  This paper presents the designing of a neural network for the classification
of Human activity. A Triaxial accelerometer sensor, housed in a chest worn
sensor unit, has been used for capturing the acceleration of the movements
associated. All the three axis acceleration data were collected at a base
station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant),
processed and classified using MATLAB. A neural network approach for
classification was used with an eye on theoretical and empirical facts. The
work shows a detailed description of the designing steps for the classification
of human body acceleration data. A 4-layer back propagation neural network,
with Levenberg-marquardt algorithm for training, showed best performance among
the other neural network training algorithms.
