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.

[1]  Wenwei Yu,et al.  Finger Motion Recognition by Skin Surface Vibration Patterns , 2007 .

[2]  Shin-Ki Kim,et al.  Control of multifunction myoelectric hand using a real-time EMG pattern recognition , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  M. Yoshikawa,et al.  Real-Time Hand Motion Estimation Using EMG Signals with Support Vector Machines , 2006, 2006 SICE-ICASE International Joint Conference.

[4]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[5]  Akira Hiraiwa,et al.  EMG (Electromyogram) Signal Recognition With a Neural Network model , 1992 .

[6]  Satoshi Nakamura,et al.  Finger Motion Recognition from Surface-EMG using k-NN method , 2007 .

[7]  Dinesh Kant Kumar,et al.  Classification of low-level finger contraction from single channel surface EMG , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Wenwei Yu,et al.  On-line supervising mechanism for learning data in surface electromyogram motion classifiers , 2002, Systems and Computers in Japan.

[9]  Hiroshi Yokoi,et al.  Adaptable EMG Prosthetic Hand using On-line Learning Method -Investigation of Mutual Adaptation between Human and Adaptable Machine , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.