Speech Recognition on an FPGA Using Discrete and Continuous Hidden Markov Models
FPL '02 Proceedings of the Reconfigurable Computing Is Going Mainstream, 12th International Conference on Field-Programmable Logic and Applications
Robust tracking of human body parts for collaborative human computer interaction
Computer Vision and Image Understanding
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Applied Pattern Recognition.
A FPGA-based Viterbi algorithm implementation for speech recognition systems
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Hand gesture recognition using depth data
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
An implementation of KSSL recognizer for HCI based on post wearable PC and wireless networks
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Small gestures go a long way: how many bits per gesture do recognizers actually need?
Proceedings of the Designing Interactive Systems Conference
The impact of motion dimensionality and bit cardinality on the design of 3D gesture recognizers
International Journal of Human-Computer Studies
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A gesture recognizer based on a desktop PC, which uses existing wire/wireless communication modules, has several restrictions such as space limitations, movement limitations, and change in recognition capacity depending on the change in the background lighting conditions when obtaining a user's meaningful gesture data from images. This paper proposes an embedded gesture recognizer that uses a data glove in order to solve these problems. The proposed embedded FPGA (field-programmable gate array)-based gesture recognizer comprises an input module, a recognition module, and a display module. The input module receives the data that is transmitted from a data glove through a UART. The recognition module determines whether one set of data is accurate by performing data calculations with a checksum function after receiving the input data and comparing it to the header byte. This module also analyzes the data from 17 distinct gestures and constructs recognition models, and then it extracts the hand gesture data and compares it to the recognition models to see if the gestures match any of the 17 models. The recognition module then transmits the recognition result to the display module. The display module displays the recognition result on an LCD screen. A data glove manufactured by 5DT was used to obtain the gesture inputs. The FPGA was the XC3S1000FG676 (Xilinx Inc.) and it was designed using VHDL. The experimental results showed a 94% average recognition rate when using the FPGA-based embedded gesture recognizer and the data glove.