Multisensory texture exploration at the tip of the pen

A tool for the multisensory stylus-based exploration of virtual textures was used to investigate how different feedback modalities (static or dynamically deformed images, vibration, sound) affect exploratory gestures. To this end, we ran an experiment where participants had to steer a path with the stylus through a curved corridor on the surface of a graphic tablet/display, and we measured steering time, dispersion of trajectories, and applied force. Despite the variety of subjective impressions elicited by the different feedback conditions, we found that only nonvisual feedback induced significant variations in trajectories and an increase in movement time. In a post-experiment, using a paper-and-wood physical realization of the same texture, we recorded a variety of gestural behaviors markedly different from those found with the virtual texture. With the physical setup, movement time was shorter and texture-dependent lateral accelerations could be observed. This work highlights the limits of multisensory pseudo-haptic techniques in the exploration of surface textures.     

SpeedSeg: A technique for segmenting pen strokes using pen speed

We present SpeedSeg, a technique for segmenting pen strokes into lines and arcs. The technique uses pen speed information to help infer the segmentation intended by the drawer. To begin, an initial set of candidate segment points is identified. This set includes speed minima below a threshold, and curvature maxima at which the pen speed is also below a threshold. The ink between each pair of consecutive segment points is then classified as either a line or an arc, depending on which fits best. Next, a feedback process is employed, and segments are judiciously merged and split as necessary to improve the quality of the segmentation. In user studies, SpeedSeg performed accurately for new users. The studies also demonstrated that SpeedSeg's accuracy is surprisingly insensitive to the values of many of the empirical parameters used by the technique. However, it is still possible to quickly tune the system to optimize performance for a given user. Finally, SpeedSeg outperformed a state-of-the-art segmentation algorithm.     

An exploration strategy for non-stationary opponents

The success or failure of any learning algorithm is partially due to the exploration strategy it exerts. However, most exploration strategies assume that the environment is stationary and non-strategic. In this work we shed light on how to design exploration strategies in non-stationary and adversarial environments. Our proposed adversarial drift exploration (DE) is able to efficiently explore the state space while keeping track of regions of the environment that have changed. This proposed exploration is general enough to be applied in single agent non-stationary environments as well as in multiagent settings where the opponent changes its strategy in time. We use a two agent strategic interaction setting to test this new type of exploration, where the opponent switches between different behavioral patterns to emulate a non-deterministic, stochastic and adversarial environment. The agent’s objective is to learn a model of the opponent’s strategy to act optimally. Our contribution is twofold. First, we present DE as a strategy for switch detection. Second, we propose a new algorithm called R-max# for learning and planning against non-stationary opponent. To handle such opponents, R-max# reasons and acts in terms of two objectives: (1) to maximize utilities in the short term while learning and (2) eventually explore opponent behavioral changes. We provide theoretical results showing that R-max# is guaranteed to detect the opponent’s switch and learn a new model in terms of finite sample complexity. R-max# makes efficient use of exploration experiences, which results in rapid adaptation and efficient DE, to deal with the non-stationary nature of the opponent. We show experimentally how using DE outperforms the state of the art algorithms that were explicitly designed for modeling opponents (in terms average rewards) in two complimentary domains.     

Interaction on-the-go: a fine-grained exploration on wearable PROCAM interfaces and gestures in mobile situations

Wearable projector and camera (PROCAM) interfaces, which provide a natural, intuitive and spatial experience, have been studied for many years. However, existing hand input research into such systems revolved around investigations into stable settings such as sitting or standing, not fully satisfying interaction requirements in sophisticated real life, especially when people are moving. Besides, increasingly more mobile phone users use their phones while walking. As a mobile computing device, the wearable PROCAM system should allow for the fact that mobility could influence usability and user experience. This paper proposes a wearable PROCAM system, with which the user can interact by inputting with finger gestures like the hover gesture and the pinch gesture on projected surfaces. A lab-based evaluation was organized, which mainly compared two gestures (the pinch gesture and the hover gesture) in three situations (sitting, standing and walking) to find out: (1) How and to what degree does mobility influence different gesture inputs? Are there any significant differences between gesture inputs in different settings? (2) What reasons cause these differences? (3) What do people think about the configuration in such systems and to what extent does the manual focus impact such interactions? From qualitative and quantitative points of view, the main findings imply that mobility impacts gesture interactions in varying degrees. The pinch gesture undergoes less influence than the hover gesture in mobile settings. Both gestures were impacted more in walking state than in sitting and standing states by all four negative factors (lack of coordination, jittering hand effect, tired forearms and extra attention paid). Manual focus influenced mobile projection interaction. Based on the findings, implications are discussed for the design of a mobile projection interface with gestures.     

