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.
Finger Motion Classification by Forearm Skin Surface Vibration Signals