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HMM finds behavioral patterns…

HMM finds behavioral patterns…. Zoltán Szabó Eötvös Loránd University. Contributors. Neural Information Processing Group György Hévízi (first author) Mihály Biczó Barnabás Póczos Bálint Takács Andr ás Lőrincz (head). HCI. Adaptive interface User’s actual state?

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HMM finds behavioral patterns…

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  1. HMM finds behavioral patterns… Zoltán Szabó Eötvös Loránd University

  2. Contributors • Neural Information Processing Group • György Hévízi (first author) • Mihály Biczó • Barnabás Póczos • Bálint Takács • András Lőrincz (head) Neural Information Processing Group, Eötvös Loránd University

  3. HCI • Adaptive interface • User’s actual state? • Behavioral model is needed Neural Information Processing Group, Eötvös Loránd University

  4. X f(Y|X) Y Possibilities for behavioral models • Examples: • Markov Chain (MC): • Hidden Markov Model (HMM): • Bayes Network ( ) : more general Neural Information Processing Group, Eötvös Loránd University

  5. Our long term goal • Adaptation to user by RL: Markov Decision Process • HMM: • Behavioral components upon practising? • Similar patterns for users? • Capable of extracting them? Neural Information Processing Group, Eötvös Loránd University

  6. Tools • Dasher: • Pointing-gestures driven text entry solution • Born at Cambridge • Optional: predictive language model • Our solution: headmouse as input device • For control experiments: normal desk mouse • HMM: user modelling Neural Information Processing Group, Eötvös Loránd University

  7. Dasher Neural Information Processing Group, Eötvös Loránd University

  8. Headmouse • Combines: head detection + tracking • Technical details: Haar wavelets + optic flow • Non-intrusive + cheap • Alternative communication tool • Free for download: • http://nipg.inf.elte.hu/headmouse/headmouse.html Neural Information Processing Group, Eötvös Loránd University

  9. User modelling • Hidden Markov Model: • Observation: cursor speed user movement • Hidden states: Gaussian emission • Assumption: independence (diagonal covariance) s Neural Information Processing Group, Eötvös Loránd University

  10. Experiments • Participants: • 5 volunteer PhD students • unexperienced in Dasher • Task: typing short sentences from lyrics with Dasher • e.g.: ,,Children need travelling shoes’’ • Cursor trajectories were saved Neural Information Processing Group, Eötvös Loránd University

  11. (A) (B) (C) Learning graph Dasher can be learned. Neural Information Processing Group, Eötvös Loránd University

  12. Practising Hidden states found by HMM Else P Neural Information Processing Group, Eötvös Loránd University

  13. Mistake OK Accelerate a a z z Interpretation of hidden states Most probable states by Viterbi: others Neural Information Processing Group, Eötvös Loránd University

  14. Outlook • Recognition of users’ behavioral patterns: • On-line adaptive functionality: • Personalization for individual users • Alternative help options • Complex interaction with computer • Relevance: • tool for handicapped non-speaking people Neural Information Processing Group, Eötvös Loránd University

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