A Hybrid Model Using Hidden Markov Chain and Logic Model for Daily Living Activity Recognition

Abstract

We detail the solution to the UCAmI Cup Challenge to recognizing on going activities at home from sensor measurements. We use binary sensors and proximity sensor measurements for the recognition. We use an hybrid strategy, combining a probabilistic model and a definition-based model. The former consists of a Hidden Markov Model using the result of a neural network as emission probabilities. It is trained with the labelled data provided by the Cup. The latter approach takes advantage of the descriptions provided for each of the activities which are expressed in logical statements based on the sensors states. We then combine the results with a weighted average. We compare the performance of each individual strategy and of the combined strategy.

Keywords

• activity recognition
• daily living activities
• neural networks
• hidden markov models
• event recognition