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

arXiv: https://arxiv.org/abs/1701.03379 · HTML: https://mlsanity.com/paper/1701.03379/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [WiFi Fingerprint Clustering for Urban Mobility Analysis](https://mlsanity.com/paper/2105.01274/) (2021)
- [Clustering and Analysis of GPS Trajectory Data using Distance-based Features](https://mlsanity.com/paper/2212.00206/) (2022)

## Abstract (arXiv)

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.