Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-v5vhk Total loading time: 0 Render date: 2024-06-25T04:01:12.809Z Has data issue: false hasContentIssue false

23 - Bioelectronic interfaces for artificially driven human movements

from Part V - Bionics

Published online by Cambridge University Press:  05 September 2015

By
Kevin A. Mazurek  and
Ralph Etienne-Cummings
Edited by
Sandro Carrara  and
Krzysztof Iniewski
Kevin A. Mazurek
Affiliation:
John Hopkins University
Ralph Etienne-Cummings
Affiliation:
John Hopkins University
Sandro Carrara
Affiliation:
École Polytechnique Fédérale de Lausanne
Krzysztof Iniewski
Affiliation:
Redlen Technologies Inc., Canada
Get access

Summary

During the course of a normal day, there are numerous physical tasks to be accomplished that require control of one’s arms and legs. For individuals with a neurological disease, motor disorder, or physical injury, the ability to control their arms or legs may be impaired or completely removed. One rehabilitative method to restore some function of movement would be to use a bioelectronic interface to provide a communication pathway between the nervous system and either the control of an external device or improved control of the existing limb. Successful bioelectronic interfaces require efficient and robust communication between external hardware and the nervous system in order to drive human movements. An ultimate solution would be a device that could “speak” the same language as the neural network it is communicating with to produce biofidelic functionality. In the following chapters (Chapters 24–26), a description will be given of how devices such as retinal prostheses perform the task of converting visual scenes into neural stimulation patterns to restore a sense of vision. Subsequent chapters (Chapters 27–34) will cover the different technologies available for using neural recordings for brain–machine interfaces (BMI). Again, this requires decoding neural signals from the cortex to properly control objects such as a computer mouse. This chapter will describe how neural recordings can be decoded to provide control signals for producing human movements in either the arms or the legs. Depending on the application, this may involve reanimating the existing limbs through methods of functional electrical stimulation (FES) or providing control signals derived from neural recordings to activate prosthetic limbs [1–12]. This chapter will begin with a brief discussion of the general flow process involved in designing an effective bioelectronic interface for artificial movements. The next two sections will discuss examples of bioelectronic interfaces for upper arm movements (reach and grasp) and lower leg movements (locomotion).

