构建11类日常力量活动的可穿戴传感器数据集
StrengthSense: A Dataset of IMU Signals Capturing Everyday Strength-Demanding Activities
- 采集29人10个部位的惯性信号，覆盖多种力量动作
- 通过视频标注验证，确保动作数据准确可靠
- 适合运动健康监测与动作识别算法研究者使用
利用可穿戴传感器如惯性测量单元（IMU）追踪力量需求型活动，对监测肌肉力量、耐力和功率至关重要。然而，当前缺乏全面捕捉此类活动的数据集。为填补这一空白，我们推出开源数据集StrengthSense，包含11种力量需求型活动（如坐站转换、爬楼梯、拖地）的IMU信号，并附带2种非力量型活动作为对比。数据来自29名健康受试者，采用10个贴附于四肢及躯干的IMU采集，通过视频记录进行标注。本文详述数据采集流程、预处理方法及技术验证过程。我们通过比较IMU估算的关节角度与视频直接提取的角度，验证了传感器数据的准确性与可靠性。研究人员可基于StrengthSense开发人体动作识别算法，推进健身与健康监测应用。
原文摘要 · Abstract (English)
Tracking strength-demanding activities with wearable sensors like IMUs is crucial for monitoring muscular strength, endurance, and power. However, there is a lack of comprehensive datasets capturing these activities. To fill this gap, we introduce \textit{StrengthSense}, an open dataset that encompasses IMU signals capturing 11 strength-demanding activities, such as sit-to-stand, climbing stairs, and mopping. For comparative purposes, the dataset also includes 2 non-strength demanding activities. The dataset was collected from 29 healthy subjects utilizing 10 IMUs placed on limbs and the torso, and was annotated using video recordings as references. This paper provides a comprehensive overview of the data collection, pre-processing, and technical validation. We conducted a comparative analysis between the joint angles estimated by IMUs and those directly extracted from video to verify the accuracy and reliability of the sensor data. Researchers and developers can utilize \textit{StrengthSense} to advance the development of human activity recognition algorithms, create fitness and health monitoring applications, and more.
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