A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors

Abstract

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.

Figures (7)

• Figure 1: Dataset folder structure and organisation. Hierarchical organisation showing 10 participant subfolders (P1--P10), each containing time-aligned IMU sensor files for wrist and waist positions across five activities. Each trial is identified by a unique UUID enabling precise cross-sensor matching. The alignment pipeline produced 598 aligned outputs from 678 matched triplets (pre-curation). The released dataset comprises 492 validated trials documented in trial_manifest.csv (395 triad-complete).
• Figure 2: Trial distribution overview. (A) Per-participant trial counts for all 492 validated trials. (B) Activity-specific trial counts showing triad-complete (all sensors) versus total validated trials. The dataset contains 395 triad-complete trials (80.3%) suitable for comprehensive cross-sensor analyses.
• Figure 3: Figure 3. Data collection setup and representative time-aligned signals. Left: (A) Experimental setup showing the waist-worn Apple Watch secured with an elastic belt at the anterior waist (below the navel) and the wrist-worn device on the left wrist; (B) participant standing on the 20 cm step platform used for step-drop trials; (C) MyCoreMotion data collection interface; (D) processing pipeline overview. Right: Representative time-aligned signals showing vertical ground reaction force from the force plate (Force_Z, black), waist acceleration (blue), and wrist acceleration (red). Vertical lines indicate initial contact ($>$50 N). Walking shows a double-peak pattern, running a single higher-amplitude peak, and drop tasks a rapid high-force onset ($\sim$2.5--4.0$\times$ body weight).
• Figure 4: Time-alignment sensitivity overview ($|\text{baseline }r| \geq 0.2$). (A) Distribution of per-comparison mean $|\Delta r|$ by validation phase. Violin plots show kernel density estimates; box plots show median, interquartile range, and outliers. (B) Relationship between baseline correlation and sensitivity. Vertical dashed lines mark the $|r| = 0.2$ threshold; the horizontal dotted line indicates a sensitivity threshold of 0.05.
• Figure 5: Empirical cumulative distribution of sensitivity by phase ($|\text{baseline }r| \geq 0.2$). Vertical dashed lines mark thresholds of 0.03 and 0.05.