research-article

Integrating neuromuscular and cyber systems for neural control of artificial legs

Authors:

Yan (Lindsay) Sun,

Xiaorong Zhang,

Fabian SierraAuthors Info & Claims

ICCPS '10: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems

Pages 129 - 138

https://doi.org/10.1145/1795194.1795213

Published: 13 April 2010 Publication History

Abstract

This paper presents a design and implementation of a cyber-physical system (CPS) for neurally controlled artificial legs. The key to the new CPS system is the neural-machine interface (NMI) that uses an embedded computer to collect and interpret electromyographic (EMG) signals from a physical system that is a leg amputee. A new deciphering algorithm, composed of an EMG pattern classifier and finite state machine (FSM), was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Our preliminary experiment on a human subject demonstrated the feasibility of our designed real-time neural-machine interface for artificial legs.

References

[1]

Herr, H. and Wilkenfeld, A. 2003. User-adaptive control of a magnetorheological prosthetic knee. Industrial Robot: An International Journal, 30, 1 (2003), 42--55.

[2]

Psonak, R. 2000. Transfemoral prosthetics. Butterworth-Heinemann Publications.

[3]

Basmajian, J. and De Luca, C. 1985. Muscles alive: their functions revealed by electromyography. Williams & Wilkins.

[4]

Parker, P. A. and Scott, R. N. 1986. Myoelectric control of prostheses. Crit Rev Biomed Eng, 13, 4 (1986), 283--310.

[5]

Englehart, K. and Hudgins, B. 2003. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng, 50, 7 (Jul 2003), 848--854.

[6]

Huang, H., Kuiken, T. A. and Lipschutz, R. D. 2009. A strategy for identifying locomotion modes using surface electromyography. IEEE Trans Biomed Eng, 56, 1 (Jan 2009), 65--73.

[7]

Xiao, W., Huang, H., Sun, Y. and Yang, Q. 2009. Promise of embedded system with GPU in artificial leg control: Enabling time-frequency feature extraction from electromyography. In Proceeding of IEEE International Conference on Engineering in Medicine and Biology Society, 1 (NM, US, Sep 3--6, 2009), 6926--6929.

[8]

Hudgins, B., Parker, P. and Scott, R. N. 1993. A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng, 40, 1 (Jan 1993), 82--94.

[9]

Guler, N. F. and Kocer, S. 2005. Classification of EMG signals using PCA and FFT. J Med Syst, 29, 3 (Jun 2005), 241--250.

Digital Library

[10]

Ajiboye, A. B. and Weir, R. F. 2005. A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control. IEEE Trans Neural Syst Rehabil Eng, 13, 3 (Sep 2005), 280--291.

[11]

Kuiken, T. A., Li, G., Lock, B. A., Lipschutz, R. D., Miller, L. A., Stubblefield, K. A. and Englehart, K. B. 2009. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. Jama, 301, 6 (Feb 11 2009), 619--628.

[12]

Gill, A. 1962. Introduction to the theory of finite-state machines. McGraw-Hill, New York.

[13]

Yang, Y., Sun, Y., Kay, S. and Yang, Q. 2009. Securing Rating Aggregation Systems using Statistical Detectors and Trust. IEEE Transactions on Information Forensics and Security, 4, 4 (2009), 883--898.

Digital Library

[14]

Page, E. S. 1954. Continuous Inspection Scheme. Biometrika 41, 1/2 (1954), 100--115.

[15]

Philips, T., Yashchin, E. and Stein, D. 2003. Using Statistical Process Control to Monitor Active Managers. Journal of Portfolio Management 30, 1 (2003), 186--191.

[16]

Sun, Y., Yu, W., Han, Z. and Liu, R. 2006. Information Theoretic Framework of Trust Modeling and Evaluation for Ad Hoc Networks. IEEE JSAC special issue on security in wireless ad hoc networks, 24, 2 (2006) 305--317.

Digital Library

[17]

AMD ATI Mobility Radeon#8482; HD 4800 Series Graphics. http://ati.amd.com/products/mobilityradeonhd4800/.

[18]

NVIDIA Corporation, CUDA: compute unified device architecture programming guide. www.nividia.com.

