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Sweat Loss Estimation Algorithm for Smartwatches
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AI
DOI：10.1109/ACCESS.2023.3253384.png)
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Abstract
En 中文
This study presents a newly released algorithm for smartwatches - Sweat loss estimation for running activities. A machine learning model (polynomial Kernel Ridge Regression) is used to estimate the sweat loss in milliliters. A clinical dataset of 748 running tests of 568 people was collected and used for training / validation. The data presents a diversity of factors playing an important role in sweat loss: anthropometric parameters of users, distance, ambient temperature and humidity. The data augmentation technique was implemented. One of the key points of the algorithm is an accelerometer-based model for running distance estimation. The model we developed has a mean absolute percentage error (MAPE) = 7.7% and a coefficient of determination (R2) = 0.95 (at distances in the range of 2-20 km). The performance of the fully automatic sweat loss estimation algorithm provides an average root mean square error (RMSE) = 236 ml; more fundamentally, health-related parameter body weight percentage RMSE (RMSEBWP) = 0.33% and R2 = 0.79. To the best of the authors' knowledge, the algorithm provides the best performance of any existing solution or described in the literature.
Keywords:
Wearable computers
Sensors
Temperature distribution
Prediction algorithms
Maximum likelihood estimation
Heating systems
Skin
IMU
PPG
fitness
running
sensors
skin temperature
smartwatch
sweat loss estimation
wearables
wrist-wearable device