Human activity monitoring based on hidden Markov models using a smartphone

2016 ◽  
Vol 19 (6) ◽  
pp. 27-31 ◽  
Author(s):  
Ruben San-Segundo ◽  
Julian Echeverry-Correa ◽  
Christian Salamea ◽  
Jose Manuel Pardo
Keyword(s):  
Hidden Markov Models ◽  
Human Activity ◽  
Markov Models ◽  
Hidden Markov ◽  
Activity Monitoring

scholarly journals Activity Classification for Interactive Game Interfaces

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
John Darby ◽  
Baihua Li ◽  
Nick Costen
Keyword(s):  
Hidden Markov Models ◽  
Human Activity ◽  
Markov Models ◽  
Feature Vector ◽  
Hidden Markov ◽  
Human Motion ◽  
Joint Angles ◽  
Activity Classification ◽  
Motion Classification ◽  
Potential Applications

We present a technique for modeling and recognising human activity from moving light displays using hidden Markov models. We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markov models are then trained with the resulting time series, one for each of a variety of human activity, using the Baum-Welch algorithm. Motion classification is then attempted by evaluation of the forward variable for each model using previously unseen test data. Experimental results based on real-world human motion capture data demonstrate the performance of the algorithm and some degree of robustness to data noise and human motion irregularity. This technique has potential applications in activity classification for gesture-based game interfaces and character animation.

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