[19]

Seiler, L., Carmean, D., Sprangle, E., Forsyth, T., Abrash, M., Dubey, P., Junkins, S., Lake, A., Sugerman, J., Cavin, R., Espasa, R., Grochowski, E., Juan, T. and Hanrahan, P. Larrabee: A Many-Core x86 Architecture for Visual Computing. http://download.intel.com/technology/architecture-silicon/Siggraph_Larrabee_paper.pdf.

[20]

Quantum3D introduces industry-leading graphics accelerator XMC: SENTIRIS, 5140. http://www.quantum3d.com.

[21]

Commike, A. Maximizing GPGPU computing for embedded systems. Military Embedded Systems, http://www.milembedded.com/articles/id/?37422009.

[22]

Perotto, A. O., Delagi, E. F., Iazzetti, J. and Morrison, D. 2005. Anatomical Guide For The Electromyographer: The Limbs And Trunk. Charles C Thomas Pub Ltd.

[23]

Yang, Y., Sun, Y., Kay, S. and Yang, Q. 2009. Defending Online Reputation Systems against Collaborative Unfair Raters through Signal Modeling and Trust. In Proceeding of The 24th ACM Symposium on Applied Computing (Hawaii, US, Mar 8--12, 2009).

Digital Library

[24]

Jin, D., Yang, J., Zhang, R., Wang, R. and Zhang, J. 2006. Terrain Identification for Prosthetic Knees Based on Electromyographic Signal Features. Tsinghua Science and Technology, 11, 1 (2006), 74--79.

[25]

Gambetta, D. 2000. Can we trust trust? University of Oxford.

[26]

Josang, A., Ismail, R. and Boyd, C. 2007. A survey of trust and reputation systems for online service provision. Decision Support Systems, 43, 2 (2007) 618--644.

Digital Library

[27]

Owens, D. J., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A. E. and Purcell, T. J. 2007. A Survey of General-Purpose Computation on Graphics Hardware. Computer Graphics Forum, 26, 1 (2007), 80--113.

[28]

Silberstein, M., Schuster, A., Geiger, D., Patney, A. and Owens, D. J. 2008. Efficient Computation of Sum-products on GPUs Through Software-Managed Cache.

[29]

Houston, M. General Purpose Computation on Graphics Processors (GPGPU). http://www-graphics.stanford.edu/~mhouston/public_talks/R520-mhouston.pdf.

[30]

Segal, M. and Peercy, M. A performance-oriented data parallel virtual machine for GPUs. http://ati.amd.com/developer/siggraph06/dpvm_e.pdf.

[31]

Volkov, V. and Demmel, J. W. 2008. Benchmarking GPUs to tune dense linear algebra. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, (TX, US, Nov 15--21, 2008)

Digital Library

Cited By

Ahkami BAhmed KThesleff AHargrove LOrtiz-Catalan M(2023)Electromyography-Based Control of Lower Limb Prostheses: A Systematic ReviewIEEE Transactions on Medical Robotics and Bionics10.1109/TMRB.2023.32823255:3(547-562)Online publication date: Aug-2023
https://doi.org/10.1109/TMRB.2023.3282325
Korki MJin JTian Y(2022)Real-Time Cyber-Physical Systems: State-of-the-Art and Future TrendsHandbook of Real-Time Computing10.1007/978-981-4585-87-3_37-2(1-32)Online publication date: 4-Mar-2022
https://doi.org/10.1007/978-981-4585-87-3_37-2
Korki MJin JTian Y(2022)Real-Time Cyber-Physical Systems: State-of-the-Art and Future TrendsHandbook of Real-Time Computing10.1007/978-981-4585-87-3_37-1(1-32)Online publication date: 17-Feb-2022
https://doi.org/10.1007/978-981-4585-87-3_37-1
Show More Cited By

Index Terms

Integrating neuromuscular and cyber systems for neural control of artificial legs
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics

Recommendations

Implementing an FPGA system for real-time intent recognition for prosthetic legs
DAC '12: Proceedings of the 49th Annual Design Automation Conference

