STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation With Gait Signature Simulation and Deep Learning

Fulin Cai, Abhidnya Patharkar, Teresa Wu, Fleming Y.M. Lure, Harry Chen, Victor C. Chen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Abnormal gait is a significant noncognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar (MDR), a nonwearable technology, can capture human gait movements for potential early ADRD risk assessment. In this article, we propose to design a systematic radar intelligence analysis for ARDR risk evaluation (STRIDE) integrating MDR sensors with advanced artificial intelligence (AI) technologies. STRIDE embeds a new deep learning (DL) classification framework. As a proof of concept, we develop a 'digital twin' of STRIDE, consisting of a human walking simulation model and an MDR simulation model, to generate a gait signature dataset. Taking established human walking parameters, the walking model simulates individuals with ADRD under various conditions. The radar model based on electromagnetic scattering and the Doppler frequency shift model is employed to generate micro-Doppler signatures from different moving body parts (e.g., foot, limb, joint, torso, and shoulder). A band-dependent DL framework is developed to predict ADRD risks. The experimental results demonstrate the effectiveness and feasibility of STRIDE for evaluating ADRD risk.

Original languageEnglish (US)
Pages (from-to)10998-11006
Number of pages9
JournalIEEE Sensors Journal
Volume23
Issue number10
DOIs
StatePublished - May 15 2023

Keywords

  • Alzheimer's disease and related dementia (ADRD)
  • deep learning (DL)
  • gait analysis
  • micro-Doppler radar (MDR)

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation With Gait Signature Simulation and Deep Learning'. Together they form a unique fingerprint.

Cite this