Finger Motion Classification by Forearm Skin Surface Vibration Signals

The development of prosthetic hand systems with both decoration and motion functionality for hand amputees has attracted wide research interests. Motion-related myoelectric potentials measured from the surface of upper part of forearms were mostly employed to construct the interface between amputees and prosthesis. However, finger motions, which play a major role in dexterous hand activities, could not be recognized from surface EMG (Electromyogram) signals. The basic idea of this study is to use motion-related surface vibration, to detect independent finger motions without using EMG signals. In this research, accelerometers were used in a finger tapping experiment to collect the finger motion related mechanical vibration patterns. Since the basic properties of the signals are unknown, a norm based, a correlation coefficient based, and a power spectrum based method were applied to the signals for feature extraction. The extracted features were then fed to back-propagation neural networks to classify for different finger motions. The results showed that, the finger motion identification is possible by using the neural networks to recognize vibration patterns.