This paper presents the design and implementation of a cyber physical system (CPS) for neural-machine interface (NMI) that continuously senses signals from a human neuromuscular control system and recognizes the user's intended locomotion modes in real-...
sEMG-based continuous estimation of joint angles of human legs by using BP neural network

In this paper, we propose an mth order nonlinear model to describe the relationship between the surface electromyography (sEMG) signals and the joint angles of human legs, in which a simple BP neural network is built for the model estimation. The inputs ...
The Ankle Mimicking Prosthetic Foot 3Locking mechanisms, actuator design, control and experiments with an amputee

The Ankle Mimicking Prosthetic Foot 3 is an energy efficient bionic foot using the principle of optimal power distribution. The main challenge behind this research is focused on retrieving as much energy as possible from the gait and to incorporate an ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICCPS '10: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems

April 2010

208 pages

ISBN:9781450300667

DOI:10.1145/1795194

Conference Chairs:
Janos Sztipanovits,
Raj Rajkumar

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

IEEE-CS\TCRT: TC on Real-Time Systems
SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

ICCPS '10

Sponsor:

IEEE-CS\TCRT
SIGBED

ICCPS '10: ACM/IEEE 1st International Conference on Cyber-Physical Systems

April 13 - 15, 2010

Stockholm, Sweden

Acceptance Rates

Overall Acceptance Rate 25 of 91 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

24
Total Citations
View Citations
416
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahkami BAhmed KThesleff AHargrove LOrtiz-Catalan M(2023)Electromyography-Based Control of Lower Limb Prostheses: A Systematic ReviewIEEE Transactions on Medical Robotics and Bionics10.1109/TMRB.2023.32823255:3(547-562)Online publication date: Aug-2023
https://doi.org/10.1109/TMRB.2023.3282325
Korki MJin JTian Y(2022)Real-Time Cyber-Physical Systems: State-of-the-Art and Future TrendsHandbook of Real-Time Computing10.1007/978-981-4585-87-3_37-2(1-32)Online publication date: 4-Mar-2022
https://doi.org/10.1007/978-981-4585-87-3_37-2
Korki MJin JTian Y(2022)Real-Time Cyber-Physical Systems: State-of-the-Art and Future TrendsHandbook of Real-Time Computing10.1007/978-981-4585-87-3_37-1(1-32)Online publication date: 17-Feb-2022
https://doi.org/10.1007/978-981-4585-87-3_37-1
Korki MJin JTian Y(2022)Real-Time Cyber-physical Systems: State-of-the-Art and Future TrendsHandbook of Real-Time Computing10.1007/978-981-287-251-7_37(509-540)Online publication date: 9-Aug-2022
https://doi.org/10.1007/978-981-287-251-7_37
Lara-Barrios CFormento PLarrosa EBlanco-Ortega A(2020)Evaluation of the offline classification error of human locomotion modes using virtual force-sensing resistor data2020 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)10.1109/ICMEAE51770.2020.00035(161-168)Online publication date: Nov-2020
https://doi.org/10.1109/ICMEAE51770.2020.00035
Gatouillat ABadr YMassot BSejdic E(2018)Internet of Medical Things: A Review of Recent Contributions Dealing With Cyber-Physical Systems in MedicineIEEE Internet of Things Journal10.1109/JIOT.2018.28490145:5(3810-3822)Online publication date: Oct-2018
https://doi.org/10.1109/JIOT.2018.2849014
Liu MZhang FHuang H(2017)An Adaptive Classification Strategy for Reliable Locomotion Mode RecognitionSensors10.3390/s1709202017:9(2020)Online publication date: 4-Sep-2017
https://doi.org/10.3390/s17092020
D'Auria DPersia F(2017)A Collaborative Robotic Cyber Physical System for Surgery Applications2017 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2017.84(79-83)Online publication date: Aug-2017
https://doi.org/10.1109/IRI.2017.84
Bordel BAlcarria RRobles TMartn D(2017)Cyberphysical systemsPervasive and Mobile Computing10.1016/j.pmcj.2017.06.01140:C(156-184)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.06.011
Ren ZQi XZhou GWang HNguyen D(2016)Throughput Assurance for Multiple Body Sensor NetworksIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.240861127:2(546-557)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1109/TPDS.2015.2408611
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents