uWave: Accelerometer-based personalized gesture recognition and its applications

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures. We evaluate uWave using a large gesture library with over 4000 samples for eight gesture patterns collected from eight users over one month. uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. We also present applications of uWave in gesture-based user authentication and interaction with 3D mobile user interfaces. In particular, we report a series of user studies that evaluates the feasibility and usability of lightweight user authentication. Our evaluation shows both the strength and limitations of gesture-based user authentication.     

Adaptive visual gesture recognition for human–robot interaction using a knowledge-based software platform

In human–human communication we can adapt or learn new gestures or new users using intelligence and contextual information. Achieving natural gesture-based interaction between humans and robots, the system should be adaptable to new users, gestures and robot behaviors. This paper presents an adaptive visual gesture recognition method for human–robot interaction using a knowledge-based software platform. The system is capable of recognizing users, static gestures comprised of the face and hand poses, and dynamic gestures of face in motion. The system learns new users, poses using multi-cluster approach, and combines computer vision and knowledge-based approaches in order to adapt to new users, gestures and robot behaviors. In the proposed method, a frame-based knowledge model is defined for the person-centric gesture interpretation and human–robot interaction. It is implemented using the frame-based Software Platform for Agent and Knowledge Management (SPAK). The effectiveness of this method has been demonstrated by an experimental human–robot interaction system using a humanoid robot ‘Robovie’.     

A taxonomy and notation method for three-dimensional hand gestures

Recently, studies on gesture-based interfaces have made an effort to improve the intuitiveness of gesture commands by asking users to define a gesture for a command. However, there are few methods to organize and notate user-defined gestures in a systematic approach. To resolve this, we propose a three-dimensional (3D) Hand Gesture Taxonomy and Notation Method. We first derived elements of a hand gesture by analyzing related studies and subsequently developed the 3D Hand Gesture Taxonomy based on the elements. Moreover, we devised a Notation Method based on a combination of the elements and also matched a code to each element for easy notation. Finally, we have verified the usefulness of the Notation Method by training participants to notate hand gestures and by asking another set of participants to recreate the notated gestures. In short, this research proposes a novel and systematic approach to notate hand gesture commands.     

连续交互空间下的混合手势交互模型

分析了触控交互技术在移动手持设备及可穿戴设备应用的应用现状及存在的问题;基于交互动作的时间连续性及空间连续性,提出了将触控交互动作的接触面轨迹与空间轨迹相结合,同时具有空中手势及触控手势的特性及优点的混合手势输入方法;基于连续交互空间的概念,将混合交互手势,空中手势、表面触控手势进行统一,建立了包括空中层、表面层、混合层的连续交互空间分层处理模型;给出了统一的信息数据定义及数转换流程;构建了通用性的手势识别框架,并对轨迹切分方法及手势分类识别方法进行了阐述.最后设计了应用实例,通过实验,对混合交互手势的可用性及连续空间分层处理模型的可行性进行了验证.实验表明,混合手势输入方式同时兼具了表面触控输入及空中手势输入的优点,在兼顾识别效率的同时,具有较好的空间自由度.     

Seeking common ground while reserving differences in gesture elicitation studies

Gesture elicitation studies have been frequently conducted in recent years for gesture design. However, most elicitation studies adopted the frequency ratio approach to assign top gestures derived from end-users to the corresponding target tasks, which may cause the results get caught in local minima, i.e., the gestures discovered in an elicitation study are not the best ones. In this paper, we propose a novel approach of seeking common ground while reserving differences in gesture elicitation research. To verify this point, we conducted a four-stage case study on the derivation of a user-defined mouse gesture vocabulary for web navigation and then provide new empirical evidences on our proposed method, including 1) gesture disagreement is a serious problem in elicitation studies, e.g., the chance for participants to produce the same mouse gesture for a given target task without any restriction is very low, below 0.26 on average; 2) offering a set of gesture candidates can improve consistency; and 3) benefited from the hindsight effect, some unique but highly teachable gestures produced in the elicitation study may also have a chance to be chosen as the top gestures. Finally, we discuss how these findings can be applied to inform all gesture-based interaction design.

     

Personalized gesture interactions for cyber-physical smart-home environments

A gesture-based interaction system for smart homes is a part of a complex cyber-physical environment, for which researchers and developers need to address major challenges in providing personalized gesture interactions. However, current research efforts have not tackled the problem of personalized gesture recognition that often involves user identification. To address this problem, we propose in this work a new event-driven service-oriented framework called gesture services for cyber-physical environments (GS-CPE) that extends the architecture of our previous work gesture profile for web services (GPWS). To provide user identification functionality, GS-CPE introduces a two-phase cascading gesture password recognition algorithm for gesture-based user identification using a two-phase cascading classifier with the hidden Markov model and the Golden Section Search, which achieves an accuracy rate of 96.2% with a small training dataset. To support personalized gesture interaction, an enhanced version of the Dynamic Time Warping algorithm with multiple gestural input sources and dynamic template adaptation support is implemented. Our experimental results demonstrate the performance of the algorithm can achieve an average accuracy rate of 98.5% in practical scenarios. Comparison results reveal that GS-CPE has faster response time and higher accuracy rate than other gesture interaction systems designed for smart-home environments.     

