A hybrid hierarchical model for accessing physical activity recognition towards free-living environments

Driven by the new revolution in healthcare, the importance of understanding Physical Activity (PA) in the uncertain, dynamic, free-living environments has been drawing growing attention. However, there is a lack of holistic investigation on how to improve the accuracy of PA recognition in free-living environments using cost-effective wearable devices with feasible algorithms. In this paper, we design a two-layer hybrid hierarchical model for accessing and evaluating cost-effective wearable intelligence approaches for PA recognition in free-living environments. The hypothesis of this model first suggests utilising less-attached on-body consumable wearables like belt and wristband devices, and then building up a PA dataset collected in free-living environments like an elderly home, hospital, office and gym. The model is then defined with components of lightGBM (LGB) and Artificial Neural Networks (ANN) for coarse and fine-grained classification, parameters of achieving high recognition rate including time window sizes, features and activation functions. The experimental results indicate that our model has superior ability over other state-of-the-art algorithms in classifying three typical types of PA (dynamic, sedentary, and transitional) with an average accuracy up to 84%. Specifically, our model performs good results of PA recognition with the ageing populations including 5 Mild Cognitive Impairment (MCI) and 17 Parkinson's disease (PD) patients.