This invention discloses a real-time
piano playing
posture correction system based on hand shape recognition, specifically in the field of music-assisted teaching technology. It addresses the problems of joint positioning loss and inaccurate dynamic force assessment under
occlusion during playing. First, a primary contour is generated by fusing
visual flow entities and reflection edges to construct a key mapping mesh. Then,
inverse kinematics deduction is performed using skeletal proportion constraints to reconstruct the three-dimensional skeleton sequence under
occlusion. Next, focusing on the key sinking time window, the
vertical displacement gradient of the metacarpophalangeal joints and the rate of change of
interphalangeal joint angles are compared to quantify joint motion values and construct a force deformation
feature vector. The extracted feature vectors are input into a standardized
database for comparison, and posture error labels are parsed out. Finally, graphic and synchronous speech correction signals are generated at rests or long notes in the electronic
score, constructing a closed-loop
correction system that does not interrupt the playing
rhythm, providing scientific support for
piano posture-assisted teaching.