Type
Chapter
Information
Handbook of Bioelectronics
Directly Interfacing Electronics and Biological Systems
 , pp. 281 - 293
Publisher: Cambridge University Press
Print publication year: 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Collinger, JL, Wodlinger, B, Downey, JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 2013;381(9866):557–564.CrossRefGoogle ScholarPubMed
Esquenazi, A, Talaty, M, Packel, A, Saulino, M. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil 2012;91(11):911–921.CrossRefGoogle ScholarPubMed
Farris, RJ, Quintero, HA, Goldfarb, M. Preliminary evaluation of a powered lower limb orthosis to aid walking in paraplegic individuals. Neural Systems Rehabil Eng, IEEE Trans 2011;19(6):652–659.CrossRefGoogle ScholarPubMed
Hochberg, LR, Bacher, D, Jarosiewicz, B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 2012;485(7398):372–375.CrossRefGoogle ScholarPubMed
Kuiken, TA, Li, G, Lock, BA, et al.Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA: J Am Med Assoc 2009;301(6):619–628.CrossRefGoogle ScholarPubMed
Miller, LA, Lipschutz, RD, Stubblefield, KA, et al. Control of a six degree of freedom prosthetic arm after targeted muscle reinnervation surgery. Arch Phys Med Rehabil 2008;89(11):2057–2065.CrossRefGoogle ScholarPubMed
DLR – Institute of Robotics and Mechatronics – Hand Arm System Available at: . Accessed 4/8/2013.
Revolutionizing Prosthetics Available at: . Accessed 4/8/2013.
Ekso | Ekso Bionics Available at: . Accessed 4/8/2013.
What’s “HAL” (Hybrid Assistive Limb®)? – CYBERDYNE. Available at: . Accessed 4/8/2013.
Technical – Rex Bionics Available at: . Accessed 4/8/2013.
ReWalk – Personal Available at: . Accessed 4/8/2013.
Pais-Vieira, M, Lebedev, M, Kunicki, C, et al. A brain-to-brain interface for real-time sharing of sensorimotor information. Sci Rep 2013;3, Article no. 1319.CrossRefGoogle ScholarPubMed
Weber, D, Stein, R, Everaert, D, Prochazka, A.Limb-state feedback from ensembles of simultaneously recorded dorsal root ganglion neurons. J Neural Eng 2007;4(3):S168.CrossRefGoogle ScholarPubMed
Tenore, FV, Ramos, A, Fahmy, A, et al. Decoding of individuated finger movements using surface electromyography. Biomed Eng, IEEE Trans 2009;56(5):1427–1434.CrossRefGoogle ScholarPubMed
Englehart, K, Hudgins, B. A robust, real-time control scheme for multifunction myoelectric control. Biomed Eng, IEEE Trans 2003;50(7):848–854.CrossRefGoogle ScholarPubMed
Vargas-Irwin, CE, Shakhnarovich, G, Yadollahpour, P, et al.Decoding complete reach and grasp actions from local primary motor cortex populations. J Neurosci 2010;30(29):9659–9669.CrossRefGoogle ScholarPubMed
Brogan, W.Modern Control Theory. Prentice Hall, 1985.Google Scholar
Light, C, Chappell, P, Hudgins, B, Engelhart, K. Intelligent multifunction myoelectric control of hand prostheses. J Med Eng Technol 2002;26(4):139–146.CrossRefGoogle ScholarPubMed
Cipriani, C, Zaccone, F, Micera, S, Carrozza, MC.On the shared control of an EMG-controlled prosthetic hand: analysis of user–prosthesis interaction. Robotics, IEEE Trans 2008;24(1):170–184.CrossRefGoogle Scholar
Peckham, PH, Knutson, JS. Functional electrical stimulation for neuromuscular applications. Annu Rev Biomed Eng 2005;7:327–360.CrossRefGoogle ScholarPubMed
Patriciu, A, Yoshida, K, Struijk, JJ, et al. Current density imaging and electrically induced skin burns under surface electrodes. Biomed Eng, IEEE Trans 2005;52(12):2024–2031.CrossRefGoogle ScholarPubMed
McCreery, DB, Agnew, WF, Yuen, TG, Bullara, L. Charge density and charge per phase as cofactors in neural injury induced by electrical stimulation. Biomed Eng, IEEE Trans 1990;37(10):996–1001.CrossRefGoogle ScholarPubMed
Merrill, DR, Bikson, M, Jefferys, JG. Electrical stimulation of excitable tissue: design of efficacious and safe protocols. J Neurosci Methods 2005;141(2):171–198.CrossRefGoogle ScholarPubMed
