Affordances and Feedback in Nuance-Oriented Interfaces

Virtual Environments (VEs) and perceptive user interfaces must deal with complex users and their modes of interaction. One way to approach this problem is to recognize users’ nuances (subtle conscious or unconscious actions). In exploring nuance-oriented interfaces, we attempted to let users work as they preferred without being biased by feedback or affordances in the system. The hope was that we would discover the users’ innate models of interaction. The results of two user studies were that users are guided not by any innate model but by affordances and feedback in the interface. So, without this guidance, even the most obvious and useful components of an interface will be ignored

Prosody Based Co-analysis for Continuous Recognition of Coverbal Gestures

Although speech and gesture recognition has been studied extensively, all the successful attempts of combining them in the unified framework were semantically motivated, e.g., keyword-gesture cooccurrence. Such formulations inherited the complexity of natural language processing. This paper presents a Bayesian formulation that uses a phenomenon of gesture and speech articulation for improving accuracy of automatic recognition of continuous coverbal gestures. The prosodic features from the speech signal were coanalyzed with the visual signal to learn the prior probability of co-occurrence of the prominent spoken segments with the particular kinematical phases of gestures. It was found that the above co-analysis helps in detecting and disambiguating visually small gestures, which subsequently improves the rate of continuous gesture recognition. The efficacy of the proposed approach was demonstrated on a large database collected from the weather channel broadcast. This formulation opens new avenues for bottom-up frameworks of multimodal integration.Comment: Alternative see: http://vision.cse.psu.edu/kettebek/academ/publications.ht

Toward natural interaction in the real world: real-time gesture recognition

Using a new hand tracking technology capable of tracking 3D hand postures in real-time, we developed a recognition system for continuous natural gestures. By natural gestures, we mean those encountered in spontaneous interaction, rather than a set of artificial gestures chosen to simplify recognition. To date we have achieved 95.6% accuracy on isolated gesture recognition, and 73% recognition rate on continuous gesture recognition, with data from 3 users and twelve gesture classes. We connected our gesture recognition system to Google Earth, enabling real time gestural control of a 3D map. We describe the challenges of signal accuracy and signal interpretation presented by working in a real-world environment, and detail how we overcame them.National Science Foundation (U.S.) (award IIS-1018055)Pfizer Inc.Foxconn Technolog

Semi-automation of gesture annotation by machine learning and human collaboration

none6siGesture and multimodal communication researchers typically annotate video data manually, even though this can be a very time-consuming task. In the present work, a method to detect gestures is proposed as a fundamental step towards a semi-automatic gesture annotation tool. The proposed method can be applied to RGB videos and requires annotations of part of a video as input. The technique deploys a pose estimation method and active learning. In the experiment, it is shown that if about 27% of the video is annotated, the remaining parts of the video can be annotated automatically with an F-score of at least 0.85. Users can run this tool with a small number of annotations first. If the predicted annotations for the remainder of the video are not satisfactory, users can add further annotations and run the tool again. The code has been released so that other researchers and practitioners can use the results of this research. This tool has been confirmed to work in conjunction with ELAN.openIenaga, Naoto; Cravotta, Alice; Terayama, Kei; Scotney, Bryan W.; Saito, Hideo; Busà, M. GraziaIenaga, Naoto; Cravotta, Alice; Terayama, Kei; Scotney, Bryan W.; Saito, Hideo; Busà, M. Grazi

Prosody and Kinesics Based Co-analysis Towards Continuous Gesture Recognition

The aim of this study is to develop a multimodal co-analysis framework for continuous gesture recognition by exploiting prosodic and kinesics manifestation of natural communication. Using this framework, a co-analysis pattern between correlating components is obtained. The co-analysis pattern is clustered using K-means clustering to determine how well the pattern distinguishes the gestures. Features of the proposed approach that differentiate it from the other models are its less susceptibility to idiosyncrasies, its scalability, and simplicity. The experiment was performed on Multimodal Annotated Gesture Corpus (MAGEC) that we created for research on understanding non-verbal communication community, particularly the gestures

Toward an intelligent multimodal interface for natural interaction

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 73-76).Advances in technology are enabling novel approaches to human-computer interaction (HCI) in a wide variety of devices and settings (e.g., the Microsoft® Surface, the Nintendo® Wii, iPhone®, etc.). While many of these devices have been commercially successful, the use of multimodal interaction technology is still not well understood from a more principled system design or cognitive science perspective. The long-term goal of our research is to build an intelligent multimodal interface for natural interaction that can serve as a testbed for enabling the formulation of a more principled system design framework for multimodal HCI. This thesis focuses on the gesture input modality. Using a new hand tracking technology capable of tracking 3D hand postures in real-time, we developed a recognition system for continuous natural gestures. By nature gestures, we mean the ones encountered in spontaneous interaction, rather than a set of artificial gestures designed for the convenience of recognition. To date we have achieved 96% accuracy on isolated gesture recognition, and 74% correct rate on continuous gesture recognition with data from different users and twelve gesture classes. We are able to connect the gesture recognition system with Google Earth, enabling gestural control of a 3D map. In particular, users can do 3D tilting of the map using non touch-based gesture which is more intuitive than touch-based ones. We also did an exploratory user study to observe natural behavior under a urban search and rescue scenario with a large tabletop display. The qualitative results from the study provides us with good starting points for understanding how users naturally gesture, and how to integrate different modalities. This thesis has set the stage for further development towards our long-term goal.by Ying Yin.S.M