Interval Estimation of Motion Intensity Variation using the Improved Inception-V3 Model

In the process of acute resistance exercise, repeated variation in motion intensity can lead to muscle fatigue and heart failure. Therefore, acquiring the interval of motion intensity variation in time the training pattern and effect can be improved by acquiring the interval of motion intensity variation. In order to achieve this goal, an improved Inception-V3 model is proposed for motion intensity variation interval estimation. The MIVIE(Motion Intensity Variation Interval Estimation) dataset consisting of Strong, Moderate, Weak groups achieve centralized and uninterrupted collection. Then, the multi-modal fusion vectors of time-frequency eigenvalues are stacked up to $227\times227$ grayscale images fed into improved inception-CNN. Finally, the manipulator’s trajectory optimization is completed under the guidance of ATO-DQN (Adaptive Trajectory Optimization-Deep Q Network) algorithm based on the motion intensity interval estimation. This work can improve the non-stationary effect of motor speed caused by changes in motion intensity during rehabilitation, which can better guarantee the safety of patients.