TY - JOUR N2 - The objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant’s everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error. L1 - http://www.journals.pan.pl/Content/116296/PDF/10D_255-261_01354_Bpast.No.68-2_19.04.20_KA_SS.pdf L2 - http://www.journals.pan.pl/Content/116296 PY - 2020 IS - No. 2 (i.a. Special Section on Computational Intelligence in Communications) EP - 261 DO - 10.24425/bpasts.2020.133118 KW - data imputation KW - spline interpolation KW - linear interpolation KW - HRV KW - IoT A1 - Tlija, A. A1 - Węgrzyn-Wolska, K. A1 - Istrate, D. VL - 68 DA - 30.04.2020 T1 - Missing-data imputation using wearable sensors in heart rate variability SP - 255 UR - http://www.journals.pan.pl/dlibra/publication/edition/116296 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -