No abstract

The research programme reported in this paper is set within the framework of our research under the theme of ICT support for Active Healthy Ageing (AHA). This longitudinal empirical research is focused on the study of the impact on the management of cardiovascular disease if supported by sustained health monitoring using wearable connected devices. One of the key objectives is stress monitoring and its classification during the daily routine of life thus enabling psycho-physiological monitoring to study the correlation between emotional states including variable stress levels and the evolution and prognosis of cardiovascular disease. In this paper, the calibration phase will be studied in order to distinguish between two emotional states: i) meditation and ii) stress condition. For this, the Heart Rate Variability (HRV) features are used as extracted from the RR interval and a support vectors machine (SVM) classifier deployed which resulted in 74% and 87% recognition accuracy based on HRV data for the recognition of the two emotional states, namely meditative, and, stressed, respectively. The main objective is to prevent Cardio-Vascular Disease (CVD) in healthy people and to treat those who already suffer from it. Creating a reference database was our first step in this research project. The sensor choice was made based on doctors' recommendations. The work methodology was as follows: first validate the « objective data » issuing from the calibration state. Second, set up the automatic algorithm and detect automatically the patient's emotional states during the experimentation period (subjective data). Third analyse the physical activities correlated to the blood pressure and emotions. This study has involved the challenge of distinguishing the influence of stress versus relaxation on the Cardio-Vascular function and in particular on the risk of exacerbation of pre-existing Cardio-Vascular Disease.

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.