Abstract
Human activity recognition (HAR), the use of sensor data to identify activities performed by an individual, has garnered substantial research attention. However, obtaining data for HAR is costly. As such, recent studies have used Generative Adversarial Networks (GANs) to create synthetic HAR data. Problematically, these solutions cannot generate personalized data that match the behavior of a user, limiting potential HAR applications as prior work has shown that personalized HAR models outperform generalized ones. To address this problem, we propose a novel controllable GAN where user and activity attributes can be explicitly requested for generation under one model. The GAN can learn these attributes independently and infer context pairs never seen during training: an impossibility for existing methodologies. We show that while our model is comparable to standard HAR GANs, its ability to consider user differences and generate novel context pairs outperforms these state-of-the-art GANs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
