Compensation of Measurement Uncertainty in a Remote Fetal Monitor

Abstract

The perinatal mortality rate is very high throughout the world. A fetal monitor may be used remotely, and this would tackle the problem of continuous monitoring of high-risk pregnancies. There is evidence that current technology is of low reliability, and, therefore, of low precision to identify fetal health. In medical technological implementation, a safe, efficient, and reliable operation must be guaranteed, and the main problem is that remote fetal monitor gathers just a few samples, so the hypothesis of classical theory is not met. We are proposing an approach that improves the data’s lack of reliability that accompanies the use of a remote fetal monitor. The method refers to how, by using the existing technologies and the initial experimental data, it is possible to apply probabilistic models that are truly representative of each application. This leads to the characterization of properties of the statistics used to generate a representative probabilistic model without the need to consider the hard suppositions. Results show that, for every case study, it was possible to improve estimations of measurement uncertainty. The proposed method is a useful tool to increase the reliability of medical technology, especially for pieces of equipment where a health care professional is not available.

Special Issue on “Human Health Engineering Volume II”

“…The 12 presented papers in this second volume are grouped according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing of physiological variables [5][6][7][8], papers describing health monitoring applications [9][10][11][12], and finally, examples of control applications for human health [13][14][15][16]. As indicated by Aerts [1], it is envisioned that the field of human health engineering is complementary to the field of biomedical engineering as it not only contributes to developing technology for curing patients or supporting chronically ill people, but also covers applications on healthy humans (e.g., sports, sleep, and stress) and thus also focuses on disease prevention and optimizing human well-being more generally.…”

“…The first series of articles in this SI describes methods for (improved) measuring and/or sensing of health-related physiological signals. The work of Arciniega-Montiel et al [5] contributes to developing methods for improving the measuring reliability of fetal monitors, used in the case of high-risk pregnancies, on the basis of probabilistic models. Al-Halhouli et al [6] demonstrated the feasibility of using a wearable and stretchable inkjet-printed strain gauge sensor for estimating respiratory rate, which is a key vital sign variable in many medical and health applications.…”