2025-10-21 ハーバード大学
Web要約 の発言:
<関連情報>
- https://seas.harvard.edu/news/2025/10/leg-better-running-data
- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0330042
地上走行中の実験室への出入り時のブレーキ力と推進力を推定する Estimating braking and propulsion forces during overground running in and out of the lab
Lauren M. Baker,Fabian C. Weigend ,Krithika Swaminathan ,Daekyum Kim,Andrew Chin,Daniel E. Lieberman,Conor J. Walsh
PLOS One Published: September 4, 2025
DOI:https://doi.org/10.1371/journal.pone.0330042
Abstract
Accurately estimating kinetic metrics, such as braking and propulsion forces, in real-world running environments enhances our understanding of performance, fatigue, and injury. Wearable inertial measurement units (IMUs) offer a potential solution to estimate kinetic metrics outside the lab when combined with machine learning. However, current IMU-based kinetic estimation models are trained and evaluated within a single environment, often on lab treadmills. The transferability of these treadmill-trained models during overground running in and out of the lab is underexplored, and the individualization and validation of such models remain a challenge. Toward bridging this gap, we trained a generalized model on treadmill data of 15 recreational runners and evaluated braking and propulsion force estimates during overground running in and out of the lab. We explored fine-tuning with individual data from lab-based overground running to quantify model performance improvements with individualization. The generalized and fine-tuned models were extrapolated to outdoor running for a subset of five participants, and estimates were compared to lab-based overground measurements. Evaluating the generalized model with a leave-one-out cross validation yielded overground braking and propulsion force root mean squared error of 4.3 ± 1.1 % bodyweight (%BW). Fine-tuning this model with eight strides reduced error to 2.6 ± 0.5 %BW. Outdoor force predictions from the fine-tuned model better aligned with expected linear trends between braking/propulsion impulses and speed than the generalized model. These results provide insights into the accuracy and applicability of IMU data-driven models for braking and propulsion estimation during overground running, facilitating the development of practical, individualized biomechanical analysis tools for real-world use.
