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Rehabilitation of stroke patients with parallel
GMM- and DNN-HMM based human activity
August 2016
GMM- and DNN-HMM based h...
Outline
Motivation: CogWatch overview
System design: instrumentation and sensors
System description: parallel detector str...
CogWatch and Stroke Rehabilitation
68% of stroke survivors in the UK suffer from
Apraxia or Action Disorganization Syndrom...
Sensors Involved in Tea-Making
CogWatch instrumented coaster
– Three-axes accelerometers
– Force sensitive resistors– Forc...
Tea-Making Setup and Sub-goals Involved
Parallel Subgoal Detectors
Subgoal Detector Diagram
Structure of the ‘target sub-goal HMM’ and
‘background HMM’ Models in a Single Detector
Different Stages of DNN Based Modelling
Real-time Viterbi Decoding (Partial
Traceback Algorithm )
Experimental Results
The relative error reduction of 79%, 50%, 42%, 36%, 13% and 13% is achieved for detection of ‘Add sug...
Summary
CogWatch system introduced for stroke patients
to improve the rehabilitation
Parallel HMM detector used for action...
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Presentation-Cogwatch

  1. 1. Rehabilitation of stroke patients with parallel GMM- and DNN-HMM based human activity August 2016 GMM- and DNN-HMM based human activity recognition using instrumented objects Maryam Najafian, Roozbeh Nabiei, Prof. Martin Russell, Prof. Allen Wing m.najafian@utdallas.edu [rxn946, m.j.russell,a.m.wing]@bham.ac.uk School of Electrical and Computer Engineering, and School of Psychology
  2. 2. Outline Motivation: CogWatch overview System design: instrumentation and sensors System description: parallel detector structureSystem description: parallel detector structure Experimental results and discussion Summary
  3. 3. CogWatch and Stroke Rehabilitation 68% of stroke survivors in the UK suffer from Apraxia or Action Disorganization Syndrome Apraxia is impairment of cognitive ability toApraxia is impairment of cognitive ability to carry out activity of daily livings (ADLs) – Self feeding (Making cup of tea) CogWatch aims to enhance the rehabilitation process of stroke patients suffers Apraxia https://www.youtube.com/watch?v=MiLUUmPlWkc&index=3&list=PLUVuIyC7hO z7QXSXm89KKJ2-GnAh-CzAB
  4. 4. Sensors Involved in Tea-Making CogWatch instrumented coaster – Three-axes accelerometers – Force sensitive resistors– Force sensitive resistors – Wireless module Kinect camera
  5. 5. Tea-Making Setup and Sub-goals Involved
  6. 6. Parallel Subgoal Detectors
  7. 7. Subgoal Detector Diagram
  8. 8. Structure of the ‘target sub-goal HMM’ and ‘background HMM’ Models in a Single Detector
  9. 9. Different Stages of DNN Based Modelling
  10. 10. Real-time Viterbi Decoding (Partial Traceback Algorithm )
  11. 11. Experimental Results The relative error reduction of 79%, 50%, 42%, 36%, 13% and 13% is achieved for detection of ‘Add sugar’, ‘Pour kettle’, ‘Add teabag’, ‘Add milk’, ‘Remove teabag’ and ‘Stir’ sub-goals, respectively. Surprisingly, for the ‘Fill kettle’ sub-goal the performance was improved by using the GMM- rather than a DNN-HMM based system. This may be due to the fact that generative models are stronger in modelling tasks which includes unseen data.
  12. 12. Summary CogWatch system introduced for stroke patients to improve the rehabilitation Parallel HMM detector used for action recognition: addresses overlapped actions HMM detector: addresses inter- and intra-user speed and sequence variabilities HMM output probability distributions were modeled using DNNs and GMMs: discriminative rather than generative Partial trace-back algorithm is used to implement the real-time Viterbi decoding
  13. 13. Thank youThank you

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