A Bayesian model of plan recognition
Artificial Intelligence
State duration modelling in hidden Markov models
Signal Processing
Nonstationary hidden Markov model
Signal Processing
Probabilistic State-Dependent Grammars for Plan Recognition
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Large-Scale Event Detection Using Semi-Hidden Markov Models
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Coupled Hidden Semi Markov Models for Activity Recognition
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
High-level goal recognition in a wireless LAN
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Extending continuous time Bayesian networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
A plan classifier based on Chi-square distribution tests
Intelligent Data Analysis
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In this paper, we investigate the use of hidden semi-Markov models (HSMMs) in analyzing data of human activities, a task commonly referred to as activity recognition. In particular, we use the models to recognize normal and abnormal two-dimensional joystick-generated movements of a cursor, controlled by human users in a computerized clinical maze task. This task - as many other activity recognition tasks - places a lot of emphasis on the duration of states. To model the impact of these durations, we present an extension of HSMMs, called Non-Stationary Hidden Semi Markov Models (NSHSMMs). We compare the performance of HMMs, HSMMs and NSHSMMs in recognizing normal and abnormal activities in the data, revealing the advantages of each method under different conditions. We report the results of applying these methods in analyzing real-world data, from 75 subjects executing clinical diagnosis maze-navigation tasks. For relatively simple activity recognition tasks, both HSMMs and NSHSMMs easily and significantly outperform HMMs. Moreover, the results show that HSMM and NSHSMM successfully differentiate between human subject behaviors. However, in some tasks the NSHSMMs outperform the HSMMs and allow significantly more accurate recognition. These results suggest that semi-Markov models, which explicitly account for durations of activities, may be useful in clinical settings for the evaluation and assessment of patients suffering from various cognitive and mental deficits.