Automatic detection of falls using hybrid data processing approaches

A method for fall detection, performed by processing circuitry of a wearable device, comprising:

• receiving three-dimensional motion data of a human subject, wherein the motion data includes sensor measurements collected in a subject time period and in an earlier time period before the subject time period;
• identifying a potential fall event from the three-dimensional motion data of the human subject, the potential fall event identified based on one or more rules;
• evaluating first samples of the motion data with a machine learning model, to produce a first determination that a fall has occurred during the potential fall event, wherein the first samples correspond to the sensor measurements collected in the subject time period, and wherein use of the machine learning model is invoked in response to the potential fall event identified using the one or more rules;
• evaluating second samples of the motion data with a deep learning neural network, to produce a second determination that a fall has occurred during the potential fall event, wherein use of the deep learning neural network is invoked after the use of the machine learning model, in response to the first determination produced from the machine learning model indicating that the fall has occurred, wherein the second samples correspond to the sensor measurements collected in the subject time period and in the earlier time period;
• classifying the potential fall event as a fall condition for the human subject, based on the first determination produced from the machine learning model and the second determination produced from the deep learning neural network both indicating that the fall has occurred;
• and
• outputting data to indicate the fall condition for the human subject.

A non-transitory device-readable storage medium, the device-readable storage medium comprising instructions that, when executed by processor circuitry of an electronic device, causes the electronic device to perform operations that:

• receive three-dimensional motion data of a human subject, wherein the motion data includes sensor measurements collected in a subject time period and in an earlier time period before the subject time period;
• identify a potential fall event from the three-dimensional motion data of the human subject, the potential fall event identified based on one or more rules;
• evaluate first samples of the motion data with a machine learning model, to produce a first determination that a fall has occurred during the potential fall event, wherein the first samples correspond to the sensor measurements collected in the subject time period, and wherein use of the machine learning model is invoked in response to the potential fall event identified using the one or more rules;
• evaluate second samples of the motion data with a deep learning neural network, to produce a second determination that a fall has occurred during the potential fall event, wherein use of the deep learning neural network is invoked after the use of the machine learning model, in response to the first determination produced from the machine learning model indicating that the fall has occurred, wherein the second samples correspond to the sensor measurements collected in the subject time period and in the earlier time period;
• classify the potential fall event as a fall condition for the human subject, based on the first determination produced from the machine learning model and the second determination produced from the deep learning neural network both indicating that the fall has occurred;
• and
• output data to indicate the fall condition for the human subject.