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Computational Sensory Motor Systems LabJohns Hopkins University
Computation Sensory Motor-Systems Lab- Prof. Ralph Etienne-Cummings
Modeling life in silicon
Computational Sensory Motor Systems LabJohns Hopkins University
The Big Picture: Lab Motivation
Restoring function after limb amputation
Restoring locomotion after severe spinal cord injury
Developing Biomorphic Robotics
Adaptive
Biomorphic
Circuits &
Systems
Computational Sensory Motor Systems LabJohns Hopkins University
Computation Sensory Motor-Systems Lab
Ralph Etienne-Cummings’ Lab
• Towards a Spinal Neural Prosthesis Device• Decoding Individual Finger Movements Using Surface EMG
Electrodes• Normal Optical Flow Imager• Integrate-and-Fire Array Transceiver• Optimization of Neural Networks• Design of Ultrasonic Imaging Arrays for Detection of
Macular Degeneration• Precision Control Microsystems
Computational Sensory Motor Systems LabJohns Hopkins University
Towards a Spinal Neural Prosthesis Device
Jacob Vogelstein
Francesco Tenore
Computational Sensory Motor Systems LabJohns Hopkins University
Our Approach
• Previous approaches ignore CPG and focus on controlling muscles to generate locomotion
• We propose to directly control the CPG and use it to generate locomotion
• Basic idea is to recreate natural neural control loop in an external artificial device (i.e. replace tonic and phasic descending inputs to the CPG with electrical stimulation)
•SLP
•RS•Muscles
•Source: Grillner, Nat Rev Neurosci, 2003
Computational Sensory Motor Systems LabJohns Hopkins University
The Big Picture: Lab Motivation
Restoring function after limb amputation
Restoring locomotion after severe spinal cord injury
Developing Biomorphic Robotics
•Adaptive•Biomorphic
Circuits &•Systems
Computational Sensory Motor Systems LabJohns Hopkins University
Responsibilities of Locomotion Controller
1. Select Gait + specify desired motor output
- phase relationships
- joint angles
2. Activate CPG + tonic stimulation initiates locomotion - epidural spinal cord stimulation (ESCS)
- intraspinal microstimulation (ISMS)
3. Generate “Efferent Copy”
+ monitor sensorimotor state - external sensors on limbs
- internal afferent recordings
4. Control Output of CPG + phasic stimulation
(efferent copy required for precisely-timed stimuli)
- convert baseline CPG activity into functional motor output
- correct deviations
- adjust individual components
- adapt output to environment
Select gait ~ brain
Activate CPG ~ brainstem (MLR)
Efferent copy ~ efferent copy
Enforce/adapt output ~ phasic RS
Computational Sensory Motor Systems LabJohns Hopkins University
Gait Control System
12 pairs of IM electrodes: 3 each for left/right hip, knee, and ankle extensors/flexorsTwo types of sensory data were collected for each leg
Hip angle (HA) Ground reaction force (GRF)
Source: Vogelstein et al., IEEE TBioCAS, (submitted)
Spike processing back-end
Analog signal processing front-end
Computational Sensory Motor Systems LabJohns Hopkins University
Results: SiCPG Chip Controls Locomotion in a Paralyzed Cat
Source: Vogelstein et al., IEEE TBioCAS (submitted)
Computational Sensory Motor Systems LabJohns Hopkins University
Decoding Individual Finger Movements Using Surface EMG Electrodes
Francesco Tenore
Computational Sensory Motor Systems LabJohns Hopkins University
Problem
• Fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls
• Traditional control schemes typically provide 2 degrees of freedom (DoF):
• Insufficient for dexterous control of individual fingers
• Surface ElectroMyoGraphy (s-EMG) electrodes placed on the forearm and upper arm of an able bodied subject and a transradial amputee
Computational Sensory Motor Systems LabJohns Hopkins University
Implemented Solution
• Neural network based approach
• Number of electrodes (inputs) amputation level (I-V) Level I:
32 electrodes, Level V: 12 electrodes
Computational Sensory Motor Systems LabJohns Hopkins University
Results
1. High decoding accuracy:• Trained able-bodied subject,
~99%• Untrained transradial amputee, ~
90%
2. No s.s. difference in decoding accuracy between able-bodied subjects and transradial amputee
3. No s.s. difference in decoding accuracy between networks that used different number of electrodes (12-32)
Computational Sensory Motor Systems LabJohns Hopkins University
Current/Future Work
• Towards real-time control: training on rest states and movements Implementation on Virtual Integration Environment (VIE)
