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

项目摘要 卒中后存活者的福尔斯跌倒率在卒中后第一年高达73%，在非卧床卒中中至少为70 幸存者每年都会经历一次坠落福尔斯的有害影响包括严重伤害、发病率增加 和死亡率，功能活动性和生活质量下降，以及与健康有关的费用高昂。大多数跌倒风险 对非卧床中风后幸存者的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。及早查明员额 中风幸存者有跌倒的风险，这对于及时制定针对性的干预措施以减少福尔斯是至关重要的。 本项目旨在开发一种基于机器学习（ML）的门诊跌倒风险评估工具， 中风幸存者通过使用惯性传感器数据在日常生活活动期间佩戴在腰部的智能手机。 这项奋进将涉及研究生和本科生（UG）在项目的每个阶段的学生，并暴露 他们对严格的科学研究的多个方面。查普曼大学是中风的前沿 他还组织了中风靴子营（SBC），每学期一次免费的康复计划。在 此外，CSU长滩的公益诊所将为我们提供方便地访问附近的中风人口。 本计画的总体目标是开发一个可携式决策支援系统，以供临床医师诊断跌倒 即使患者不在诊所，也有风险。本研究的目的是：1）建立数字生物标志物， 从智能手机数据中提取，同时执行规定的ADL， 实验室环境中的低风险跌倒者。构建ML模型所需的其余数据全部收集在 参与者的家庭环境。参与者将在所有清醒的时间内将智能手机戴在腰间 并进行有规律的日常生活活动。2)为了训练三个ML模型，这些模型可以使用不同的 数据模态：使用i）被动收集的3天ADL数据，ii）来自规定的简单ADL任务的数据， 转身、行走和从坐到站，iii）主观和客观数据的组合。3)为了评估预测性 ML模型对六个月后实际跌倒发生率的有效性。 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 评估非卧床中风幸存者。识别中风后高跌倒风险的个体将允许 早期干预，以改善这些人的护理和生活质量。此外，这项研究有可能 用于开发一种可以跟踪中风康复过程的产品。

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.