The Learning Edge
metadata only
Chaos-informed 1D CNNs for gait dynamics classification
This paper presents a chaotic-adaptive one-dimensional convolutional neural network (1D CNN) architecture for classifying gait dynamics. The novel approach uses a nonlinear dynamic modeling concept to learn subtle deviations in gait patterns, which enables the identification of both normal and const
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis#793250
- Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis#789246
- Deep learning for stress oriented human activity recognition.#251332
- The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations#926861
- Enhancing Adversarial Robustness via Cooperative Adversarial Distillation in Spiking Neural Networks#381267
Record · ID 1082043
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive