  In order to detect and correct physical exercises, a Grow-When-Required
Network (GWR) with recurrent connections, episodic memory and a novel subnode
mechanism is developed in order to learn spatiotemporal relationships of body
movements and poses. Once an exercise is performed, the information of pose and
movement per frame is stored in the GWR. For every frame, the current pose and
motion pair is compared against a predicted output of the GWR, allowing for
feedback not only on the pose but also on the velocity of the motion. In a
practical scenario, a physical exercise is performed by an expert like a
physiotherapist and then used as a reference for a humanoid robot like Pepper
to give feedback on a patient's execution of the same exercise. This approach,
however, comes with two challenges. First, the distance from the humanoid robot
and the position of the user in the camera's view of the humanoid robot have to
be considered by the GWR as well, requiring a robustness against the user's
positioning in the field of view of the humanoid robot. Second, since both the
pose and motion are dependent on the body measurements of the original
performer, the expert's exercise cannot be easily used as a reference. This
paper tackles the first challenge by designing an architecture that allows for
tolerances in translation and rotations regarding the center of the field of
view. For the second challenge, we allow the GWR to grow online on incremental
data. For evaluation, we created a novel exercise dataset with virtual avatars
called the Virtual-Squat dataset. Overall, we claim that our novel architecture
based on the GWR can use a learned exercise reference for different body
variations through continual online learning, while preventing catastrophic
forgetting, enabling for an engaging long-term human-robot interaction with a
humanoid robot.
