Building personalized activity recognition models with scarce labeled data based on class similarities

With the recent advent of new devices with embedded sensors, Human Activity Recognition (HAR) has become a trending topic in the last years because of its potential applications in pervasive health care, assisted living, exercise monitoring, etc. Most of the works on HAR either require from the user to label the activities as they are performed so the system can learn them, or rely on a trained device that expects a “typical” ideal user. The first approach is impractical, as the training process easily become time consuming, expensive, etc., while the second one drops the HAR precision for many non-typical users. In this work we propose a “crowdsourcing” method for building personalized models for HAR by combining the advantages of both user-dependent and general models by finding class similarities between the target user and the community users. We evaluated our approach on 4 different public datasets and showed that the personalized models outperformed the user-dependent and general models when labeled data is scarce.