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

项目摘要 中风后幸存者在中风后第一年跌倒的比例高达73%，而非卧床中风患者的跌倒发生率至少为70% 幸存者每年都会经历一次坠落。跌倒的有害影响包括严重受伤、增加发病率 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成本。大多数人有跌倒的风险 对中风后非卧床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，仅限于临床或实验室环境。早期识别后- 面临跌倒风险的中风幸存者对于制定及时的量身定做的干预措施以减少跌倒至关重要。 该项目旨在开发一个基于机器学习(ML)的步行跌倒风险评估工具 中风幸存者在日常生活活动中使用腰间佩戴的智能手机的惯性传感器数据。 这项努力将涉及研究生和本科生(UG)在项目的每个阶段，并暴露 在严谨的科学研究的多个方面。查普曼大学走在中风的前沿 康复和组织中风训练营(SBC)，这是一个每学期免费的康复计划。在……里面 此外，加州大学长滩分校的公益诊所将为我们提供与附近中风患者的便捷联系。 该项目的总体目标是开发一个便携式决策支持系统，供临床医生诊断跌倒 即使病人不在诊所，也要冒风险。这项研究的目的是1)确定数字生物标记物 在执行规定的ADL时从智能手机数据中提取的ADL在高和 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完全收集在 参与者的家庭设置。参与者将在所有清醒时间将智能手机佩戴在腰部 并进行有规律的日常生活活动。2)训练了三种不同的ML模型，它们可以对跌倒风险进行分类 数据形式：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，如 转身、走路和坐到站，iii)主客观数据相结合。3)评估预测性 六个月后，ML模型相对于实际秋季事件的有效性。 该项目的成功实施将通过准确的跌倒风险来加强中风的护理 对非卧床卒中幸存者的评估。识别中风后跌倒的高危人群将使 早期干预以改善这些人的护理和生活质量。此外，这项研究还具有潜在的 开发一种可以跟踪中风康复过程中进展的产品。

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.