用苹果手表数据估地反作用力，公开492组带真值的多模态数据。
A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors
- 采集10人五类动作的双腕/腰佩戴手表加速度数据与力板真值同步
- 395组完整三模态数据可支持跨传感器模型验证，峰值力重复性信度达0.871~0.990
- 数据含质量控制标记与分析脚本，适合做可复现的可穿戴生物力学研究
本文发布一个完全开放的多模态数据集，用于从消费级苹果手表传感器估计垂直地面反作用力（vGRF），并提供实验室力板作为真值。10名年龄26–41岁的健康成年人在行走、慢跑、跑步、脚跟下落和台阶下落五种活动中佩戴左右手腕及腰部的两块苹果手表。数据集包含492个经验证的试验，同步记录约100 Hz的惯性测量单元（IMU）数据与1000 Hz的力板垂直力（Force_Z）数据。数据发布内容包括原始与处理后的时间序列、试验级元数据、质量控制标志和机器可读数据字典。试验级匹配通过稳定标识符实现跨模态关联。其中395个为三模态完整数据（腕、腰、力板），支持跨传感器分析与可复现模型评估。数据质量通过三阶段跨传感器合理性与一致性框架、峰值vGRF重复性分析（组内相关系数0.871–0.990）以及力值范围与试验完整性系统检查进行表征。蒙特卡洛敏感性分析表明，基于相关性的验证指标对单样本时间偏移具有鲁棒性，且在IMU采样分辨率范围内。所有数据以CC BY 4.0协议发布，分析脚本随数据一同存档，并镜像于GitHub。该资源支持可穿戴生物力学的可复现研究、机器学习模型在vGRF估计中的基准测试，以及利用广泛可用的消费级可穿戴设备探究传感器位置影响。
原文摘要 · Abstract (English)
This Data Descriptor presents a fully open, multi-modal dataset for estimating vertical ground reaction force (vGRF) from consumer-grade Apple Watch sensors with laboratory force plate ground truth. Ten healthy adults aged 26--41 years performed five activities: walking, jogging, running, heel drops, and step drops, while wearing two Apple Watches positioned at the left wrist and waist. The dataset contains 492 validated trials with time-aligned inertial measurement unit (IMU) recordings (approximately 100 Hz) and force plate vGRF (Force\_Z, 1000 Hz). The release includes raw and processed time series, trial-level metadata, quality-control flags, and machine-readable data dictionaries. Trial-level matching manifests link recordings across modalities using stable identifiers. Of the 492 validated trials, 395 are triad-complete, containing wrist, waist, and force plate data, enabling cross-sensor analyses and reproducible model evaluation. Dataset quality is characterised through a three-phase cross-sensor plausibility and consistency framework, repeatability analysis of peak vGRF (intraclass correlation coefficient 0.871--0.990), and systematic checks of force ranges and trial completeness. Monte Carlo sensitivity analysis showed that correlation-based validation metrics were robust to single-sample timing perturbations at the IMU sampling resolution. All data are released under CC BY 4.0, with analysis scripts archived alongside the dataset and mirrored on GitHub. This resource supports reproducible research in wearable biomechanics, benchmarking of machine learning models for vGRF estimation, and investigation of sensor placement effects using widely available consumer wearables.
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