Tailoring Machine Learning to Textile-embedded Sensors Matthew - - PowerPoint PPT Presentation

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Tailoring Machine Learning to Textile-embedded Sensors Matthew - - PowerPoint PPT Presentation

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Tailoring Machine Learning to Textile-embedded Sensors Matthew Howard July 23, 2018 matthew.j.howard@kcl.ac.uk nms.kcl.ac.uk/matthew.j.howard @mhoward3210 Robot Learning Lab Human behaviour modelling, extraction, understanding.

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Tailoring Machine Learning to Textile-embedded Sensors

Matthew Howard

July 23, 2018 matthew.j.howard@kcl.ac.uk · nms.kcl.ac.uk/matthew.j.howard · @mhoward3210

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Robot Learning Lab

  • Human behaviour modelling,

extraction, understanding.

  • Imitation learning,

programming by demonstration.

  • Optimal feedback control,

reinforcement learning.

  • Humanoid robotics, variable

impedance actuation.

  • Natural interfaces for capturing

human behaviour.

matthew.j.howard@kcl.ac.uk · www.inf.kcl.ac.uk/staff/mhoward · @mhoward3210

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matthew.j.howard@kcl.ac.uk · nms.kcl.ac.uk/matthew.j.howard · @mhoward3210

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Programming by Demonstration

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MH, D. Braun, & S. Vijayakumar. Transferring human impedance behavior to heterogeneous variable impedance

  • actuators. IEEE T-Ro, 29(4):847-862, 2013
  • D. Mitrovic, S. Klanke, & S. Vijayakumar. Learning impedance

control of antagonistic systems based on stochastic optimization

  • principles. IJRR, 30(5):556-573, 2011
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Myographic Prosthesis Control

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Measuring natural motion

  • R. B. R. Manero, et al. Wearable embroidered muscle activity sensing device for the human upper leg. EMBC 2016,
  • D. Roetenberg, H. Luinge, & P. Slycke. Xsens mvn: full 6dof human motion tracking using miniature inertial sensors. Tech. rep.,

Xsens Motion Technologies, 2009, L. Buechley & M. Eisenberg. The lilypad arduino: Toward wearable engineering for everyone. IEEE Pervasive, 7:12-15, 2008 matthew.j.howard@kcl.ac.uk · nms.kcl.ac.uk/matthew.j.howard · @mhoward3210

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Measuring natural motion

matthew.j.howard@kcl.ac.uk · nms.kcl.ac.uk/matthew.j.howard · @mhoward3210

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Measuring natural motion

Ali Shafti – CORE Seminar 11th January 2017

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Surface Electromyography (sEMG)

  • Non-invasive but obtrusive
  • Allows monitoring of muscle activity, effort and fatigue
  • Applications in
  • Gait monitoring
  • Effort assessment
  • Robotic prosthetics
  • Human-robot interaction
  • Ergonomics and comfort assessments

C.J. De Luca et. al “Decomposition of Surface EMG Signals”, J Neurophysiol, 2006.

matthew.j.howard@kcl.ac.uk · nms.kcl.ac.uk/matthew.j.howard · @mhoward3210

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Measuring natural motion

Ali Shafti – CORE Seminar 11th January 2017

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Surface Electromyography (sEMG)

  • Non-invasive but obtrusive
  • Allows monitoring of muscle activity, effort and fatigue
  • Applications in
  • Gait monitoring
  • Effort assessment
  • Robotic prosthetics
  • Human-robot interaction
  • Ergonomics and comfort assessments

C.J. De Luca et. al “Decomposition of Surface EMG Signals”, J Neurophysiol, 2006.

Vijay Bhaskar Semwal Vijay Bhaskar Semwal

matthew.j.howard@kcl.ac.uk · nms.kcl.ac.uk/matthew.j.howard · @mhoward3210

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Karina Thompson Matthew Howard

Muscles, Getting a Stitch!

matthew.j.howard@kcl.ac.uk · nms.kcl.ac.uk/matthew.j.howard · @mhoward3210