基于ARM和加速度传感器的电子画笔设计

介绍了当前不同类型的电子笔,比较了它们之间的优缺点并应用微控制器ADμC7022、三轴低量级加速度传感器MA7260Q和无线USB接口芯片CYRF6934实现了基于空间加速度计算的无线电子画笔的设计。     

An annotation scheme for conversational gestures: how to economically capture timing and form

The empirical investigation of human gesture stands at the center of multiple research disciplines, and various gesture annotation schemes exist, with varying degrees of precision and required annotation effort. We present a gesture annotation scheme for the specific purpose of automatically generating and animating character-specific hand/arm gestures, but with potential general value. We focus on how to capture temporal structure and locational information with relatively little annotation effort. The scheme is evaluated in terms of how accurately it captures the original gestures by re-creating those gestures on an animated character using the annotated data. This paper presents our scheme in detail and compares it to other approaches.     

Designing concurrent full-body gestures for intense gameplay

Full body gestures provide alternative input to video games that are more natural and intuitive. However, full-body game gestures designed by developers may not always be the most suitable gestures available. A key challenge in full-body game gestural interfaces lies in how to design gestures such that they accommodate the intensive, dynamic nature of video games, e.g., several gestures may need to be executed simultaneously using different body parts. This paper investigates suitable simultaneous full-body game gestures, with the aim of accommodating high interactivity during intense gameplay. Three user studies were conducted: first, to determine user preferences, a user-elicitation study was conducted where participants were asked to define gestures for common game actions/commands; second, to identify suitable and alternative body parts, participants were asked to rate the suitability of each body part (one and two hands, one and two legs, head, eyes, and torso) for common game actions/commands; third, to explore the consensus of suitable simultaneous gestures, we proposed a novel choice-based elicitation approach where participants were asked to mix and match gestures from a predefined list to produce their preferred simultaneous gestures. Our key findings include (i) user preferences of game gestures, (ii) a set of suitable and alternative body parts for common game actions/commands, (iii) a consensus set of simultaneous full-body game gestures that assist interaction in different interactive game situations, and (iv) generalized design guidelines for future full-body game interfaces. These results can assist designers and practitioners to develop more effective full-body game gestural interfaces or other highly interactive full-body gestural interfaces.     

An exploration into evolutionary models for non-routine design

Non-routine design is characterized by the lack of knowledge regarding the relationships between the given requirements and the form required to satisfy those requirements. Thus a knowledge-lean methodology, as characterized by evolutionary models, seems suitable for non-routine design. However, evolutionary methods to date, as characterized by genetic algorithms (GAs), have been developed mainly for optimization and machine learning. This paper explores some of the issues involved in the use of evolutionary models as computational models in non-routine design.     

Modeling and evaluating beat gestures for social robots

Multimedia Tools and Applications - Natural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we...     

Evaluation of in-air hand gestures interaction for older people

Universal Access in the Information Society - In-air gestural interfaces are gaining popularity due to the increasing availability and low cost of gesture recognition hardware. However, the current...     

Recognition of dynamic hand gestures

This paper is concerned with the problem of recognition of dynamic hand gestures. We have considered gestures which are sequences of distinct hand poses. In these gestures hand poses can undergo motion and discrete changes. However, continuous deformations of the hand shapes are not permitted. We have developed a recognition engine which can reliably recognize these gestures despite individual variations. The engine also has the ability to detect start and end of gesture sequences in an automated fashion. The recognition strategy uses a combination of static shape recognition (performed using contour discriminant analysis), Kalman filter based hand tracking and a HMM based temporal characterization scheme. The system is fairly robust to background clutter and uses skin color for static shape recognition and tracking. A real time implementation on standard hardware is developed. Experimental results establish the effectiveness of the approach.     

触觉手势情感识别的超限学习方法

为了解决声音和图像情感识别的不足,提出一种新的情感识别方式:触觉情感识别。对CoST（corpus of social touch）数据集进行了一系列触觉情感识别研究,对CoST数据集进行数据预处理,提出一些关于触觉情感识别的特征。利用极限学习机分类器探究不同手势下的情感识别,对14种手势下的3种情感（温柔、正常、暴躁）进行识别,准确度较高,且识别速度快识别时间短。结果表明,手势的不同会影响情感识别的准确率,其中手势“stroke”的识别效果在不同分类器下的分类精度均为最高,且有较好的分类精度,达到72.07%;极限学习机作为触觉情感识别的分类器,具有较好的分类效果,识别速度快;有的手势本身对应着某种情感,从而影响分类结果。     

A framework for designing sensor-based interactions to promote exploration and reflection in play

Sensor-based interactions are increasingly being used in the design of user experiences, ranging from the activation of controls to the delivery of ‘context-aware’ information in the home. The benefits of doing so include the ability to deliver relevant information to people at appropriate times and to enable ‘hands-free’ control. A downside, however, is that sensor control often displaces user control, resulting in the user not knowing how to or being able to control aspects of a system. While this can be frustrating in many situations, it provides new opportunities for enhancing or augmenting various kinds of activities, where uncertainty can be exploited to good effect. We describe how we designed an adventure game for young children that incorporated a number of sensor-based interactions. We also present a preliminary conceptual framework intended to help designers and researchers develop novel user experiences using sensor-based interactions. A set of concepts are provided that characterize salient aspects of the user experience involved in sensing together with a discussion of the core properties of sensor technologies.     