The Gesture Disagreement Problem in Free-hand Gesture Interaction

Accurately understanding a user’s intention is often essential to the success of any interactive system. An information retrieval system, for example, should address the vocabulary problem (Furnas et al., 1987) to accommodate different query terms users may choose. A system that supports natural user interaction (e.g., full-body game and immersive virtual reality) must recognize gestures that are chosen by users for an action. This article reports an experimental study on the gesture choice for tasks in three application domains. We found that the chance for users to produce the same gesture for a given task is below 0.355 on average, and offering a set of gesture candidates can improve the agreement score. We discuss the characteristics of those tasks that exhibit the gesture disagreement problem and those tasks that do not. Based on our findings, we propose some design guidelines for free-hand gesture-based interfaces.     

Analysis of 3D Hand Trajectory Gestures Using Stroke-Based Composite Hidden Markov Models

We present a glove-based hand gesture recognition system using hidden Markov models (HMMs) for recognizing the unconstrained 3D trajectory gestures of operators in a remote work environment. A Polhemus sensor attached to a PinchGlove is employed to obtain a sequence of 3D positions of a hand trajectory. The direct use of 3D data provides more naturalness in generating gestures, thereby avoiding some of the constraints usually imposed to prevent performance degradation when trajectory data are projected into a specific 2D plane. We use two kinds of HMMs according to the basic units to be modeled: gesture-based HMM and stroke-based HMM. The decomposition of gestures into more primitive strokes is quite attractive, since reversely concatenating stroke-based HMMs makes it possible to construct a new set of gesture-based HMMs. Any deterioration in performance and reliability arising from decomposition can be remedied by a fine-tuned relearning process for such composite HMMs. We also propose an efficient method of estimating a variable threshold of reliability for an HMM, which is found to be useful in rejecting unreliable patterns. In recognition experiments on 16 types of gestures defined for remote work, the fine-tuned composite HMM achieves the best performance of 96.88% recognition rate and also the highest reliability.     

基于肌肉感知的手势交互模型

在人机交互技术由以计算机演化为以人为中心的背景下,通过感知肌肉活动的手势识别方法,因其可穿戴性、隐式交互性和可靠性的特点在近几年得到了人机交互研究领域的高度关注.但目前的相关研究缺乏统一的语义分析模型和系统模型支持研究和开发.为此,分析讨论了交互手势的分类并归纳总结出适合肌肉感知方法的输入原语,提出基于肌肉感知的手势交互语义分析模型和分层处理的系统结构模型,旨在提高该类型交互应用的研究和开发工作效率.最后分析了办公室环境下的操作手势交互应用场景,给出了该语义分析模型和分层系统结构模型的应用实例.     

Head gestures for perceptual interfaces: The role of context in improving recognition

Head pose and gesture offer several conversational grounding cues and are used extensively in face-to-face interaction among people. To accurately recognize visual feedback, humans often use contextual knowledge from previous and current events to anticipate when feedback is most likely to occur. In this paper we describe how contextual information can be used to predict visual feedback and improve recognition of head gestures in human–computer interfaces. Lexical, prosodic, timing, and gesture features can be used to predict a user's visual feedback during conversational dialog with a robotic or virtual agent. In non-conversational interfaces, context features based on user–interface system events can improve detection of head gestures for dialog box confirmation or document browsing. Our user study with prototype gesture-based components indicate quantitative and qualitative benefits of gesture-based confirmation over conventional alternatives. Using a discriminative approach to contextual prediction and multi-modal integration, performance of head gesture detection was improved with context features even when the topic of the test set was significantly different than the training set.     

Adaptive mixture-of-experts models for data glove interface with multiple users

Hand gestures have great potential to act as a computer interface in the entertainment environment. However, there are two major problems when implementing the hand gesture-based interface for multiple users, the complexity problem and the personalization problem. In order to solve these problems and implement multi-user data glove interface successfully, we propose an adaptive mixture-of-experts model for data-glove based hand gesture recognition models which can solve both the problems.The proposed model consists of the mixture-of-experts used to recognize the gestures of an individual user, and a teacher network trained with the gesture data from multiple users. The mixture-of-experts model is trained with an expectation-maximization (EM) algorithm and an on-line learning rule. The model parameters are adjusted based on the feedback received from the real-time recognition of the teacher network.The model is applied to a musical performance game with the data glove (5DT Inc.) as a practical example. Comparison experiments using several representative classifiers showed both outstanding performance and adaptability of the proposed method. Usability assessment completed by the users while playing the musical performance game revealed the usefulness of the data glove interface system with the proposed method.     

A visual attention-based method to address the midas touch problem existing in gesture-based interaction

The “Midas Touch” problem has long been a difficult problem existing in gesture-based interaction. This paper proposes a visual attention-based method to address this problem from the perspective of cognitive psychology. There are three main contributions in this paper: (1) a visual attention-based parallel perception model is constructed by combining top-down and bottom-up attention, (2) a framework is proposed for dynamic gesture spotting and recognition simultaneously, and (3) a gesture toolkit is created to facilitate gesture design and development. Experimental results show that the proposed method has a good performance for both isolated and continuous gesture recognition tasks. Finally, we highlight the implications of this work for the design and development of all gesture-based applications.     

