<p dir="ltr">Due to biomechanical dynamics are related to the movement patterns and gait characteristics of human people and may provide important insights if mined by deep learning models, we conduct the study the spatial variability of biomechanical dynamics, aiming to evaluate and determine the optimal body location that is of great promise in robust physical activity type detection. Then we have developed a framework for deep learning pruning, aiming to determine the optimal pruning schemes while maintaining acceptable performance. Finally, we have enhanced and boosted the efficient deep learning framework, to co-optimize the accuracy and the continuity during the pruning process.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26069641 |
| Date | 03 September 2024 |
| Creators | Ming Liu (18857713) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/DEEP_LEARNING_OF_BIOMECHANICAL_DYNAMICS_WITH_SPATIAL_VARIABILITY_MINING_AND_MODEL_SPARSIFIATION/26069641 |
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