Active Sensing In Human Activity Recognition

This work studies the problem of reducing the energy consumption of wearable sensors in a Human Activity Recognition (HAR) system. A HAR system is implemented using Hidden Markov Models, where decisions over the acquisition of new data are made based on the entropy of the posterior distribution of the activities. This problem is intractable in general, so three different active sensing algorithms are implemented to find numerically the data acquisition events. The performance of these algorithms is evaluated using a HAR database, resulting in a significant reduction on the number of observations acquired, thus reducing the energy consumption, while maintaining the performance of the system.