Automatic detection of falls using hybrid data processing approaches

Abstract

Various approaches for automated fall detection, implemented in wearables and other device form factors of a personal emergency response system (PERS) are disclosed. In an example, a hybrid approach for fall detection includes: identifying a potential fall event from three-dimensional motion data of a human subject, using filtering rules; evaluating the motion data with a machine learning model (e.g., a decision tree ensemble (DTE) model), to produce a first determination that a fall has occurred; evaluating the motion data with a deep learning neural network (e.g., recurrent neural network such as a gated recurrent unit (GRU)), to produce a second determination that a fall has occurred; classifying the potential fall event as a fall condition for the human subject, based on the first determination and the second determination; and outputting data to indicate the fall condition for the human subject.