Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition

TL;DR

This paper introduces an entropy-based decision fusion method combining multiple classifiers to enhance human activity recognition accuracy from smartphone sensor data, validated on benchmark datasets.

Contribution

It proposes a novel fusion approach using Tsallis entropy to integrate CNN, R-CNN, and SVM classifiers for improved activity recognition.

Findings

Recognition performance is comparable to existing methods.

The fusion approach effectively combines multiple classifiers.

Experiments conducted on UCI-HAR and WISDM datasets.

Abstract

Human activity recognition serves an important part in building continuous behavioral monitoring systems, which are deployable for visual surveillance, patient rehabilitation, gaming, and even personally inclined smart homes. This paper demonstrates our efforts to develop a collaborative decision fusion mechanism for integrating the predicted scores from multiple learning algorithms trained on smartphone sensor based human activity data. We present an approach for fusing convolutional neural network, recurrent convolutional network, and support vector machine by computing and fusing the relative weighted scores from each classifier based on Tsallis entropy to improve human activity recognition performance. To assess the suitability of this approach, experiments are conducted on two benchmark datasets, UCI-HAR and WISDM. The recognition results attained using the proposed approach are comparable to existing methods.