Levy, M, Mizrahi, J, Susak, Z. Recruitment, force and fatigue characteristics of quadriceps muscles of paraplegics isometrically activated by surface functional electrical stimulation. J Biomed Eng 1990;12(2):150–156.CrossRefGoogle ScholarPubMed
Grandjean, PA, Mortimer, JT. Recruitment properties of monopolar and bipolar epimysial electrodes. Ann Biomed Eng 1986;14(1):53–66.CrossRefGoogle ScholarPubMed
Kobetic, R, Marsolais, EB. Synthesis of paraplegic gait with multichannel functional neuromuscular stimulation. Rehabil Eng, IEEE Trans 1994;2(2):66–79.CrossRefGoogle Scholar
Aoyagi, Y, Mushahwar, VK, Stein, RB, Prochazka, A.Movements elicited by electrical stimulation of muscles, nerves, intermediate spinal cord, and spinal roots in anesthetized and decerebrate cats. Neural Systems Rehabil Eng, IEEE Trans 2004;12(1):1–11.CrossRefGoogle ScholarPubMed
Mushahwar, VK, Collins, DF, Prochazka, A. Spinal cord microstimulation generates functional limb movements in chronically implanted cats. Exp Neurol 2000 6;163(2):422–429.CrossRefGoogle ScholarPubMed
Loeb, GE, Peck, RA, Moore, WH, Hood, K. BION™ system for distributed neural prosthetic interfaces. Med Eng Phys 2001;23(1):9–18.CrossRefGoogle Scholar
Loeb, GE, Richmond, FJ, Baker, LL. The BION devices: injectable interfaces with peripheral nerves and muscles. Neurosurg Focus 2006;20(5):1–9.CrossRefGoogle ScholarPubMed
Hsiao, SS, Fettiplace, M, Darbandi, B. Sensory feedback for upper limb prostheses. Prog Brain Res 2011;192:69–81.CrossRefGoogle ScholarPubMed
Alderson, SW.The electric arm. In Human Limbs and Their SubstitutesMcGraw-Hill; 1954, pp. 359–408.Google Scholar
Popov, B. The bio-electrically controlled prosthesis. J Bone Joint Surg Br 1965;47:421–424.CrossRefGoogle ScholarPubMed
Jacobson, SC, Knutti, DF, Johnson, RT, Sears HH. Development of the Utah artificial arm. Biomed Eng, IEEE Trans 1982(4):249–269.CrossRefGoogle Scholar
BBC News – Bionic hand for ‘elective amputation’ patient Available at: . Accessed 4/8/2013.
Englehart, K, Hudgins, B, Parker, PA, Stevenson, M. Classification of the myoelectric signal using time-frequency based representations. Med Eng Phys 1999;21(6):431–438.CrossRefGoogle ScholarPubMed
Haykin, S.Neural networks: A comprehensive approach. IEEE Computer Society Press; 1994.Google Scholar
Fukunaga, K.Introduction to statistical pattern recognition. Academic PPress; 1990.Google Scholar
Bishop, CM.Pattern recognition and machine learning. Springer; 2006.Google Scholar
Kuiken, TA, Miller, LA, Lipschutz, RD, et al. Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study. Lancet 2007;369(9559):371–380.CrossRefGoogle Scholar
News & Views : Neurology Today Available at: . Accessed 4/8/2013.
Welch, G, Bishop, G. An introduction to the Kalman filter. University of North Carolina Technical Report. 1995.
Harvey, AC.Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press; 1991.Google Scholar
Wu, W, Gao, Y, Bienenstock, E, Donoghue, JP, Black, MJ. Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput 2006;18(1):80–118.CrossRefGoogle ScholarPubMed
Georgopoulos, AP, Schwartz, AB, Kettner, RE. Neuronal population coding of movement direction. Science 1986;233(4771):1416–1419.CrossRefGoogle ScholarPubMed
Wang, W, Chan, SS, Heldman, DA, Moran, DW. Motor cortical representation of position and velocity during reaching. J Neurophysiol 2007;97(6):4258–4270.CrossRefGoogle ScholarPubMed
Marquaridt, DW. Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics 1970;12(3):591–612.CrossRefGoogle Scholar
Paralysis Facts & Figures – Spinal Cord Injury – Paralysis Research Center Available at: . Accessed 4/8/2013.
Duysens, J, Van de Crommert, HW. Neural control of locomotion; Part 1: The central pattern generator from cats to humans. Gait Posture 1998;7(2):131–141.CrossRefGoogle Scholar
