AI-based Fall-Risk Assessment during Daily Activities in Post Stroke Survivors using Smartphones

使用智能手机对中风后幸存者进行日常活动期间基于人工智能的跌倒风险评估

• 批准号: 10580558
• 负责人: Rahul Soangra
• 金额: $ 39.13万
• 依托单位: CHAPMAN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580558
• 关键词: Activities of Daily Living; Assessment tool; Caring; Cellular Phone; Characteristics; Classification; Clinic; Clinical; Data; Decision Support Systems; Diagnosis; Digital biomarker; Early Intervention; Early identification; Engineering; Environment; Foundations; Future; Gait; Goals; Health; Health Sciences; Hispanic; Home; Hour; Individual; Injury; Intervention; Laboratories; Living Wills; Machine Learning; Measurement; Mission; Modality; Modeling; Monitor; Morbidity - disease rate; Movement; Obesity; Participant; Patient Recruitments; Patients; Performance; Population; Quality of life; ROC Curve; Rehabilitation therapy; Reporting; Research; Risk; Risk Assessment; STEM research; Sensitivity and Specificity; Stroke; Students; Training; Underrepresented Students; United States National Institutes of Health; Universities; Walking; Woman; artificial intelligence algorithm; cost; deep neural network; design; ethnic minority; experience; fall risk; falls; follow-up; graduate student; health application; high risk; improved; machine learning algorithm; machine learning model; mortality; personalized care; portability; post stroke; programs; sensor; stroke rehabilitation; stroke survivor; tool; undergraduate student; wearable sensor technology

Project Summary Falls in post-stroke survivors are up to 73% in the first year after stroke, and at least 70% of ambulatory stroke survivors experience an annual fall. The detrimental effects of falls include serious injuries, increased morbidity and mortality, dwindling functional mobility and quality of life, and high health-related costs. Most fall risk assessments for ambulatory post-stroke survivors are based on an ordinal scale of functional measurements, lack objectivity and accuracy, and are limited to clinical or laboratory environments. Early identification of post- stroke survivors at risk of falling is crucial for developing timely tailored interventions to reduce falls. This project aims to develop a machine learning (ML) based fall risk assessment tool for ambulatory stroke survivors by using inertial sensor data from smartphone worn at the waist during activities of daily living. This endeavor will involve graduate and undergraduate (UG) students at each stage of the project and expose them to multiple facets of rigorous scientific research. Chapman University is at the forefront of stroke rehabilitation and organizes Stroke Boot Camp (SBC), a free rehabilitation program every semester. In addition, CSU Long Beach’s pro bono clinic will provide us with easy access to the nearby stroke population. The overall goal of this project is to develop a portable decision support system for clinicians to diagnose fall risk even when the patient is away from the clinic. This study aims 1) To establish if digital biomarkers extracted from the smartphone data while performing prescribed ADLs significantly differ between high and low-risk fallers in laboratory settings. The remaining data needed to build the ML model is collected entirely in the participant’s home setting. The participants will wear the smartphone on their waist during all waking hours and perform regular activities of daily living. 2) To train three ML models that can classify fall-risk using different data modalities: using i) passively collected 3-day ADL data, ii) data from prescribed simple ADL tasks like turning, walking, and sit-to-stand, iii) combined subjective and objective data. 3) To assess the predictive validity of the ML models against actual fall occurrences after six months. The successful implementation of the project will enhance stroke care by an accurate fall risk assessment for ambulatory stroke survivors. Identifying post-stroke individuals at high risk of falling will allow early intervention to improve care and quality of life in these individuals. In addition, this study has the potential for developing a product that could track progress during stroke rehabilitation.

项目概要 中风后幸存者中，中风后第一年跌倒的比例高达 73%，其中至少 70% 是流动中风 幸存者每年都会经历一次跌倒，跌倒的不利影响包括严重受伤、发病率增加。 和死亡率、功能活动能力和生活质量下降以及与健康相关的高昂费用。 对中风后幸存者的动态评估是基于功能测量的顺序量表， 缺乏客观性和准确性，并且仅限于临床或实验室环境的早期识别。 有跌倒风险的中风幸存者对于及时制定针对性的干预措施以减少跌倒至关重要。 该项目旨在开发一种基于机器学习（ML）的动态跌倒风险评估工具 通过使用日常生活活动中佩戴在腰部的智能手机的惯性传感器数据来帮助中风幸存者。 这项工作将让研究生和本科生 (UG) 参与项目的每个阶段，并揭示 查普曼大学在多个方面都处于中风研究的前沿。 康复并组织中风训练营（SBC），这是每学期的免费康复计划。 此外，科罗拉多州立大学长滩分校的无偿诊所将使我们能够轻松接触附近的中风人群。 该项目的总体目标是开发一个便携式决策支持系统，供封建领主诊断跌倒 即使患者离开诊所也存在风险。本研究的目的是 1) 确定数字生物标志物是否存在。 在执行规定的 ADL 时从智能手机数据中提取的数据在高和低之间存在显着差异 建立机器学习模型所需的剩余数据完全在实验室环境中收集。 参与者将在醒着的时候将智能手机佩戴在腰间 并进行日常生活的常规活动 2) 训练三个可以使用不同方式对跌倒风险进行分类的 ML 模型。 数据模式：使用 i) 被动收集 3 天的 ADL 数据，ii) 来自规定的简单 ADL 任务的数据，例如 转身、行走和坐立，iii) 结合主观和客观数据 3) 评估预测。 六个月后机器学习模型相对于实际跌倒发生情况的有效性。 该项目的成功实施将通过准确的跌倒风险来加强中风护理 对流动中风幸存者进行评估将有助于识别高跌倒风险的中风后个体。 此外，这项研究还有潜力改善这些人的护理和生活质量。 获奖理由：开发一款可以跟踪中风康复过程中进展情况的产品。