Journal Article10.1162/089976699300016890
Modeling and prediction of human behavior
Alex Pentland,Andrew Liu +1 more
TL;DR: This work proposes that many human behaviors can be accurately described as a set of dynamic models sequenced together by a Markov chain and uses these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time.
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Abstract: We propose that many human behaviors can be accurately described as a set of dynamic modes (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to achieve 95% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements. Language: en
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Citations
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Reconnaissance de stress à partir de données hétérogènes
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- 03 Jul 2017
TL;DR: A ete propose le projet Psypocket qui vise a concevoir un systeme portable capable d'analyser precisement l'etat de stress d'une personne en fonction of ses modifications physiologiques, psychologiques and comportementales, puis de proposer des solutions de retroaction pour reguler cet etat.
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Compensating for Operational Uncertainty in Man–Machine Systems: A Case Study on Intelligent Vehicle Parking Assist System
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TL;DR: In this article, the authors present a general framework for a reliable system that compensates for human-operating uncertainty during operation, which is applied to the development of an intelligent vehicle parking assist system.
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Fatjon Seraj
- 28 Jun 2017
TL;DR: This thesis shows that with the help of advanced signal processing and machine learning, despite the many inaccuracies in the observations, the transport infrastructure can accurately reflect road quality and type of damage.
Modelling of State of Charge Recognition: Use of a Bayesian Approach to Formulate Hidden State Perceptions and Emotions
TL;DR: A Bayesian framework is proposed that explains and estimates human SOC perception and related emotions and experimentally verifies the model predictions and suggests that the deviations between actual SOC variations and driver expectations are necessary to determine the emotional experiences of drivers caused by managing SOC noise.
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Data-driven Steering Torque Behaviour Modelling with Hidden Markov Models
TL;DR: In this paper , a Hidden Markov Model (HMM) is used to predict driver steering torque with Gaussian Mixture Regression (GMR) and an extensive parameter selection framework enables to objectively select the model hyper-parameters and prevents overfitting.
1
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TL;DR: Using hidden Markov models (HMM's), an unobstrusive single view camera system is developed that can recognize hand gestures, namely, a subset of American Sign Language (ASL), achieving high recognition rates for full sentence ASL using only visual cues.
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