Dataset for Monitoring and Visualizing Stroke Rehabilitation Progress using Wearable Sensors (IMU)

This dataset is associated with a manuscript that is currently under peer review. Article Abstract: Stroke is one of the leading causes of death and disability worldwide, and recovering mobility is an important goal during post-stroke rehabilitation. In this work, we present a study to verify the feasibility of monitoring and visualizing longitudinal stroke gait rehabilitation progress using wearable sensors. Wearable devices such as inertial measurement units (IMUs) are easy-to-use and cost-effective tools for quantifying mobility. However, there is a need for research on longitudinal monitoring of stroke rehabilitation progress with wearables, as well as generating clinically relevant insights using appropriate visualizations. To this aim, we recruited ten stroke patients in their early rehabilitation stage. We collected and analyzed the IMU-derived gait features across two visits, and presented visualizations of the foot movement trajectories as well as the spatio-temporal gait parameters in the average, symmetry, and variation domains to quantify changes in gait. Our visualization and quantification methods are evaluated and validated by clinical experts, and prove to be promising in aiding clinicians to monitor rehabilitation progression. Data description: The dataset consists data from ten stroke patients who completed both visits. The "raw" data folder contains tri-axial acceleration and angular velocity data from the IMUs. In addition, information about the participants such as demographics (e.g., body height and body weight), FAC scores at both visits, and evaluations of gait improvement are documented in the file "participant_info.csv". The “interim” folder contains IMU data that has been manually segmented to remove irrelevant movements before and after each walking session during a visit, based on visual inspection of raw IMU signals. For quality control, the segmented accelerometer and gyroscope data of each sensor were plotted, and the plots were saved in the same folder as the IMU signals. In addition, during the first execution of gait parameter extraction, calculated 3D feet trajectories were cached in the "interim" folder, so that for future executions, the cached trajectories can be loaded directly, reducing the computational efforts for re-calculation. The file "stance_magnitude_thresholds_manual.csv" documents the angular velocity thresholds used to identify stance phases for the gait analysis algorithm for each participant. The threshold values were determined manually by observing the angular velocity signals. The “processed” folder contains stride-by-stride spatio-temporal gait parameters extracted for each of the four walking conditions, and aggregated gait parameters in terms of coefficients of variation and symmetry for all walking conditions for each participant.本数据集与一篇目前处于同行评审阶段的手稿相关联。 文章摘要： 卒中是全球范围内致死与致残的主要病因之一，恢复运动能力是卒中后康复的核心目标。本研究旨在验证利用可穿戴传感器监测并可视化卒中患者步态康复纵向进展的可行性。惯性测量单元（Inertial Measurement Unit, IMU）等可穿戴设备是量化运动功能的便捷且高性价比工具，但目前仍需开展相关研究，探索通过可穿戴设备对卒中康复进展进行纵向监测，并借助恰当的可视化手段生成具备临床参考价值的分析结论。为此，我们招募了10名处于早期康复阶段的卒中患者，在两次随访中采集并分析了基于IMU提取的步态特征，并通过可视化足部运动轨迹以及平均、对称性、变异性维度下的时空步态参数，以量化步态变化情况。本研究的可视化与量化方法已通过临床专家的评估与验证，结果表明其在辅助临床医师监测康复进展方面具有良好的应用前景。 数据描述： 本数据集包含完成两次随访的10名卒中患者的相关数据。"raw"文件夹中存储了IMU采集的三轴加速度与角速度原始数据。此外，参与者的人口统计学信息（如身高、体重）、两次随访的FAC评分以及步态改善评估结果均记录于"participant_info.csv"文件中。 "interim"文件夹中存储了经过手动分段处理的IMU数据：基于对原始IMU信号的目视检查，剔除了每次随访中步行时段前后的无关运动数据。为保障数据质量，我们对每个传感器的分段后加速度计与陀螺仪数据进行了绘图，并将图表与IMU信号一同存储于该文件夹中。此外，在首次执行步态参数提取流程时，计算得到的三维足部轨迹会被缓存至"interim"文件夹，以便后续执行时直接加载缓存的轨迹，减少重复计算的算力开销。文件"stance_magnitude_thresholds_manual.csv"记录了为每位患者的步态分析算法识别站立阶段所用的角速度阈值，该阈值通过观察角速度信号手动确定。 "processed"文件夹中存储了针对四种步行工况逐步提取的时空步态参数，以及每位患者所有步行工况下的变异系数与对称性维度的聚合步态参数。