Sensor-Based Dataset for Knee Joint Mobility and Rehabilitation Monitoring During Climbing and Walking Activities with Extracted Features

The Sensor-Based Dataset for Knee Joint Mobility and Rehabilitation Monitoring includes four distinct files, each serving a specific role in analyzing knee function during movement. The Walking Data file holds raw sensor readings collected during regular and brisk walking, helping to assess gait characteristics and joint mobility. The Climbing Data captures sensor output from stair-climbing tasks, offering valuable insights into knee performance under increased physical effort.Data is collected from 150 persons for both the activities for the duration of 2 minutes. To improve model training, the Augmentation Data contains artificially varied versions of the original signals created through methods like time shifting and noise injection. Lastly, the Feature Extraction Data provides a structured collection of calculated metrics such as minimum value, maximum value,mean,variance,skewness and kurtosis, which are useful for evaluating movement patterns and supporting further analysis in rehabilitation contexts. Together, these files form a robust foundation for research and development in wearable health monitoring systems.

基于传感器的膝关节活动度与康复监测数据集（Sensor-Based Dataset for Knee Joint Mobility and Rehabilitation Monitoring）包含四个独立文件，各文件在分析运动状态下的膝关节功能时各有专属作用。行走数据（Walking Data）存储了常规行走与快步走场景下采集的原始传感器读数，可用于评估步态特征与关节活动度。爬楼数据（Climbing Data）采集了爬楼梯任务中的传感器输出，能够为解析高强度运动下的膝关节表现提供极具价值的参考依据。两类活动的数据均采集自150名受试者，单类活动的采集时长为2分钟。为优化模型训练流程，增强数据（Augmentation Data）包含通过时移、噪声注入等方法生成的原始信号人工变体。特征提取数据（Feature Extraction Data）提供了结构化的计算指标集合，涵盖最小值、最大值、均值、方差、偏度与峰度，此类指标可用于评估运动模式，为康复领域的后续分析提供有力支撑。上述四类文件共同构成了可穿戴健康监测系统领域研发与研究的坚实基础。