm has a consciousness and an emotional behavior in the same way as humans and animals. Thus, we have developed the hierarchical structure model which is the relationship between consciousness field and behavior module. The model is called Consciousness-Base Architecture (CBA) that was combined with the motivation module. Moreover, the ability to learn and remember the situations, it is necessary to make for more intelligent robot. Thereby, we attempt to build a memory module in the robot system. The memory for such system needs to efficiently organize and recognize previous experience in order to retrieve the relevant data and compared with the current situation. In this paper, we present the evaluation function for autonomous action selection, the memory module has been built and the behavior of the robotic arm has been verified in the experiment.

Most mobile phones and tablets support only two access control device states: locked and unlocked. We investigated how well all or-nothing device access control meets the need of users by interviewing 20 participants who had both a smartphone and tablet. We find all-or-nothing device access control to be a remarkably poor fit with users' preferences. On both phones and tablets, participants wanted roughly half their applications to be available even when their device was locked and half protected by authentication. We also solicited participants' interest in new access control mechanisms designed specifically to facilitate device sharing. Fourteen participants out of 20 preferred these controls to existing security locks alone. Finally, we gauged participants' interest in using face and voice biometrics to authenticate to their mobile phone and tablets; participants were surprisingly receptive to biometrics, given that they were also aware of security and reliability limitations.

While past work has examined password usage on a specific computer, web site, or organization, there is little work examining overall password usage in daily life. Through a diary study, we examine all usage of passwords, and offer some new findings based on quantitative analyses regarding how often people log in, where they log in, and how frequently people use foreign computers. Our analysis also confirms or updates existing statistics about password usage patterns. We also discuss some implications for design as well as security education.

While a large body of research on image-based authentication has focused on memorability, comparatively less attention has been paid to the new security challenges these schemes may introduce. Because images can convey more information than text, image-based authentication may be more vulnerable to educated guess attacks than passwords. In this paper, we evaluate the resilience of a recognition-based graphical authentication scheme using distorted images against two types of educated guess attacks through two user studies.

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A number of recent scams and security attacks (phishing, spyware, fake terminals, ...) hinge on a crook's ability to observe user behavior. In this paper, we describe the design, implementation, and evaluation of a novel class of user authentication systems that are resilient to observation attacks.Our proposal is the first to rely on the human ability to simultaneously process multiple sensory inputs to authenticate, and is resilient to most observation attacks. We build a prototype based on user feedback gained through low fidelity tests. We conduct a within-subjects usability study of the prototype with 38 participants, which we complement with a security analysis.Our results show that users can authenticate within times comparable to that of graphical password schemes, with relatively low error rates, while being considerably better protected against observation attacks. Our design and evaluation process allows us to outline design principles for observation-resilient authentication systems.

In this paper, we propose and evaluate Use Your Illusion, a novel mechanism for user authentication that is secure and usable regardless of the size of the device on which it is used. Our system relies on the human ability to recognize a degraded version of a previously seen image. We illustrate how distorted images can be used to maintain the usability of graphical password schemes while making them more resilient to social engineering or observation attacks. Because it is difficult to mentally "revert" a degraded image, without knowledge of the original image, our scheme provides a strong line of defense against impostor access, while preserving the desirable memorability properties of graphical password schemes.

An autonomous robot and a mobile robot are expected to provide various services within the human living environment. But there are many problems to solve in trying to develop autonomous robots that can work like humans. Therefore, the environment for robot activity assumed in this study was a finite space such as a family room, an office, or a hospital room. So, accurate position estimation is important to the robot in achieving a success. Thus we have developed a navigation system that detects whether the robot is driving accurately using only a CCD camera in a room and a corridor. This paper describes that this systems are implemented and experimented with our developed robot.