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Activity recognition using logical hidden semi-Markov models
2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2013Activity recognition is challenging and valuable in both real and virtual world. As important directed graphical models, hidden Markov models and their extensions are widely used to solve probabilistic activity recognition problems. In this paper, logical hidden semi-Markov models (LHSMMs) which combine logical hidden Markov models (LHMMs), a ...
Ya-Bing Zha +3 more
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Coupled Hidden Semi Markov Models for Activity Recognition
2007 IEEE Workshop on Motion and Video Computing (WMVC'07), 2007Recognizing human activity from a stream of sensory observations is important for a number of applications such as surveillance and human-computer interaction. Hidden Markov Models (HMMs) have been proposed as suitable tools for modeling the variations in the observations for the same action and for discriminating among different actions.
Pradeep Natarajan, Ramakant Nevatia
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Hidden semi-Markov models for electricity load disaggregation
ACM SIGMETRICS Performance Evaluation Review, 2019This paper assesses the performance of a technique for estimating the power consumption of individual devices based on aggregate consumption. The new semi-Markov technique, outperforms pure hidden Markov models on the REDD dataset. The technique also exploits information from transients to eliminate a substantial fraction of the observed ...
Yung Fei Wong +2 more
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Implementation of hidden semi-Markov models
2011One of the most frequently used concepts applied to a variety of engineering and scientific studies over the recent years is that of a Hidden Markov Model (HMM). The Hidden semi-Markov model (HsMM) is contrived in such a way that it does not make any premise of constant or geometric distributions of a state duration.
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Online apnea–bradycardia detection based on hidden semi-Markov models
Medical & Biological Engineering & Computing, 2014In this paper, we propose a new online apnea-bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series.
Miguel, Altuve +4 more
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Adaptive Training for Hidden Semi-Markov Model
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006Junichi Yamagishi, Takao Kobayashi
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A time-efficient factorial hidden Semi-Markov model for non-intrusive load monitoring
Electric Power Systems Research, 2021Chao Wang, Huai-Qing Zhang
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