title: Finger motion classification by forearm skin surface vibration signals.
abstract: 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.
authors.affiliation: Department of Medical System Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan.
authors.forename: Wenwei
authors.initials: W
authors.lastname: Yu
authors.forename: Toshiharu
authors.initials: T
authors.lastname: Kishi
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.forename: Yuse
authors.initials: Y
authors.lastname: Horiuchi
authors.forename: Jose
authors.initials: J
authors.lastname: Gonzalez