An investigation of the usability of the stylus pen for various age groups on personal digital assistants

Many handheld devices with stylus pens are available in the market; however, there have been few studies which examine the effects of the size of the stylus pen on user performance and subjective preferences for handheld device interfaces for various age groups. Two experiments (pen-length experiment and pen-tip width/pen-width experiment) were conducted to determine the most suitable dimensions (pen-length, pen-tip width and pen-width) for a stylus pen among young adult, child and older users. In pen-length experiment, five pen-lengths (7, 9, 11, 13 and 15 cm) were evaluated for young adult, child and older users, respectively. In pen-tip width/pen-width experiment, six combinations of three pen-tip widths (0.5, 1.0 and 1.5 mm) and two pen-widths (4 and 7 mm) were compared for young adult, child and older users, respectively. In both experiments, subjects conducted pointing, steering and writing tasks on a PDA. The results were assessed in terms of user performance and subjective evaluations for all three pointing, steering and writing tasks. We determined that the most suitable dimensions of the pen are as follows: pen-length 11–15 cm, pen-tip width 1.0–1.5 mm and pen-width 7 mm for older users; for child users, pen-length 7–13 cm, pen-tip width 1.0–1.5 mm and pen-width 4 mm; and for young adult users, pen-length 11 cm, pen-tip width 0.5 mm, and pen-width 7 mm.     

一种室内移动机器人自主探索方法

研究室内未知环境下的移动机器人自主探索问题,并提出改进策略.首先,提出一种基于可通行区域的探索目标点快速提取方法,以补充原有方法在特殊环境结构下出现的提取探索目标点失败的缺陷;然后,提出一种基于激光数据和栅格地图信息的实时拓扑地图构建与优化方法,使得探索地图更加精简,探索过程更加高效;最后,通过改进的避障模块实现机器人的运动控制,以到达机器人安全探索的目标.同时,该系统采取机器人操作系统(Robot operating system, ROS)下的分布式结构,将整体算法合理分配到客户端和服务器,降低系统实现的硬件要求.现场实验表明,所提出方法具有良好的自主导航性能,在较复杂的室内未知环境下,仍能保持良好的地图构建能力和避障能力.     

An efficient cooperative exploration strategy for wireless sensor network

Wireless sensor networks (WSNs) are used in several applications such as healthcare devices, aerospace systems, automobile industry, security monitoring. However, WSNs have several challenges to improve the efficiency, robustness, failure tolerance and reliability of these sensors. Thus, cooperation between sensors is an important deal that increases sensor trust. Cooperative WSNs can be used to optimize the exploration of an unknown area in a distributed way. In this paper, the distributed Markovian model strategy that is used due to their past state-dependent reasoning. Moreover, the exploration strategy depends totally on the wireless communication protocol. Hence, in this paper, we propose an efficient cooperative strategy based on cognitive radio and software-defined radio which are promising technologies that increase spectral utilization and optimize the use of radio resources. We implement a distributed exploration strategy (DES) in mobile robots, and several experiments have been performed to localize targets while avoiding obstacles. Experiments were performed with several exploration robots. A comparison with another exploration strategy shows that DES improves the robots exploration.     

INDIANA: An interactive system for assisting database exploration

We propose INDIANA, a system conceived to support a novel paradigm of database exploration. INDIANA assists the users who are interested in gaining insights about a database though an interactive and incremental process, like a conversation that does not happen in natural language. During this process, the system iteratively provides the user with some features of the data that might be “interesting” from the statistical viewpoint, receiving some feedbacks that are later used by the system to refine the features provided to the user in the next step. A key ability of INDIANA is to assist “data enthusiastic” users (i.e., inexperienced or casual users) in the exploration of transactional databases in an interactive way. For this purpose, we develop a number of novel, statistically-grounded algorithms to support the interactive exploration of the database. We report an in-depth experimental evaluation to show that the proposed system guarantees a very good trade-off between accuracy and scalability, and a user study that supports the claim that the system is effective in real-world database-exploration tasks.     

SMA micro-hand implemented in small robot for generating gestures

Intelligent Service Robotics - Research on robots that can be used for communication with humans has become popular in recent years. Communication robots should ideally be as small as an infant in...     

User-defined gestures for controlling primitive motions of an end effector

In designing and developing a gesture recognition system, it is crucial to know the characteristics of a gesture selected to control, for example, an end effector of a robot arm. We conducted an experiment to collect a set of user-defined gestures and investigate characteristics of the gestures for controlling primitive motions of an end effector in human–robot collaboration. We recorded 152 gestures from 19 volunteers by presenting virtual robotic arm movements to the participants, and then asked the participants to think about and perform gestures that would cause the motions. It was found that the hands were the parts of the body used most often for gesture articulation even when the participants were holding tools and objects with both hands: a number of participants used one- and two-handed gestures interchangeably, gestures were consistently performed by the participants across all pairs of reversible gestures, and the participants expected better recognition performance for gestures that were easy to think of and perform. These findings are expected to be useful as guidelines in creating a gesture set for controlling robotic arms according to natural user behaviors.