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• W4384785369 abstract "Mood disorders are becoming more frequent and, especially after the pandemic years, the importance of analysing and preventing such disorders has become clear. In the worst cases, people can suffer from depression or bipolar disorder leading to hospitalization or sick leave with serious economic and social consequences for the individual and their environment. Thanks to the development of technology, there are increasingly useful portable devices for monitoring individual daily activity. The data collected is very useful for understanding not only the individual's environment but also for characterizing their emotional profile. However, good monitoring requires the use of a diverse set of information sources. Medical consultations are the traditional source of information but in many cases this information is lacking or insufficient. The new sources of information are diverse: smart devices such as smart watches or even the mobile phone itself, portable sensors of various types and even activity records on social networks. All these data can be integrated and processed in such a way that a characterization profile of behaviour related to the emotional state of the individual is determined. However, the manufacturers of these devices apply aggregation algorithms to the monitored data to provide the client with a friendlier and easier to interpret version, but they do not usually provide the raw data collected by the sensors (accelerometer, gyroscope, temperature, etc.). There is still no regulated standardization that obliges the manufacturer to provide the data to the owners of the devices. Both raw data and aggregation algorithms work like a black box in most cases. The information presented by the device applications (generally apps) is very relevant and the interpretation of the users has direct consequences on their behaviour (behaviour modification, medication administration, etc.). For this reason, it is essential to verify the algorithms used in the previous process, guaranteeing that the information integrated (and presented) really corresponds to the information collected by the sensors. In this paper we present a suitable system for the verification of aggregated data from personal activity monitoring sensors. The system includes a parsing algorithm that makes the data structure and relates it to the output. The effectiveness of the algorithm has been tested with real data over a period of two years and for both daytime activity and sleep quality monitoring. The algorithm is perfectly scalable to be used on any device, so the computer system presented can be useful for future computer auditing of this type of process." @default.
• W4384785369 created "2023-07-20" @default.
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• W4384785369 date "2023-01-01" @default.
• W4384785369 modified "2023-09-26" @default.
• W4384785369 title "Verification of the integration process of algorithms and sensor data for mental health applications" @default.
• W4384785369 doi "https://doi.org/10.54941/ahfe1004158" @default.
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