Using hidden Markov models (HMM's), an unobstrusive single view camera system is developed that can recognize hand gestures, namely, a subset of American Sign Language (ASL). Previous systems have concentrated on finger spelling or isolated word recognition, often using tethered electronic gloves for input. We achieve high recognition rates for full sentence ASL using only visual cues. A forty word lexicon consisting of personal pronouns, verbs, nouns, and adjectives is used to create 494 randomly constructed five word sentences that are signed by the subject to the computer. The data is separated into a 395 sentence training set and an independent 99 sentence test set. While signing, the 2D position, orientation, and eccentricity of bounding ellipses of the hands are tracked in real time with the assistance of solidly colored gloves. Simultaneous recognition and segmentation of the resultant stream of feature vectors occurs five times faster than real time on an HP 735. With a strong ...

Ten different feature vectors are tested in a gesture recognition task which utilizes 3D data gathered in real-time from stereo video cameras, and HMMs for learning and recognition of gestures. Results indicate velocity features are superior to positional features, and partial rotational invariance is sufficient for good performance.

Modern wearable computer designs package workstation level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed the most for the handicapped: everyday mobile environments. This paper de- scribes a research effort to make a wearable computer that can recognize (with the possible goal of translat- ing) sentence level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40 word lexicon.

Abstract. The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classification. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4 % accuracy. 1

Small, body-mounted video cameras enable a different style of wearable computing interface. As processing power increases, a wearable computer can spend more time observing its user to provide serendipitous information, manage interruptions and tasks, and predict future needs without being directly commanded by the user. This paper introduces an assistant for playing the real-space game Patrol. This assistant tracks the wearer’s location and current task through computer vision techniques and without off-body infrastructure. In addition, this paper continues augmented reality research, started in 1995, for binding virtual data to physical locations. 1.

Despite the success of hidden Markov models (HMMs) and other techniques for speech recognition, there remains a wide perception in the speech research community that new ideas are needed to continue improvements in performance. This paper represents a contribution to this effort.

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The report describes the rationale, design, collection and basic statistics of the initial training and test database for the Cambridge Video Mail Retrieval (VMR) Project. This database is intended to support both training for the wordspotting processes and testing for the document searching methods using these that are being developed for the project's message retrieval task. This project is supported by DTI Grant IED4/1/5804 and SERC Grant GR/H87629. 1 Introduction This report describes the motivation, design, collection and analysis of the basic recorded speech database for the first stage of the Cambridge University (Engineering Department (CUED) and Computer Laboratory (CUCL)), and Olivetti Research Limited (ORL) research project on Video Mail Retrieval (Hopper, Sparck Jones & Young 1993). The specification and collection of this database, Database 1, formed task 1 of the overall project plan. The development of a system to automatically retrieve spoken video mail documents r...

this paper.

The Cambridge Research Laboratory was founded in 1987 to advance the state of the art in both core computing and human-computer interaction, and to use the knowledge so gained to support the Company’s corporate objectives. We believe this is best accomplished through interconnected pursuits in technology creation, advanced systems engineering, and business development. We are actively investigating scalable computing; mobile computing; vision-based human and scene sensing; speech interaction; computer-animated synthetic persona; intelligent information appliances; and the capture, coding, storage, indexing, retrieval, decoding, and rendering of multimedia data. We recognize and embrace a technology creation model which is characterized by three major phases: Freedom: The life blood of the Laboratory comes from the observations and imaginations of our research staff. It is here that challenging research problems are uncovered (through discussions with customers, through interactions with others in the Corporation, through other professional interactions, through reading, and the like) or that new ideas are born. For any such problem or idea, this phase culminates in the nucleation of a project team around a well articulated central research question and the outlining of a research plan. Focus: Once a team is formed, we aggressively pursue the creation of new technology based on