Feedback-based metric learning for activity recognition.

• Incremental user activity recognition model learned from crowdsourcing user feedback. • Similarity metric learning is introduced and improved the activity recognition model. • Multi-metric learning mixed expert model integrated different user feedbacks. • The mixture expert model interprets the process of user feedback generation. • Manifested the efficiency by good accuracy between mixture expert and other methods. Mobile activity recognition is an effective approach to understanding human context in real time. Existing methods based on supervised learning that require a large amount of training samples for building activity recognition models. The collection of labeled training samples is a boring process and most users are reluctant to get involved. Crowdsourcing is a simple and potential approach to collecting the training samples and building accurate activity recognizers. Since different people usually have different physical features and behavior patterns, an accurate activity recognition model cannot be constructed directly from the training samples collected by crowdsourcing. In this paper, we have proposed a Mixture Expert Model for Activity Recognition (MEMAR) based on feedback and crowdsourcing samples. The proposed model can continuously discover the difference between user activity and crowdsourcing samples. Then we update activity recognition models with the discovered differences. A mobile can correctly utilize crowdsourcing samples for recognition model construction with MEMAR and can also track and recognize mobile users' behavior dynamics. The experiments based on a smartphone dataset verify the validity of MEMAR. We believe MEMAR provides a basis for context-aware mobile applications. [ABSTRACT FROM AUTHOR]