The flexibility of body joints plays an important role in daily life, particularly when performing high-precision rapid pose switching. Importantly, understanding the characteristics of human joint movement is necessary for constructing robotic joints with the softness of humanoid joints. A novel method for estimating continuous motion and time-varying stiffness of the human elbow joint was proposed in the current study, which was based on surface electromyography (sEMG). We used the Hill-based muscle model (HMM) to establish a continuous motion estimation model (CMEM) of the elbow joint, and the genetic algorithm (GA) was used to optimize unknown parameters. Muscle short-range stiffness (SRS) was then used to characterize muscle stiffness, and a joint kinetic equation was used to express the relationship between skeletal muscle stiffness and elbow joint stiffness. Finally, we established a time-varying stiffness estimation model (TVSEM) of the elbow joint based on the CMEM. In addition, five subjects were tested to verify the performance of the CMEM and TVSEM. The total average root-mean-square errors (RMSEs) of the CMEM with the optimal trials were 0.19[Formula: see text]rad and 0.21[Formula: see text]rad and the repeated trials were 0.24[Formula: see text]rad and 0.25[Formula: see text]rad, with 1.25-kg and 2.5[Formula: see text]kg-loads, respectively. The values of elbow joint stiffness ranged from 0–40[Formula: see text]Nm/rad for different muscle activities, which were estimated by the TVSEM.
Analysis of stochastic gradient tracking of time-varying polynomial Wiener systems
