Computerized systems and methods for continuous and real-time fatigue detection based on computer vision analysis

A system comprising:

• at least one edge device comprising at least one processor, wherein the at least one processor is in communication with a non-transitory computer readable medium having software instructions stored thereon, wherein, upon execution of the software instructions, the at least one processor is configured to:
• receive a sequence of images from a digital imaging device;
• wherein the sequence of images capture a sequence of positions in a movement performed by a user;
• utilize at least one behavior determination machine learning model to generate a plurality of movement measurements for each image in the sequence of images based at least in part on:
• the sequence of positions in the sequence of images, and at least one behavior determination machine learning layer comprising a plurality of behavior determination machine learning parameters trained to ingest the sequence of images and output the plurality of movement measurements according to training on historical images;
• determine, by the at least one processor, a plurality of movement features based at least in part on the plurality of movement measurements for each image in the sequence of images;
• utilize at least one fatigue score machine learning model to generate at least one fatigue score based at least in part on:
• the plurality of movement features, and
• at least one fatigue score layer comprising a plurality of fatigue score regression parameters trained to ingest the plurality of movement features and output the at least one fatigue score according to training on movement measurements;
• wherein the at least one fatigue score is indicative of a degree of fatigue exhibited in the sequence of positions of the movement performed by the user;
• and
• update at least one activity log associated with the user based at least in part on at least one fatigue score.