Reconstruction and EMG-informed control, simulation and analysis of human movement for athletics: performance improvement and injury prevention.
In this paper we present methods to track and characterize human dynamic skills using motion capture and electromographic sensing. These methods are based on task-space control to obtain the joint kinematics and extract the key physiological parameters and on computed muscle control to solve the muscle force distribution problem. We also present a dynamic control and analysis framework that integrates these metrics for the purpose of reconstructing and analyzing sports motions in real-time.
Demircan
Emel
E
Mechanical Engineering Department, Stanford University, Stanford, CA 94305, USA. emeld@stanford.edu
Khatib
Oussama
O
Wheeler
Jason
J
Delp
Scott
S
D000465:Algorithms; D001265:Athletic Injuries / Q000175:diagnosis / Q000503:physiopathology / Q000517:prevention & control; D001676:Biofeedback, Psychology / Q000379:methods* / Q000502:physiology*; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009068:Movement / Q000502:physiology*; D010809:Physical Fitness / Q000502:physiology*; D013177:Sports / Q000502:physiology*; D013647:Task Performance and Analysis