Extracting Point of Interest and Classifying Environment for Low Sampling Crowd Sensing Smartphone Sensor Data

TL;DR

This paper introduces a low sampling rate framework for extracting points of interest and classifying environments in smartphone sensor data, reducing battery consumption while maintaining accuracy.

Contribution

It presents a novel framework combining stay points detection and sensor fusion for environment classification, optimized for low sampling rates.

Findings

DBSCAN outperforms other clustering algorithms in accuracy

The framework effectively reduces battery usage without sacrificing data quality

Real-world data validates the approach's effectiveness

Abstract

The advancement of smartphones with various type of sensors enabled us to harness diverse information with crowd sensing mobile application. However, traditional approaches have suffered drawbacks such as high battery consumption as a trade off to obtain high accuracy data using high sampling rate. To mitigate the battery consumption, we proposed low sampling point of interest (POI) extraction framework, which is built upon validation based stay points detection (VSPD) and sensor fusion based environment classification (SFEC). We studied various of clustering algorithm and showed that density based spatial clustering of application with noise(DBSCAN) algorithms produce most accurate result among existing methods. The SFEC model is utilized for classifying the indoor or outdoor environment of the POI clustered earlier by VSPD. Real world data are collected, bench-marked using existing clustering method to denote effectiveness of low sampling rate model in high noise spatial temporal data.