2 research outputs found
Intelligent Wireless Sensor Nodes for Human Footstep Sound Classification for Security Application
Sensor nodes present in a wireless sensor network (WSN) for security
surveillance applications should preferably be small, energy-efficient and
inexpensive with on-sensor computational abilities. An appropriate data
processing scheme in the sensor node can help in reducing the power dissipation
of the transceiver through compression of information to be communicated. In
this paper, authors have attempted a simulation-based study of human footstep
sound classification in natural surroundings using simple time-domain features.
We used a spiking neural network (SNN), a computationally low weight
classifier, derived from an artificial neural network (ANN), for
classification. A classification accuracy greater than 85% is achieved using an
SNN, degradation of ~5% as compared to ANN. The SNN scheme, along with the
required feature extraction scheme, can be amenable to low power sub-threshold
analog implementation. Results show that all analog implementation of the
proposed SNN scheme can achieve significant power savings over the digital
implementation of the same computing scheme and also over other conventional
digital architectures using frequency-domain feature extraction and ANN-based
classification.Comment: 12 pages, Journa