2014/01/30 by Amin Rasekh, Rasekh, Amin, Chien-An Chen +4 · 1 citation
Computer Science · Engineering · #Computers and Society (cs.CY) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #Time Series Analysis and Forecasting #cs.CY #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.1401.8212
arxiv created 2014/01/30 · openalex publication_date 2014/01/30 · arxiv updated 2014/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Human activity recognition has wide applications in medical research and human survey system. In this project, we design a robust activity recognition system based on a smartphone. The system uses a 3-dimentional smartphone accelerometer as the only sensor to collect time series signals, from which 31 features are generated in both time and frequency domain. Activities are classified using 4 different passive learning methods, i.e., quadratic classifier, k-nearest neighbor algorithm, support vector machine, and artificial neural networks. Dimensionality reduction is performed through both feature extraction and subset selection. Besides passive learning, we also apply active learning algorithms to reduce data labeling expense. Experiment results show that the classification rate of passive learning reaches 84.4% and it is robust to common positions and poses of cellphone. The results of active learning on real data demonstrate a reduction of labeling labor to achieve comparable performance with passive learning.