Surface EMG (sEMG) signals are useful for estimating the motion or exercise of users. Wireless-type sensor electrodes, which are placed on multiple parts of the body and send the measured signals to a server, have recently become commercially available. With many estimation algorithms, the relationships between the sensor IDs and the body parts they are placed on (ID configuration) are expected to be fixed between the calibration and estimation phases. If the ID configuration is changed after the calibration phase, the estimation accuracy tends to dramatically decrease. Since it is inconvenient for users to check the ID configuration every time, we developed a method to correct the electrode ID configuration on the basis of the distribution of sEMG features. Using open data, we investigated the feasibility of our method by shuffling the order of sEMG signals. The results showed that the method was able to correct the ID configuration and restore the estimation accuracy to close to that of the calibration.