Activity Level and Fall Detection Using Accelerometer Data

A processing device, comprising:

• one or more processors;
• and
• memory having stored computer-readable instructions that are executable by the one or more processors to perform operations comprising:
• obtaining measurements of a user generated by a wearable monitoring device during an observation period, the measurements including accelerometer data;
• detecting physical steps taken by the user based on peaks in the accelerometer data above a threshold;
• generating a step count based on the detected physical steps within predetermined time epochs;
• generating an activity level classification of the user for the predetermined time epochs based on the step count within a corresponding predetermined time epoch;
• and
• outputting at least one of the step count or the activity level classification for the predetermined time epochs in a user interface.

A system, comprising:

• a wearable monitoring device that is wearable by a user to detect one or more measurements of the user during an observation period, the one or more measurements including accelerometer measurements and electrical potential measurements of a heart of the user;
• and
• a computing device configured to:
• receive the one or more measurements from the wearable monitoring device;
• generate activity level classifications of the user within predetermined time epochs based on the accelerometer measurements within a corresponding predetermined time epoch;
• generate a fall prediction by processing the accelerometer measurements using a machine learning model trained to correlate patterns in the accelerometer measurements to fall events;
• and
• output the activity level classifications and the fall prediction.