Due to the time-varying nature of EMG signals, designing a practical EMG-based motor decoder for robotic prosthetic hands is still a challenge. Contribution: To address this issue, we present a novel personalized and adaptive motor decoding framework, considering the main challenges in practice. The proposed framework operates based on initial model calibration and unsupervised domain adaptation during daily usage to attain optimal motion detection rate and speed by minimizing hardware requirements. Methods: Twelve subjects (8 male and 4 female) participated in this study, where we recorded EMG signals from three of their forearm muscles while performing 20 repetitions of 9 different hand motions. Results: Ignoring the sequence of data, it was observed that the motion classifier trained on data from other subjects has a significantly lower classification rate (28±0.8%) compared to a personalized model (94±4.7%). We also demonstrated that data from the same subject on a different day is not comparable (37±4.6%) and can be considered as data from another subject. In addition to the inter-subject and inter-session variability of EMG signals, our observations also indicated significant intra-session variability. This necessitates the adaptation method to be conducted within a session while preserving the sequence of data for analysis, a consideration overlooked by previous studies. According to our results, at the start of usage, the accuracy of non-adaptive and proposed adaptive models is almost identical, with an accuracy of 96.3±4.4%. However, after only 17 repetitions, the adaptive model's accuracy (88.8±7.9%) is on average 4.86% higher than the non-adaptive model (84.02±8.38%); t-test result: p* = 0.031.