Augmented Cognition Methods And Apparatus For Contemporaneous Feedback In Psychomotor Learning

Abstract

A method of creating a scalable dynamic jointed skeleton (DJS) model for enhancing psychomotor leaning using augmented cognition methods realized by an artificial intelligence (AI) engine or image processor. The method involves extracting a DJS model from either live motion images of video files of an athlete, teacher, or expert to create a scalable reference model for using in training, whereby the AI engine extracts physical attributes of the subject including arm length, length, torso length as well as capturing successive movements of a motor skill such as swinging a gold club including position, stance, club position, swing velocity and acceleration, twisting, and more.

1 . A method to teach psychomotor skills to a live athlete or student comprising a camera, an image processor, a dynamic jointed skeleton reference model and a display device visible by the live athlete during practice, whereby;
 the live athlete's movements are captured by a camera in real time as a succession of video frames and filtered to remove superfluous detail;   the image processor analyzes the live athlete's relevant physical attributes from the captured video frame images then scales the dimensions of the dynamic jointed skeleton to best match the live athlete's body dimensions;   the scaled dynamic jointed skeletal model generates images of a jointed skeleton as a motion sequence;   the generated images of the jointed skeleton model are dynamically overlaid onto the live athlete's image to create a composite video image containing both live and generated image content;   and where the composite image is delivered to a video display unit for the live athlete to observe thereby delivering a real-time visual comparison of the athlete's position and movements to that of the skeleton.