NCBI PubMed Central
open access
Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning.
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
- Intelligent fall risk prediction and real-time warning system for elderly care based on multimodal deep learning and wearable sensor fusion.#631148
- Developing a Falls Prediction Tool Using Both Accelerometer and Video Gait Analysis Data in Older Adults#1150179
- Wearable Ferroelectret Force Myography Dataset for Gait Phase Estimation#248636
- Advancing Neurological Healthcare: Machine Learning Applications for Gait Disorders and Fall Detection / Jalal Alizadeh#1145581
- Monitoring and Sensing System for People's Behavior During Fall Events Based on Mobility Analysis#126867
Record · ID 159898
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