Cai Fulin, Patharkar Abhidnya, Wu Teresa, Lure Fleming Y M, Chen Harry, Chen Victor C
School of Computing and Augmented Intelligence and ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA.
MS Technologies Corp, Rockville, MD 20580, USA.
IEEE Sens J. 2023 May 15;23(10):10998-11006. doi: 10.1109/jsen.2023.3263071. Epub 2023 Apr 3.
Abnormal gait is a significant non-cognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar 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 a micro-Doppler radar 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, shoulder, etc.). 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.
异常步态是阿尔茨海默病（AD）及AD相关痴呆（ADRD）的一项重要非认知生物标志物。微多普勒雷达作为一种非穿戴技术，能够捕捉人体步态运动，用于潜在的ADRD早期风险评估。在本研究中，我们提议设计将微多普勒雷达传感器与先进人工智能（AI）技术相结合的STRIDE。STRIDE嵌入了一个新的深度学习（DL）分类框架。作为概念验证，我们开发了STRIDE的“数字孪生”，它由一个人体行走模拟模型和一个微多普勒雷达模拟模型组成，以生成步态特征数据集。利用既定的人体行走参数，该行走模型模拟了不同条件下患有ADRD的个体。基于电磁散射和多普勒频移模型的雷达模型用于生成来自不同运动身体部位（如脚、肢体、关节、躯干、肩膀等）的微多普勒特征。开发了一个基于频段的DL框架来预测ADRD风险。实验结果证明了STRIDE在评估ADRD风险方面的有效性和可行性。
