MoveSmart: A multi-modal dataset for ground reaction force estimation using Apple Watch and force plate data
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2025
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| _version_ | 1866902267427291136 |
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| author | Ghaffarzadeh, Parvin Chakraborty, Debarati Aslansefat, Koorosh Dostan, Ali Papadopoulos, Yiannis |
| author_facet | Ghaffarzadeh, Parvin Chakraborty, Debarati Aslansefat, Koorosh Dostan, Ali Papadopoulos, Yiannis |
| contents | <p>This dataset contains synchronised inertial sensor data from Apple Watch devices (wrist and waist, ≈100 Hz) and laboratory force plate ground reaction force (GRF) data (1000 Hz) collected from 10 healthy adults performing five activities: walking, jogging, running, heel drops, and step drops. A total of 757 validated trials are included, with 584 trials having complete trial ID matching enabling a validated 3-phase transfer learning analysis.</p> <p>The dataset enables research on wearable-based GRF estimation, biomechanical signal transfer, sensor placement effects, and machine learning model development. The validated 3-phase analysis demonstrates: (1) Waist→Force Plate baseline mapping (mean r = 0.550 ± 0.170), (2) Wrist→Waist transfer validation (mean r = 0.568 ± 0.234), and (3) Wrist→Force Plate deployment testing (mean r = 0.486 ± 0.243), with an excellent transfer learning gap of 0.038 across all activities. Overall deployment readiness is 56.8% (trials with r > 0.5), with running and jogging showing best performance.</p> <p>Data includes synchronised time-series, event metadata, trial matching manifest, quality flags, biomechanical validation results, and full analysis code. This dataset establishes a benchmark reference for wearable-based GRF estimation research and supports reproducibility and open science in biomechanics.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17376717 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | MoveSmart: A multi-modal dataset for ground reaction force estimation using Apple Watch and force plate data Ghaffarzadeh, Parvin Chakraborty, Debarati Aslansefat, Koorosh Dostan, Ali Papadopoulos, Yiannis ground reaction force, Apple Watch, wearable sensors, gait analysis, biomechanics, IMU, accelerometer, transfer learning, force plate, multi-modal dataset, external loading <p>This dataset contains synchronised inertial sensor data from Apple Watch devices (wrist and waist, ≈100 Hz) and laboratory force plate ground reaction force (GRF) data (1000 Hz) collected from 10 healthy adults performing five activities: walking, jogging, running, heel drops, and step drops. A total of 757 validated trials are included, with 584 trials having complete trial ID matching enabling a validated 3-phase transfer learning analysis.</p> <p>The dataset enables research on wearable-based GRF estimation, biomechanical signal transfer, sensor placement effects, and machine learning model development. The validated 3-phase analysis demonstrates: (1) Waist→Force Plate baseline mapping (mean r = 0.550 ± 0.170), (2) Wrist→Waist transfer validation (mean r = 0.568 ± 0.234), and (3) Wrist→Force Plate deployment testing (mean r = 0.486 ± 0.243), with an excellent transfer learning gap of 0.038 across all activities. Overall deployment readiness is 56.8% (trials with r > 0.5), with running and jogging showing best performance.</p> <p>Data includes synchronised time-series, event metadata, trial matching manifest, quality flags, biomechanical validation results, and full analysis code. This dataset establishes a benchmark reference for wearable-based GRF estimation research and supports reproducibility and open science in biomechanics.</p> |
| title | MoveSmart: A multi-modal dataset for ground reaction force estimation using Apple Watch and force plate data |
| topic | ground reaction force, Apple Watch, wearable sensors, gait analysis, biomechanics, IMU, accelerometer, transfer learning, force plate, multi-modal dataset, external loading |
| url | https://doi.org/10.5281/zenodo.17376717 |