A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning
Tong Wu, Yang Gu, Yiqiang Chen, Jiwei Wang, Siyu Zhang · 2020
Falls are one of the major causes of accidental or unintentional injury death worldwide. Therefore, this paper proposes a reliable fall detection algorithm and a mobile cloud collaboration system for fall detection. The algorithm is an ensemble learning method based on decision tree, named Fall-detection Ensemble Decision Tree (FEDT). The mobile cloud collaboration system is composed of three stages: 1) mobile stage: a light-weighted threshold method is used to filter out activities of daily livings (ADLs), 2) collaboration stage: TCP protocol is used to transmit data to cloud and meanwhile features are extracted in the cloud, 3) cloud stage: the model trained by FEDT is deployed to give the final detection result with the extracted features. Experiments show that the proposed FEDT outperforms the others' over 1-3% both on sensitivity and specificity and has superior robustness on different devices.