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Using encoder-decoder neural networks to learn representations of personal walking style, and generating person-specific gait for desired activities.

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abs711/Optimizing-representations-for-gait-generation

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Optimizing-representations-for-gait-generation

Code coming soon! Details of this project can be found on the following link: https://ieeexplore.ieee.org/document/10121330

How would this work in practice

Concept_in_action_updated_heads1

Network Architectures

We compare 4 methods: Baseline (adapted from https://arxiv.org/pdf/1511.05644.pdf), Output Regularization, Latent Regularization, and Combined Regularization.

Baseline

1_Vanilla_training_detailed_relu_linear

Output Regularization

3_Output_Regularization_detailed_relu_linear

Latent Regularization

4_Latent_Regularization_wPretraining_detailed_relu_linear

Learned representations of personal style from the data

Different subjects are shown in different colors.

All_latent_spaces2_targetarrows

Generated Trajectories

Trajectories generated by different methods overlayed with the "Ground Truth" of the unseen target user (shown in black).

all_gens_final png

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Using encoder-decoder neural networks to learn representations of personal walking style, and generating person-specific gait for desired activities.

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