A user identification system based on free-air hand signature-gestures acquired with a 3-D camera was developed. In the system, users interactively defined their own motion signatures by demonstration and trained the system by performing a plurality of signatures. The system identifies the user by comparing the distances of a sample to other signatures. The distance metric is learned by using neighborhood components analysis. An interactive enrollment algorithm which uses sequential clustering, and allows the system to advise the user during signature selection and system training was developed. Four validation tests were conducted: 1) all users using a single predefined signature-gesture (independent); 2) each user using a personal signature-gesture (dependent); 3) copycat tests for examining robustness against forgery; and 4) operation of the interactive enrollment system. For identifying a single user out of user cohorts of three to seven people, the independent system had average accuracies of 91%-77% depending on cohort size and signature shape. Higher average accuracies of 98%-92% were obtained for the dependent system. In the forgery tests, users with high signature variability over time were susceptible to forgeries, but users with low signature variability obtained a low equal error rate of 0.083. The interactive enrollment system significantly improved recognition accuracy. The proposed system can be integrated into gesture-based home entertainment systems and used for interface customization, content adaptation, and parental control. User attitudes toward the system within this context were assessed using the widely accepted technology acceptance model. Based on 69 responders, the results indicated a positive user attitude toward the system and a high intention to use it. The users expressed a preference for personalized gestures, a finding that indicates the importance of the interactive enrollment module for personalizing the signature-gestures.

This paper presents a technique for trajectory classification with applications to dynamic free-air hand gesture recognition. Such gestures are unencumbered and drawn in free air. Our approach is an extension to the longest common subsequence (LCS) classification algorithm. A learning preprocessing stage is performed to create a probabilistic 2-D template for each gesture, which allows taking into account different trajectory distortions with different probabilities. The modified LCS, termed the most probable LCS (MPLCS), is developed to measure the similarity between the probabilistic template and the hand gesture sample. The final decision is based on the length and probability of the extracted subsequence. Validation tests using a cohort of gesture digits from video-based capture show that the approach is promising with a recognition rate of more than 98 % for video stream preisolated digits. The MPLCS algorithm can be integrated into a gesture recognition interface to facilitate gesture character input. This can greatly enhance the usability of such interfaces.

This paper presents a generalized method for the design of a gesture vocabulary (GV) for intuitive and natural two-way human-robot dialog. Two GV design methodologies are proposed; one for a robot GV (RGV) and a second for a human GV (HGV). The design is based on motion gestures exerted from a cohort of subjects in response to a set of tasks needed to execute several robot waiter (RW)-customer dialogs. Using a RW setting as a case study, preliminary experimental results indicate the unique nature of the HGV obtained.

A gesture recognition system (GRS) is comprised of a gesture, gesture-capture device (sensor), tracking algorithm (for motion capture), feature extraction, and classification algorithm. With the impending movement toward natural communication with mechanical and software systems, it is important to examine the first apparatus that separates the human communicator and the device being controlled. Although there are numerous reviews of GRSs, a comprehensive analysis of the integration of sensors into GRSs and their impact on system performance is lacking in the professional literature. Thus, we have undertaken this effort. Determination of the sensor stimulus, context of use, and sensor platform are major preliminary design issues in GRSs. Thus, these three components form the basic structure of our taxonomy. We emphasize the relationship between these critical components and the design of the GRS in terms of its architectural functions and computational requirements. In this treatise, we consider sensors that are capable of capturing dynamic and static arm and hand gestures. Although we discuss various sensor types, our main focus is on visual sensors as we expect these to become the sensor of choice in the foreseeable future. We delineate the challenges ahead for their increased effectiveness in this application domain. We note as a special challenge, the development of sensors that take over many of the functions the GRS designer struggles with today. We believe our contribution, in this first survey on sensors for GRSs, can give valuable insights into this important research and development topic, and encourage advanced research directions and new approaches.

This paper presents a physical model developed to find the directions of forces and moments required to open a plastic bag - which forces will contribute toward opening the knot and which forces will lock it further. The analysis is part of the implementation of aQ(¿)-learning algorithm on a robot system. The learning task is to let a fixed-arm robot observe the position of a plastic bag located on a platform, grasp it, and learn how to shake out its contents in minimum time. The physical model proves that the learned optimal bag shaking policy is consistent with the physical model and shows that there were no subjective influences. Experimental results show that the learned policy actually converged to the best policy.

