Session

Friday, June 23, 2017 - 09:00 to 18:45
A Low Bit Rate Wearable Motion Sensing Platform for Gesture Classification
Abstract: 
A body motion sensing platform is presented. The target application for this platform is gesture recognition. The platform consists of wearable sensor nodes built using off-the-shelf components. Each sensor node includes inertial sensors as well as a low- power microcontroller and a 2.4 GHz radio transceiver. Signal classifiers such as Fisher’s linear discriminant classifiers, static neural networks and focused time delay neural networks (fTDNN) are employed to classify the signals obtained from the wearable sensor nodes. It was found that at a sampling rate of 10 Hz and just 4 bits/sample, the fTDNN classifier achieves 88% classification rate. Our results show that reducing the sampling and quantization rates could be used in energy constrained sensor networks if the main objective is classification rather than signal reconstruction.
Fahad Moiz's picture
Fahad Moiz
Walter D. Leon-Salas's picture
Walter D. Leon-Salas
Reza Derakhshani's picture
Reza Derakhshani
University of Missouri-Kansas City (USA)
Yugyung Lee's picture
Yugyung Lee