Research is being conducted on pneumatic physical reservoir computing that utilizes the nonlinear dynamic characteristics of pneumatic rubber artificial muscles (PAMs). In the conventional works, an athlete wearing PAMs on both legs walked on a treadmill, and experiments were conducted to estimate the leg angle and angular velocity from the obtained air pressure changes, confirming its effectiveness. In this study, we applied this technology to wrist motion state estimation. We aimed to estimate the multimodal wrist motion state, precisely the angle, angular velocity, and myoelectric potential, from air pressure data. We also developed two types of wearable devices with different PAM placements to estimate the wrist motion state and verified their estimation performance through experiments. In previous studies, walking on a treadmill was planar motion with leg movement restricted to the forward and backward directions, which was relatively simple and easy to estimate. On the other hand, the wrist is a two-degrees-of-freedom joint that can move in various directions. Estimating this motion state is expected to be relatively complicated, and this study will tackle this new challenge. The experiments confirmed that angle, angular velocity, and myoelectric potential can be estimated. We also confirmed differences in the tendency of estimation accuracy depending on the PAM placement. The findings obtained in this study are expected to serve as a stepping stone to new wrist rehabilitation in the future.