BIONIC LIMBS AND THEIR CONTROLS
By :
Arya Sree.T
S7 AEI
College Of Engineering, Trivandrum
2007-11 batch

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Overview
Bionics
Prosthetics
Hybrid bionic system
Signal pathway
Invasive PNS neural interfaces
Targeted muscle reinnervation
Prosthetic proprioception
EAP as artificial muscles
Challenges posed
Conclusion




Bionics

Bi( as in ‘life’) + onics( as in ‘electronics’)


Mechanical systems that function like living organisms or parts of living organisms

Prosthetics

Powered prosthetics: deducing user intend from existing structures in motor pathway for natural control


Fundamental control problem for prosthetics:

Limb amputation level prop. to DOF needed
Limb amputation level inv prop. to control inputs
Hybrid bionic system
Artificial system + HMI
Background - Action potential
Polarization - K+ move out

Depolarization - Na+ move in

Repolarization - back to initial ion concentrations


Signal pathway
Human Machine Interfaces
Extract voluntary commands and deliver sensory feedback

Different types
EMG based control
Cortical noninvasive HMIs
Cortical invasive HMIs
Invasive PNS HMIs





Invasive PNS neural interfaces

More intuitive limb control

Sensory feedback given through peripheral nerves giving the user more control over joint position and grip force

Pulse train of varying current amplitudes sent to electrodes in pns to recreate signals conveying touch and proprioception
PNS neural interfaces (diag)
Recording and processing signals using LIFE (expt)
Targeted muscle reinnervation
Goal : to create more natural control by ‘rewiring’

Denervate an expendable muscle area
Attach motor neurons from the amputed arm to different areas of dennervated region
Control inputs to the prosthetics are taken from these nerve endings

If successful, thinking about using the limb would result in the contraction of limb
TMR (diagram)
Prosthetic proprioception

The ability to perceive position and movement of one’s own body

Done by a class of receptors called proprioceptors

Originates from receptors in muscles, joints and skin

Sensors and encoders give signals which is given as feedback


EAP as artificial muscles
Can mimic muscle to actuate biologically inspired mechanisms

Resilient, fault tolerant, noiseless, low power consumers

Two types
Electronic
Ionic


Challenges posed
Inherent complexity of the neural system

Connecting up small neurons without causing excessive damage

Every person’s brain is different, so difficult to adapt hardware

Difficulty in making complex algorithms for signal recognition

Expensive

Conclusion
Goal : restoration of sensorimotor functions for the control of artificial limbs

PNS invasive interfaces, TMR- feasible medium term solution
Adequate characteristics
Reduced secondary risk concerns
Only little training needed for the amputees
The near future

Improvement in the quality of life

Development of mind controlled systems that can work in different environments

Applications in robotics

Scope of various researches and projects

References

Improved Myoelectric Prosthesis Control Using Targeted Reinnervartion Surgery: A Case Series
By: Miller, L.A.Stubblefield, K.A. Lipschutz, R.D.Lock, B.A.Kuiken, T.A.Rehabilitation Inst. of Chicago, Chicago
This paper appears in: Neural Systems and Reinnervation Engineering, IEEE Transactions on
Issue Date: Feb 2008
Date of Current Version: 12 February 2008

Development of BIONic Muscle Spindle for Prosthetic Proprioception
By: Sachs, N.A. Loeb, G.E. Dept of Biomed. Eng., Univ. of Southern California, Los Angeles, CA
This paper appears in: Biomedical Engineering, IEEE Transactions on
Issue Date: June 2007
Date of Current Version: 21 May 2007

On the Use of Longitudinal Peripheral Interfaces for the Control of Cybernetic Hand Prostheses in Amputees
By: Micera, S. Navarro, X. Carpaneto, J. Citi, L. Tonet, O.Rossini, P.M.Carrozza, M.C. Hoffmann, K.P. Vivo, M. Yoshida, K. Dario, P.
ARTS & CRIM Labs., Pisa
Issue Date: Oct. 2008
Date of Current Version: 05 Nov 2008

The Bionic Design and Intelligent Control of Multi-Axis Artificial Leg
By: Fei Weiqi Yuan Hualong Xie
Sch. of Inf. Sci & Eng., Shenyang Univ. of Technol., Shenyang, China
Issue Date: June 2009
Date of Current Version: 14 July 2009