The proliferation of mobile devices has enabled extensive mobiledata supported applications, e.g., exercise and activity recognition and quantification. Typically, these applications need predefined features and are only applicable to predefined activities. In this work, we address the issue of deep understanding of arbitrary activities and semantic searching of any activity over massive mobile sensing data. The challenges stem from the rich dynamics and the wide-spectrum of activities that a human being could perform. We propose a hierarchical activity representation, extract common bases of motion data in an unsupervised manner by leveraging the power of deep neural networks, and propose a universal multi-resolution representation for all activities without prior knowledge. Based on this representation, we design an innovative system Lasagna to manage and search motion data semantically. We implement a prototype system and our comprehensive evaluations show that our system can achieve highly accurate activity classification (with precision 98.9%) and search (with recall almost 100% and precision about 90%) over a diverse set of activities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.