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Sensor data validation is an important process executed during the data acquisition and data processing modules of the multisensor mobile system.
This process consists of the validation of the external conditions of the data and the validity of the data for specific purpose, in order to obtain accurate and reliable results.
The characteristics of the sensors are also important for the selection of the best techniques, which may be separated in three large groups, which are sensor performance characteristics, pervasive metrics, and environmental characteristics .
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 .
Data validation techniques are commonly composed by statistical methods.
Due to the characteristics of mobile devices, data validation techniques can be executed locally in the mobile device or at the server-side, depending on the amount of data to validate simultaneously, the frequency of the validation tasks, and the computational, communication, and storage resources needed for the validation.
On the one hand, simple test based methods include different techniques, such as physical range check, local realistic range detection, detection of gaps in the data, constant value detection, the signals’ gradient test, the tolerance band method, and the material redundancy detection [7, 10, 11].
Therefore, very often the data captured or processed has to be cleaned, treated, or imputed to obtain better and reliable results.To mitigate this problem, this paper presents and discusses the most used data validation algorithms and techniques and their usage in a mobile application that relies on the sensors’ data to give meaningful output to its user.The algorithms are listed and their use is discussed.Moreover, the use of the sensors’ data to feed higher-level algorithms needs to guarantee a minimum degree of error, with this error being the difference between the output of these applications, built on limited computational mobile platforms, and the output of a golden standard.
To achieve a minimum degree of error, statistical methods need to be applied to ensure that the output of the mobile application is to maximum extent similar to the output given by the relevant golden standard, if and when this is possible.
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.