  Falls are one of the important causes of accidental or unintentional injury
death worldwide. Therefore, this paper presents 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
Falldetection Ensemble Decision Tree (FEDT). The mobile cloud collaboration
system can be divided into three stages: 1) mobile stage: use a light-weighted
threshold method to filter out the activities of daily livings (ADLs), 2)
collaboration stage: transmit data to cloud and meanwhile extract features in
the cloud, 3) cloud stage: deploy the model trained by FEDT to give the final
detection result with the extracted features. Experiments show that the
performance of the proposed FEDT outperforms the others' over 1-3% both on
sensitivity and specificity, and more importantly, the system can provide
reliable fall detection in practical scenario.
