Computational Sensory Motor Systems Lab Johns Hopkins University Computation Sensory Motor-Systems...

Computational Sensory Motor Systems LabJohns Hopkins University

Computation Sensory Motor-Systems Lab- Prof. Ralph Etienne-Cummings

Modeling life in silicon


The Big Picture: Lab Motivation

Restoring function after limb amputation

Restoring locomotion after severe spinal cord injury

Developing Biomorphic Robotics

Adaptive

Biomorphic

Circuits &

Systems


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


Towards a Spinal Neural Prosthesis Device

Jacob Vogelstein

Francesco Tenore


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


The Big Picture: Lab Motivation

Restoring function after limb amputation

Restoring locomotion after severe spinal cord injury

Developing Biomorphic Robotics

•Adaptive•Biomorphic

Circuits &•Systems


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


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


Results: SiCPG Chip Controls Locomotion in a Paralyzed Cat

Source: Vogelstein et al., IEEE TBioCAS (submitted)


Decoding Individual Finger Movements Using Surface EMG Electrodes

Francesco Tenore


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


Implemented Solution

• Neural network based approach

• Number of electrodes (inputs) amputation level (I-V) Level I:

32 electrodes, Level V: 12 electrodes


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)


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


Normal Optical Flow Imager

Andre Harrison



Computer Vision Neuromorphic



• 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


Integrate-and-Fire Array Transceiver

Fopefolu Folowosele


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


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


Application: Visual Processing


Optimization of Neural Networks

Alex Russel and Garrick Orchard


Pre Evolution Architecture


Evolved Hip Controller


Evolved Knee Controller


The Final Product


Design of Ultrasonic Imaging Arrays the Detection ofMacular Degeneration

Clyde Clarke


Design of Ultrasonic Imaging Arrays the Detection of Macular Degeneration

www.seewithlasik.com/.../CO0077.jpg


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


Adaptive and Reconfigurable Microsystems for High Precision Control

Ndubuisi Ekewe


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

Computational Sensory Motor Systems Lab Johns Hopkins University Computation Sensory Motor-Systems...

Documents

Transcript of Computational Sensory Motor Systems Lab Johns Hopkins University Computation Sensory Motor-Systems...