To improve the initial accuracy of wearable sensor-driven human interfaces, inter-subject variabilities must be reduced through transfer learning. If subject transfer can be performed without labeling the target user’s calibration data, an interface that provides stable accuracy can be easily achieved without a cumbersome calibration protocol. Herein, we propose a subject-transfer framework based on multiple distance measures that enables subject transfer using only unlabeled calibration data by minimizing the distance between the data distributions of the target and the source. To assess the performance of this framework, we used two surface electromyogram databases (one private database and one public database called the NinaPro database 5) acquired from the same wearable sensor, the Myo Gesture Control Armband. The proposed framework improved the pattern recognition accuracy compared with well-established classifiers constructed from randomly selected source subject data. In the future, we will apply this framework to online human interfaces that are not based on a specific calibration protocol. The scripts used in this study can be downloaded from https://github.com/aistairc/Unlabeled_STM.