Wearable sensors for upper limbs enable the use of myoelectric control systems in real environments. An important issue in the practical use of myoelectric control is how to deal with the variations of electromyograms (EMGs); the distribution of EMGs changes over days and device (electrode) positions. The amount of training data is usually limited, as the data are collected at the beginning of the system use. To compensate for the difference of EMGs over time and device placement with limited-amount training data, transfer learning can be employed. However, it was unclear how transfer learning improve the motion recognition accuracy over long-term use with varying device positions. In this paper, we evaluated transfer learning algorithms on one-month long data with three different device positions. We found that transfer learning was able to compensate for the variations over long period and also over different electrode placements, suggesting the practical efficacy of transfer learning. But there were some cases when transfer learning did not recover the original accuracy, in particular when electrodes were placed at "out-of-muscle" positions. These findings would motivate further investigations into the design of myoelectric control systems, e.g., denser electrode configurations or lifetime-long recordings.