Many of these applications are designed and developed by amateur programmers, and that in itself is good as it confirms an increase in the overall set of skills of the developer community.
Nevertheless, and even when the applications are developed by professionals or by companies, there are not many applications that publicize or disclose how the sensors’ data is processed.
One of the causes for the presence of incorrect values during the data acquisition process may be existence of environmental noise.
Even when the data is correctly collected, the data may still be incorrect because of noise.
The recording of sensor data and the sequent processing of this data need to include validation subtasks that guarantee that the data are suitable to be fed into the higher-level algorithms.
The sequence of this validation may be applied not only in data acquisition but also in data processing since increase, as these increase the degree of confidence of the systems, with the confidence in the output being of great importance, especially for systems involved in medical diagnosis, but also for the identification of ADLs or sports monitoring.While data validation is important for improving the reliability of a system, it also depends on other factors, such as power instability, temperature changes, out-of-range data, internal and external noises, and synchronization problems that occur when multiple sensors are integrated into a system .However, the reconstruction of the data and correction for the correct measurement is also important, and several research studies have proposed systems, methods, models, and frameworks to improve the data validation and reconstruction [3, 4].The end result is to provide a compact generic description of the quality of a measurement to the controller, with which decisions as to how to use the measurement can be made.This paper proposes the use of SEVA principles in the interpretation of data from biomedical instrumentation, in order to aid the decision-making process, particularly in critical care.