面向虚拟现实的层次化交互手势建模与理解方法

虚拟现实中的交互手势包括多种不同类型,层次化建模方法避免了采用单一模型导致效率不高的问题.识别是一个由粗到精的过程,通过滑动窗技术实时提取手势的统计特征,实现手势类别的粗略划分,然后采用不同方法对各类手势进行分析.交互环境及上下文信息用以辅助手势的类别划分,提高了识别效率.最后通过虚拟家居系统对该方法进行了验证.     

User-Defined Gestures for Gestural Interaction: Extending from Hands to Other Body Parts

Most gestural interaction studies on gesture elicitation have focused on hand gestures, and few have considered the involvement of other body parts. Moreover, most of the relevant studies used the frequency of the proposed gesture as the main index, and the participants were not familiar with the design space. In this study, we developed a gesture set that includes hand and non-hand gestures by combining the indices of gesture frequency, subjective ratings, and physiological risk ratings. We first collected candidate gestures in Experiment 1 through a user-defined method by requiring participants to perform gestures of their choice for 15 most commonly used commands, without any body part limitations. In Experiment 2, a new group of participants evaluated the representative gestures obtained in Experiment 1. We finally obtained a gesture set that included gestures made with the hands and other body parts. Three user characteristics were exhibited in this set: a preference for one-handed movements, a preference for gestures with social meaning, and a preference for dynamic gestures over static gestures.     

基于Leap Motion的动态手势识别

动态手势识别作为人机交互的一个重要方向，在各个领域具有广泛的需求。相较于静态手势，动态手势的变化更为复杂，对其特征的充分提取与描述是准确识别动态手势的关键。为了解决对动态手势特征描述不充分的问题，利用高精度的Leap Motion传感器对手部三维坐标信息进行采集，提出了一种包含手指姿势和手掌位移的特征在内的、能够充分描述复杂动态手势的特征序列，并结合长短期记忆网络模型进行动态手势识别。实验结果表明，提出的方法在包含16种动态手势的数据集上的识别准确率为98.50%；与其他特征序列的对比实验表明，提出的特征序列，能更充分准确地描述动态手势特征。     

基于光流的动态手势识别

手势识别是人机交互中的重要组成部分，文章针对基于光流PCA（主分量分析）和DTW（动态时间规整）进行命令手势识别。利用块相关算法计算光流，并通过主分量分析得到降维的投影系数，以及手掌区域的质心作为混合特征向量。针对该混合特征向量定义了新的加权距离测度，并用DTW对手势进行匹配。针对9个手势训练和识别，识别率达到92％。     

基于傅立叶描述子和HMM的手势识别

针对家庭服务机器人平台中人机交互的问题,提出基于视觉的手势识别作为人与机器人交互的方式,研究利用傅立叶描述子对手势形状进行描述,并结合支持向量机和隐马尔可夫模型分别对静态手势和动态手势进行分类,实现了静态手势和动态手势的识别。该系统基于新型传感器Kinect,在图像分割阶段结合图像深度信息,可以有效的将手势区域提取出来,在一定范围内具有较强的鲁棒性,特征提取阶段基于傅立叶描述子,使手势识别具有旋转、缩放、平移不变性。针对七种常见静态手势和四种动态手势进行测试,平均识别率分别达到98.8%和96.7%,实验结果表明该系统具有较高的准确度。     

融合关节旋转特征和指尖距离特征的手势识别

作为人机交互的重要方式,手势交互和识别由于其具有的高自由度而成为计算机图形学、虚拟现实与人机交互等领域的研究热点.传统直接提取手势轮廓或手部关节点位置信息的手势识别方法,其提取的特征通常难以准确表示手势之间的区别.针对手势识别中不同手势具有的高自由度以及由于手势图像分辨率低、背景杂乱、手被遮挡、手指形状尺寸不同、个体差异性导致手势特征表示不准确等问题,本文提出了一种新的融合关节旋转特征和指尖距离特征的手势特征表示与手势识别方法.首先从手势深度图中利用手部模板并将手部看成链段结构提取手部20个关节点的3D位置信息;然后利用手部关节点位置信息提取四元数关节旋转特征和指尖距离特征,该表示构成了手势特征的内在表示;最后利用一对一支持向量机对手势进行有效识别分类.本文不仅提出了一种新的手势特征表示与提取方法,该表示融合了关节旋转信息和指尖距离特征;而且从理论上证明了该特征表示能唯一地表征手势关节点的位置信息;同时提出了基于一对一SVM多分类策略进行手势分类与识别.对ASTAR静态手势深度图数据集中8类中国数字手势和21类美国字母手势数据集分别进行了实验验证,其分类识别准确率分别为99.71%和85.24%.实验结果表明,本文提出的基于关节旋转特征和指尖距离特征的融合特征能很好地表示不同手势的几何特征,能准确地表征静态手势并进行手势识别.