RMSSD-Based Risk Prediction Using Wearable Sensor 


Vol. 15,  No. 2, pp. 139-145, Feb.  2026
https://doi.org/10.3745/TKIPS.2026.15.2.139


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  Abstract

As the use of wearable devices continues to expand, research utilizing daily collected biosignals to assess autonomic nervous system mn activity, stress levels, and cardiovascular health has been actively conducted. This study proposes a model to predict the 24-hour-ahead value of RMSSD (Root Mean Square of Successive Differences), a key indicator of HRV (Heart Rate Variability), using heart rate and step count data obtained from wearable sensors. Public wearable datasets provided by the Jeju Free International City Development Center were used, and data from 95 adult participants were included in the analysis. Various statistical features were derived from heart rate and step count signals to construct the input variables, and 14 machine learning and time-series forecasting algorithms were compared. The results showed that before data augmentation, the XGBoost model achieved an RMSE of 8.69 and an MAE of 7.05, while after augmentation, the Random Forest model yielded the lowest prediction error with an RMSE of 8.65 and an MAE of 7.01. These findings demonstrate that wearable-derived biosignals can be effectively used to predict future RMSSD values and provide a foundation for developing personalized health management systems.

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[IEEE Style]

H. Kim, Y. Jeong, J. Kim, "RMSSD-Based Risk Prediction Using Wearable Sensor," The Transactions of the Korea Information Processing Society, vol. 15, no. 2, pp. 139-145, 2026. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.139.

[ACM Style]

Ha-Neul Kim, Young-Seob Jeong, and Jeung-Im Kim. 2026. RMSSD-Based Risk Prediction Using Wearable Sensor. The Transactions of the Korea Information Processing Society, 15, 2, (2026), 139-145. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.139.

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