Patients with transradial hand amputations have long been able to be fitted with myoelectric arm prostheses. The current state of technology offers users a range of highly developed mechanical and bionic hand prostheses with individually movable fingers. The control of such prostheses, however, is not intuitive, requires much training, and not all possible gestures can be performed. The reason for this is often cited as the number and quality of the myoelectric signals, the amount of training data, and resulting the achievable classification quality. Since the incremental improvement of our existing approach allowed only minor improvements, we experimented with new methods for classification and control. This contribution presents an integrated solution consisting of a smart sensor, individualized feature extraction, distributed classification, and inverse kinematics to control individual fingers.