An American Sign Language (ASL) recognition system developed based on multidimensional Hidden Markov Models (HMM) is presented in this paper. A CybergloveTM sensory glove and a Flock of Birds motion tracker are used to extract the features of ASL gestures. The data obtained from the strain gages in the glove defines the hand shape while the data from the motion tracker describes the trajectory ...

Smartphones usage among visually impaired users is growing in prominence and mobile phone providers are continuously looking for solutions to make touch screen interfaces more accessible to them. Key accessibility features for vision related impairment includes assistive screen reading applications like Voiceover in iOS or Talkback in Android which supports a variety of touch gestures for perfo...

The use of hand gesture provides an attractive alternative to cumbersome interface devices for human computer interface. In our research, we exploit multiple cues including divergence features, vorticity features and hand motion direction vector. Divergence and vorticity are derived from the optical flow for hand gesture recognition in videos. The hand tracking algorithm finds the centroids of ...

This work describes a new approach to gesture mapping in a performance with a traditional musical instrument and live electronics inspired by theories of embodied music cognition (EMC) and musical gestures. Considerations on EMC and how gestures affect the experience of music inform different mapping strategies. Our intent is to enhance the expressiveness and the liveness of performance by trac...

In this paper, we address the problem of the recognition of isolated, complex, dynamic hand gestures. The goal of this paper is to provide an empirical comparison of two state-of-the-art techniques for temporal event modeling combined with specific features on two different databases. The models proposed are the Hidden Markov Model (HMM) and Input/Output Hidden Markov Model (IOHMM), implemented...

We describe an anthropomorphic agent that is engaged in an imitation game with the human user. In imitating natural gestures demonstrated by the user, the agent brings together gesture recognition and synthesis on two levels of representation. On the mimicking level, the essential form features of the meaning-bearing gesture phase (stroke) are extracted and reproduced by the agent. Meaning-base...

We describe an anthropomorphic agent that is engaged in an imitation game with the human user In imitating natural gestures demonstrated by the user the agent brings together gesture recognition and synthesis on two levels of representation On the mimicking level the essential form features of the meaning bearing gesture phase stroke are extracted and reproduced by the agent Meaning based imita...

We introduce a modular system to generate communicative expressive gestures accompanying speech for an agent. This system is designed as a common model for different embodiments so that its processes are independent from a specific agent. There are two main features of this system. Firstly gesture expressivity is taken into account when gesture animation are computed on the fly from abstract ge...

In this study, we introduce a novel variant and application of the Collaborative Representation based Classification in spectral domain for recognition of the hand gestures using the raw surface Electromyography signals. The intuitive use of spectral features are explained via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize...

A well-known phenomenon in natural interaction is that speakers adapt their linguistic and nonverbal behaviors. Research on gestural alignment is, however, still in its early stages based on evidence from experimental settings. This paper provides a first systematic study of gesture form convergence based on a large sample of naturalistic dialogue data. We found evidence for gestural alignment,...