Myoelectrical Control of Multiple Channels Based on Single Muscle Contractions

Abstract

Scientists at the University of California, Davis have developed a method to control multi-channel myoelectric signals.

Full Description

Myoelectric technology uses the electrical signals generated during muscle contractions to generate external control signals^1,2. These measured signals are amplified and processed to generate a desired response in an external device(s). Current available methods describe a one-to-one relationship in which one muscle produces electrical signals to control a single target^3-5. That is to say, current systems generate one control signal from each muscle, limiting the number of control signals available and necessitating many skin-mounted sensors on several superficial muscles6-8.

Scientists at the University of California, Davis have developed a method to produce two or more control signals based on the electrical signals obtained from a sensor(s) mounted on the skin covering a single superficial muscle. Such signals can be used to control multiple aspects of one or more devices.

Applications

• Computer interfaces
• Video game controls
• Educational equipment
• Interfaces for disabled individuals with assistive technologies such as wheelchairs and prosthetic limbs.

Features/Benefits

• Uses only one muscle to control multiple aspects of one or more devices

Related Materials

• 1. Gordon, K. E. and Ferris, D. P. Proportional Myoelectric Control of a Virtual Object to Investigate Human Efferent Control. Exp. Brain. Res. 159:478-486. 2004.
• 2. Fimbel, E. J. Lemay, M. Arguin, M. Speed-Accuracy Trade-offs in Myocontrol. Human Movement Science 25. 165–180. 2006.
• 3. Chan, A and Englehart, K. B. Continuous Myoelectric Control for Powered Prostheses Using Hidden Markov Models. IEEE Transactions on Biomedical Engineering. 52(1):121-124. 2005.
• 4. Karlsson, S. et al. Time-Frequency Analysis of Myoelectric Signals During Dynamic Contractions: A Comparative Study. IEEE transactions on Biomedical Engineering. 47(2):228-238. 2000.
• 5. Zhou, P. Rymer, W. Z. Can standard surface EMG processing parameters be used to estimate motor unit global firing rate? Journal of Neural Engineering1:99-110. 2004.
• 6. Park, S H. and Lee, S P. EMG Pattern Recognition Based on Artificial Intelligence Techniques. IEEE Transactions on Rehabilitation Engineering. 6(4): 400-405. 1998.
• 7. Chin, C. et al. Hands-Free Human Computer Interaction Via an Electromyogram-Based Classification Algorithm. International ISA Biomedical Sciences Instrumentation Symposium. 2005; 41:31-6.
• 8. Huang, C.N. et al. Application of Facial Electromyography in Computer Mouse Access for People with Disabilities. Disability and Rehabilitation, 28(4): 231 – 237. 2006.

Patent Status