Activity classification at a higher level
Rabih Younes, Thomas L. Martin, Mark T. Jones · 2015
Research in activity classification has focused on the sensors, the classification techniques and the machine learning algorithms used in the classifier. In this work, we study a higher level of activity classification. We present two methods that can take the final observations of a classifier and improve them. The first method uses hidden Markov models to define a probabilistic model that can be used to improve classification accuracy. The second method is a novel method that we developed that uses probabilistic models along with matching costs in order to improve accuracy. Testing showed that both proposed methods presented a significant increase in classification accuracy rates, while also proving that they can both run in real time.