Activity Recognition Based on Smartphone and Dual-Tree Complex Wavelet Transform

Smartphone contains many multiple and powerful sensors, which establishes exciting new opportunities for human-computer interaction and data mining. Those sensors placed inside smartphone are used for phone function enhancement initially. In this work, we show how general machine learning algorithms can use labeled accelerometer data to classify motion activities when users hold a smartphone. First we establish an Android-based data collection application to gain persons´ motion data via accelerometer placed inside smartphone. Then we collect 6 different motion activities from 3 users. Lastly we use normal machine learning algorithm to classify those collected activities. Previous works only use time-domain features for classification. This leads to low accuracy since activity data contains frequency-domain and orientation information. In this paper, we propose a new method for extracting both time-domain and frequency-domain features. We use dual-tree complex wavelet transform (DT-CWT) as feature extraction tool. Then we use general machine learning algorithm tool WEKA for classification. Results show that our method performs better than other researcher´s method which only extracts time-domain feature from accelerometer data in accuracy aspect. Our algorithm acquires accuracy at 86% by using DT-CWT statistical information and orientation feature.