The use of doctor-computer interaction devices in the operation room (OR) requires new modalities that support medical imaging manipulation while allowing doctors' hands to remain sterile, supporting their focus of attention, and providing fast response times. This paper presents "Gestix," a vision-based hand gesture capture and recognition system that interprets in real-time the user's gestures for navigation and manipulation of images in an electronic medical record (EMR) database. Navigation and other gestures are translated to commands based on their temporal trajectories, through video capture. "Gestix" was tested during a brain biopsy procedure. In the in vivo experiment, this interface prevented the surgeon's focus shift and change of location while achieving a rapid intuitive reaction and easy interaction. Data from two usability tests provide insights and implications regarding human-computer interaction based on nonverbal conversational modalities.

This paper presents a collaborative reinforcement learning algorithm, CQ(lambda), designed to accelerate learning by integrating a human operator into the learning process. The CQ(lambda) -learning algorithm enables collaboration of knowledge between the robot and a human; the human, responsible for remotely monitoring the robot, suggests solutions when intervention is required. Based on its learning performance, the robot switches between fully autonomous operation, and the integration of human commands. The CQ(lambda) -learning algorithm was tested on a Motoman UP-6 fixed-arm robot required to empty the contents of a suspicious bag. Experimental results of comparing the CQ(lambda) with the standard Q(lambda), indicated the superiority of the CQ(lambda) while achieving an improvement of 21.25% in the average reward.

A global approach to hand gesture vocabulary design is proposed which includes human as well as technical design factors. The method of selecting gestures for preconceived command vocabularies has not been addressed in a systematic manner. Present methods are ad hoc. In an analytical approach technological factors of gesture recognition accuracy are easily obtained and well studied. Conversely, it is difficult to obtain measures of human centered desires (intuitiveness, comfort). These factors, being subjective, are costly and time consuming to obtain, and hence we have developed automated methods for acquisition of these data through specially designed applications. Results of the intuitiveness experiments showed when commands are presented as stimuli the gestural responses vary widely over a population of subjects. This result refutes the hypothesis that there exist universal common gestures to express user intentions or commands.

In this work, we address the issue of reconfigurability of a hand-gesture recognition system. The calibration or setup of the operational parameters of such a system is a time-consuming effort, usually performed by trial and error, and often causing system performance to suffer because of designer impatience. In this work, we suggest a methodology using a neighborhood-search algorithm for tuning system parameters. Thus, the design of hand-gesture recognition systems is transformed into an optimization problem. To test the methodology, we address the difficult problem of simultaneous calibration of the parameters of the image processing/fuzzy C-means (FCM) components of a hand-gesture recognition system. In addition, we proffer a method for supervising the FCM algorithm using linear programming and heuristic labeling. Resulting solutions exhibited fast convergence (in the order of ten iterations) to reach recognition accuracies within several percent of the optimal. Comparative performance testing using three gesture databases (BGU, American Sign Language and Gripsee), and a real-time implementation (Tele-Gest) are reported on.

This work describes a hand gesture recognition system using an optimized image processing-fuzzy C-means (FCM) algorithm. The parameters of the image processing and clustering algorithm were simultaneously found using a neighborhood parameter search routine, resulting in solutions within 1-2% of optimal. Comparison of user dependent and user independent systems, when tested with their own trainers, resulted in recognition accuracies of 98.9% and 98.2%, respectively. For experienced users, the opposite was true, testing recognition accuracies where better for user independent than user dependent systems (98.2% over 96.0%). These results are statistically significant at the .007 levels.

Abstract Since the introduction of the Chinese Postman Problem (CPP), many variations on the same theme have been developed. In this paper we examine still another variation. The arcs of the graph are partitioned and a precedence relation defined, specifying the order in which the elements of the partition have to be traversed. We first examine the conditions for a feasible solution to the problem. Next, we specify the graph properties of the precedence partition that insure a polynomial complexity solution of O(N5), where N is the number of nodes in the original graph. When the precedence relation on sets of arcs is general, we prove that the problem of finding the minimum length of feasible postman tour is NP‐complete.