Mobility is essential for human health. Regular physical activity helps prevent heart disease and stroke, relieves symptoms of depression, and promotes weight loss. Unfortunately, many conditions, such as cerebral palsy, osteoarthritis, and obesity, limit mobility at an enormous personal and societal cost. While vast amounts of data are available from hundreds of research labs and millions of smartphones, there is a dearth of methods for analyzing this massive, heterogeneous dataset. We propose to establish the National Center for Mobility Data Integration to Insight (the Mobilize Center) to overcome the data science challenges facing mobility big data and biomedical big data in general. Our preliminary work identified four bottlenecks in data science, which drive four Data Science Research Cores. The Cores include Biomechanical Modeling, Statistical Learning, Behavioral and Social Modeling, and Integrative Modeling and Prediction. Our Cores will produce novel methods to integrate diverse modeling modalities and gain insight from noisy, sparse, heterogeneous, and time-varying big data. Our data-sharing consortia, with clinical, research, and industry partners, will provide mobility data for over ten million people. Three Driving Biomedical Problems will focus and validate our data science research. The Mobilize Center will disseminate our novel data science tools to thousands of researchers and create a sustainable data-sharing consortium. We will train tens of thousands of scientists to use data science methods in biomedicine through our in-person and online educational programs. We will establish a cohesive, vibrant, and sustainable National Center through the leadership of an experienced executive team and will help unify the BD2K consortia through our Biomedical Computation Review publication and the Simtk.org resource portal. The Mobilize Center will lay the groundwork for the next generation of data science systems and revolutionize diagnosis and treatment for millions of people affected by limited mobility.

Public Health Relevance

Regular physical activity is essential for human health, yet a broad range of conditions impair mobility. This project will transform human movement research by developing tools for data analysis and creating software that will advance research to prevent, diagnose, and reduce impairments that limit human movement.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54EB020405-01
Application #
8775015
Study Section
Special Emphasis Panel ()
Program Officer
Peng, Grace
Project Start
2014-09-29
Project End
2018-09-30
Budget Start
2014-09-29
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
$2,050,748
Indirect Cost
$733,199
Name
Stanford University
Department
Biomedical Engineering
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Wu, Sen; Hsiao, Luke; Cheng, Xiao et al. (2018) Fonduer: Knowledge Base Construction from Richly Formatted Data. Proc ACM SIGMOD Int Conf Manag Data 2018:1301-1316
Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure (2018) Modeling Interdependent and Periodic Real-World Action Sequences. Proc Int World Wide Web Conf 2018:803-812
Erdemir, Ahmet; Hunter, Peter J; Holzapfel, Gerhard A et al. (2018) Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research. J Biomech Eng 140:
Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure et al. (2018) HUMAN DECISIONS AND MACHINE PREDICTIONS. Q J Econ 133:237-293
Pierson, Emma; Althoff, Tim; Leskovec, Jure (2018) Modeling Individual Cyclic Variation in Human Behavior. Proc Int World Wide Web Conf 2018:107-116
Powers, Scott; Qian, Junyang; Jung, Kenneth et al. (2018) Some methods for heterogeneous treatment effect estimation in high dimensions. Stat Med 37:1767-1787
Halilaj, Eni; Rajagopal, Apoorva; Fiterau, Madalina et al. (2018) Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. J Biomech 81:1-11
Lin, Zhiyuan; Althoff, Tim; Leskovec, Jure (2018) I'll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application. Proc Int World Wide Web Conf 2018:1501-1511
Seth, Ajay; Hicks, Jennifer L; Uchida, Thomas K et al. (2018) OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol 14:e1006223
Yong, Jennifer R; Silder, Amy; Montgomery, Kate L et al. (2018) Acute changes in foot strike pattern and cadence affect running parameters associated with tibial stress fractures. J Biomech 76:1-7

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