This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $300,000 to The Pennsylvania State University to create simulated walking data, called synthetic data, that can be used together with real walking data to train machine learning tools. The goal is to develop methods for synthesizing realistic gait data that will enable the use of data-hungry modern nonparametric techniques, which could lead to significant improvements in predicting fall risk. The award period runs from August 1, 2025 to July 31, 2027. The project aims to address challenges in synthesizing gait data due to the complexity of the physical system, the sequential nature of the data, and high variation in gait patterns across individuals and scenarios. The research will explore multiple data generation techniques, including methods using a central pattern generator-based model and linear/nonlinear higher-order controllers to ensure physical feasibility of the synthetic data.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $300.0k | 7/2/25 |