Dimitrijevic, MR, Gerasimenko, Y, Pinter, MM. Evidence for a spinal central pattern generator in humansa. Ann N Y Acad Sci 1998;860(1):360–376.CrossRefGoogle Scholar
Grillner, S, Wallen, P. Central pattern generators for locomotion, with special reference to vertebrates. Annu Rev Neurosci 1985;8(1):233–261.CrossRefGoogle ScholarPubMed
Golubitsky, M, Stewart, I, Buono, P, Collins, J. Symmetry in locomotor central pattern generators and animal gaits. Nature 1999;401(6754):693–695.CrossRefGoogle ScholarPubMed
Harris-Warrick, RM, Cohen, AH. Serotonin modulates the central pattern generator for locomotion in the isolated lamprey spinal cord. J Exp Biol 1985;116(1):27–46.Google ScholarPubMed
Grillner, S, Zangger, P. On the central generation of locomotion in the low spinal cat. Exp Brain Res 1979;34(2):241–261.CrossRefGoogle ScholarPubMed
Foot drop – MayoClinic.com Available at: . Accessed 4/8/2013.
Hausdorff, JM, Ring, H. Effects of a new radio frequency-controlled neuroprosthesis on gait symmetry and rhythmicity in patients with chronic hemiparesis. Am J Phys Med Rehabil 2008;87(1):4–13.CrossRefGoogle ScholarPubMed
Stein, RB, Chong, S, Everaert, DG et al. A multicenter trial of a footdrop stimulator controlled by a tilt sensor. Neurorehabil Neural Repair 2006;20(3):371–379.CrossRefGoogle ScholarPubMed
Chen, BX. Robots in all walks of life. The New York Times 2012 September 11.Google Scholar
Clarke, S. Bionic breakthrough: robotic suit helps paraplegics walk again. ABC News 2010; Available at: . Accessed 4/8/2013.
Quintero, HA, Farris, RJ, Goldfarb, M. AMethod for the autonomous control of lower limb exoskeletons for persons with paraplegia. J Med Devices 2012;6(4).CrossRefGoogle Scholar
BBC News. ‘Bionic’ woman Claire Lomas completes London Marathon. Available at: . Accessed 4/8/2013.
Mushahwar, VK, Jacobs, PL, Normann, RA, Triolo, RJ, Kleitman, N. New functional electrical stimulation approaches to standing and walking. J Neural Eng 2007;4(3):S181.CrossRefGoogle ScholarPubMed
Mushahwar, VK, Gillard, DM, Gauthier, MJ, Prochazka, A. Intraspinal micro stimulation generates locomotor-like and feedback-controlled movements. Neural Syst Rehabil Eng, IEEE Trans 2002;10(1):68–81.CrossRefGoogle ScholarPubMed
Barthélemy, D, Leblond, H, Provencher, J, Rossignol, S. Nonlocomotor and locomotor hindlimb responses evoked by electrical microstimulation of the lumbar cord in spinalized cats. J Neurophysiol 2006;96(6):3273–3292.CrossRefGoogle ScholarPubMed
Holinski, B, Mazurek, K, Everaert, D, et al. Restoring stepping after spinal cord injury using intraspinal microstimulation and novel control strategies, Eng Med Biol Soc, Conf Proc IEEE 2011; 5798–5801.
Mazurek, K, Holinski, B, Everaert, D, et al. Feed forward and feedback control for over-ground locomotion in anaesthetized cats. J Neural Eng 2012;9(2):026003.CrossRefGoogle ScholarPubMed
Jung, R, Brauer, EJ, Abbas, JJ. Real-time interaction between a neuromorphic electronic circuit and the spinal cord. Neural Syst Rehabil Eng, IEEE Trans 2001;9(3):319–326.CrossRefGoogle ScholarPubMed
Winter, D, Yack, H.EMG profiles during normal human walking: stride-to-stride and inter-subject variability. Electroencephalogr Clin Neurophysiol 1987;67(5):402–411.CrossRefGoogle ScholarPubMed
Huang, H, Kuiken, TA, Lipschutz, RD. A strategy for identifying locomotion modes using surface electromyography. Biomed Eng, IEEE 2009;56(1):65–73.CrossRefGoogle ScholarPubMed
Ferris, DP, Gordon, KE, Sawicki, GS, Peethambaran, A. An improved powered ankle–foot orthosis using proportional myoelectric control. Gait Posture 2006;23(4):425–428.CrossRefGoogle ScholarPubMed
Sawicki, GS, Ferris, DP. A pneumatically powered knee–ankle–foot orthosis (KAFO) with myoelectric activation and inhibition. J Neuroeng Rehabil 2009;6(1):23.CrossRefGoogle Scholar
Kessler, HH. Amputations and prosthesis. Am J Surg 1939;43(2):560–572.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×