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.

As compared to 1977 potato acreage in Poland has decreased by about 1.4 million hectares and amounted to 958 thousand hectares in 2002. Potatoes are protected mainly against Leptinotarsa decemlineata Say, Phytophthora infestans and weeds. Number of chemical treatments against Colorado potato beetle fluctuated between 1.1. to 1. 7 per year, in average. In 1977-1987 mainly organophosphorous insecticides were applied, whereas in last years pyrethroids and nereistoxin were used. Chemical treatments against late blight fluctuated between 1.2 to 1.6 per year, in average. Observations performed in 1977-2002 showed that the application of contact fungicides considerably decreased. In weed control in potatoes only one treatment, in average, was performed using herbicides, mainly pre-emergence ones. Herbicides containing linuron and metribuzin were most frequently applied.