Name
Abstract | ||
|---|---|---|
In this paper, we present an energy efficient motion sensing platform for wireless body area networks that supports real-time, persistent gesture recognition through motion tracking sensors. The platform consists of multiple heterogeneous wearable sensors. The custom-built body sensors include inertial sensors, a low-power microcontroller and a 2.4 GHz radio transceiver. Signal classifiers such as Fishers linear discriminant classifiers, static neural networks, and focused time delay neural networks (fTDNN) are employed to classify the signals obtained from the wearable sensors. 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 for gesture recognition. |
Year | Venue | Field |
|---|---|---|
2017 | CBMS | Computer vision,Wireless,Wearable computer,Computer science,Sampling (signal processing),Gesture recognition,Inertial measurement unit,Artificial intelligence,Artificial neural network,Wireless sensor network,Match moving |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fahad Moiz | 1 | 1 | 1.03 |
| Walter D. Leon | 2 | 69 | 13.94 |
| Yugyung Lee | 3 | 334 | 49.97 |
| Reza Derakhshani | 4 | 166 | 21.08 |