Motion Analysis of Deadlift for Trainers With Different Levels Based on Body Sensor Network

Deadlift is one of the most common weight training exercises that can help build waist muscles. However, many trainers with less experience may not receive the expected effect or even damage themselves. To exercise in a healthier way, a portable and quantifiable deadlift evaluation system is proposed in this article. The system employs a body sensor network (BSN), including the inertial and surface electromyography (sEMG) information, to reconstruct the human motion and segment the deadlift into certain phases to realize the detailed analysis. A two-step extended Kalman filter (EKF) that can reduce the magnetic interference is presented to estimate the attitude and the accuracy of the method is validated by the OptiTrack system. We also carry out the kinematic and sEMG analysis among trainers with different levels. The experiment results reveal that our system can significantly discriminate the technical differences for those trainers, which demonstrated that the system can provide valuable information for the deadlift training.