An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band
This paper investigates a range of daily life activities and uses a random forest classifier to detect them based on wrist motions and optical heart rate. The highest accuracy was achieved with a forest of 64 trees and 13-s signal segments.