Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms.

Purpose: Machine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.

Methods: The hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets.