High Accuracy Human Activity Monitoring using Neural network

TL;DR

This paper develops a neural network-based system for classifying human activities using accelerometer data collected via wireless sensors, demonstrating high accuracy in activity recognition.

Contribution

It introduces a 4-layer back propagation neural network with Levenberg-Marquardt training for effective human activity classification.

Findings

Neural network achieved high classification accuracy.

Levenberg-Marquardt algorithm outperformed other training methods.

Wireless accelerometer data effectively used for activity monitoring.

Abstract

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.