A Random Forest-based ensemble method for activity recognition

• Published in: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2015; pp. 5074 - 5077
• Main Authors: Feng, Zengtao, Mo, Lingfei, Li, Meng
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-01-2015
• Subjects: Accuracy; Algorithms; Classification algorithms; Feature extraction; Humans; Machine Learning; Movement; Multi-Sensor Ensemble; Pattern Recognition, Automated - methods; Physical Activity Assessment; Radio frequency; Random Forest; Training; Vegetation
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2015.7319532

This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.