• Independent Component Analysis (ICA) to minimize number of electrodes by choosing the ones that most contribute to the accuracy results
Computational Sensory Motor Systems LabJohns Hopkins University
Normal Optical Flow Imager
Andre Harrison
Computational Sensory Motor Systems LabJohns Hopkins University
Normal Optical Flow Imager
Computer Vision Neuromorphic
Computational Sensory Motor Systems LabJohns Hopkins University
Normal Optical Flow Imager
• Imager that computes 2-D dense Normal Optical Flow estimates using spatio-temporal image gradients, without interfering with the imaging process
• Optical Flow is the apparent motion of the image intensity
Computational Sensory Motor Systems LabJohns Hopkins University
Normal Optical Flow Imager
Computational Sensory Motor Systems LabJohns Hopkins University
Integrate-and-Fire Array Transceiver
Fopefolu Folowosele
Computational Sensory Motor Systems LabJohns Hopkins University
Motivation
The brain is capable of processing sensory information in real time, to analyze its surroundings and prescribe appropriate action
Software models run slower than real time and are unable to interactwith the environment
Silicon designs take a few months to be fabricated, after which they are constrained by limited flexibility
Computational Sensory Motor Systems LabJohns Hopkins University
IFAT
The IFAT combines the speed of dedicated hardware with the programmability of software for studying real-time operations of cortical, large-scale neural networks
Computational Sensory Motor Systems LabJohns Hopkins University
Application: Visual Processing
Computational Sensory Motor Systems LabJohns Hopkins University
Optimization of Neural Networks
Alex Russel and Garrick Orchard
Computational Sensory Motor Systems LabJohns Hopkins University
Pre Evolution Architecture
Computational Sensory Motor Systems LabJohns Hopkins University
Evolved Hip Controller
Computational Sensory Motor Systems LabJohns Hopkins University
Evolved Knee Controller
Computational Sensory Motor Systems LabJohns Hopkins University
The Final Product
Computational Sensory Motor Systems LabJohns Hopkins University
Design of Ultrasonic Imaging Arrays the Detection ofMacular Degeneration
Clyde Clarke
Computational Sensory Motor Systems LabJohns Hopkins University
Design of Ultrasonic Imaging Arrays the Detection of Macular Degeneration
www.seewithlasik.com/.../CO0077.jpg
Computational Sensory Motor Systems LabJohns Hopkins University
Tool-tip Mounted Ultrasonic Micro-Array
C. Numerical Modeling1) Finite Element Method2) Finite Difference Method
B. Derive Equations for Wave Propagation in Vitreous and Retina
1) Scattering2) Absorption
L
xd
L
WW
yd
A. Create Models of Transducer array operating in Homogeneous Media
[Yakub,IEEE Trans 02]
D. Modify Design Parameters of Array to perform optimally in Surgical Environment
Computational Sensory Motor Systems LabJohns Hopkins University
Adaptive and Reconfigurable Microsystems for High Precision Control
Ndubuisi Ekewe
Computational Sensory Motor Systems LabJohns Hopkins University
Adaptive and Reconfigurable Microsystems for High Precision Control
laryngoscope
base link
rotating base
distal dexterity unit (DDU)
DDU for saliva suction
DDU holder
tool manipulation unit (TMU)
fast clamping device
snake drive unitelectrical supply
/data lines
laryngoscope
base link
rotating base
distal dexterity unit (DDU)
DDU for saliva suction
DDU holder
tool manipulation unit (TMU)
fast clamping device
snake drive unitelectrical supply
/data lines
DDUholder
Parallel Manipulation UnitSnake-likeunit
enddisk
ball joint
secondarybackbone
internal wire
movingplatform lock ring
spacerdisk
basedisk
centralbackbone
DDUholder
Parallel Manipulation UnitSnake-likeunit
enddisk
ball joint
secondarybackbone
internal wire
movingplatform lock ring
spacerdisk
basedisk
centralbackbone
Simaan, 2004
EncoderG1
R2
RI
VoutMotor
D/ARs
G2 Buffer
Vcontrol
Vs
Digital position
and speed
SpeedCmd
PosMeas
SpeedMeas
SPI Interface
Microprocessor
G2-value
Digital Control(PID + FF)Position,
Velocity or Torque
Motor Setup
On-chip systems
A/DMotorFeedbk
Vifb
EncoderG1
R2
RI
VoutMotor
D/ARs
G2 Buffer
Vcontrol
Vs
Digital position
and speed
SpeedCmd
PosMeas
SpeedMeas
SPI Interface
Microprocessor
G2-value
Digital Control(PID + FF)Position,
Velocity or Torque
Motor Setup
On-chip systems
A/DMotorFeedbk
Vifb
Ekekwe et al, US Patent (Pending)
102
103
104
105
100
101
102
103
104
Encoder Frequency [Hz]
Out
put
PredictedMeasured
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