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.本数据集关联一篇目前处于同行评审阶段的手稿。 ### 论文摘要 脑卒中是全球范围内致死与致残的主要病因之一，恢复运动功能是卒中后康复阶段的重要目标。本研究旨在验证利用可穿戴传感器监测并可视化脑卒中患者纵向步态康复进程的可行性。惯性测量单元（Inertial Measurement Unit, IMU）等可穿戴设备是量化运动功能的易用且高性价比工具，但目前仍需开展针对可穿戴设备纵向监测卒中康复进程的相关研究，并通过恰当的可视化手段生成具有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脑卒中患者，在两次随访中采集并分析了基于IMU的步态特征，并从平均、对称性及变异性三个维度可视化展示了足部运动轨迹与时空步态参数，以量化步态变化。我们的可视化与量化方法经临床专家评估验证，在辅助临床医师监测康复进展方面展现出良好应用前景。 ### 数据说明 本数据集包含10名完成两次随访的脑卒中患者的相关数据。 「raw」文件夹存储了IMU采集的三轴加速度与角速度原始数据。此外，文件「participant_info.csv」记录了受试者的人口统计学信息（如身高、体重）、两次随访的功能性步行分类（Functional Ambulation Category, FAC）评分以及步态改善情况的评估结果。 「interim」文件夹存储了经手动分段的IMU数据：基于对原始IMU信号的目视检查，移除了每次随访中步行时段前后的无关运动数据。为开展质量控制，我们对每个传感器的分段加速度计与陀螺仪数据进行了绘图，并将图像与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时，计算得到的三维足部轨迹被缓存至「interim」文件夹中，后续执行时可直接加载缓存轨迹，以减少重复计算的算力开销。文件「stance_magnitude_thresholds_manual.csv」记录了为每位受试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，以及每位受试者所有步行工况下的变异系数与对称性相关的聚合步态参数。