An analysis and proposal for the work of social workers in psychopedagogical services in schools in the Valencian Autonomous Community

Los Trabajadores Sociales de Educación de la provincia de Alicante nos planteamos la necesidad de actualizar las funciones que la normativa de la, denominada en su día, Conselleria de Educación y Ciencia nos marca, dentro de los Servicios Psicopedagógicos Escolares (SPE) y Gabinetes Psicopedagógicos Municipales (GPM) de la Comunidad Autónoma Valenciana. Los motivos vienen determinados por considerar una normativa obsoleta (Orden 10 de marzo de 1995), una realidad social y educativa cambiante que determinan nuevas demandas hacia el trabajador social de educación, y la configuración de un perfil profesional que nos diferencie de otros profesionales que también intervienen en el ámbito socio-educativo. Este documento es el resultado del trabajo realizado inicialmente por un grupo de trabajadores sociales de educación (a partir de ahora TRASO) de la Provincia de Alicante y consensuado, finalmente, en el I Encuentro de Trabajadores Sociales de Educación de la Comunidad Autónoma Valenciana en diciembre de 2016.As social workers in education in the province of Alicante we consider it necessary to update our roles as described in the regulations of the Regional Council of Education and Science, within the School Psychopedagogical Services and the Municipal Educational Psychopedagogical Services of the Autonomous Community of Valencia. We propose these changes because the regulations are outdated (1995), and because of the need to reflect the changing educational and social reality. In addition, the work of social workers in the educational field has changed, and social workers need their own professional profile that is differentiated from that of other professionals who work in the field of education. This document is the result of the work of a group of social workers in the educational field in the province of Alicante. It was approved by the first meeting of social workers in the educational field in the Autonomous Community of Valencia in December of 2016

Experimental sleep phases monitoring

Nowadays there is a rich diversity of sleep monitoring systems available on the market. They promise to offer information about sleep quality of the user by recording a limited number of vital signals, mainly heart rate and body movement. Typically, fitness trackers, smart watches, smart shirts, smartphone applications or patches do not provide access to the raw sensor data. Moreover, the sleep classification algorithm and the agreement ratio with the gold standard, polysomnography (PSG) are not disclosed. Some commercial systems record and store the data on the wearable device, but the user needs to transfer and import it into specialised software applications or return it to the doctor, for clinical evaluation of the data set. Thus an immediate feedback mechanism or the possibility of remote control and supervision are lacking. Furthermore, many such systems only distinguish between sleep and wake states, or between wake, light sleep and deep sleep. It is not always clear how these stages are mapped to the four known sleep stages: REM, NREM1, NREM2, NREM3-4. [1] The goal of this research is to find a reduced complexity method to process a minimum number of bio vital signals, while providing accurate sleep classification results. The model we propose offers remote control and real time supervision capabilities, by using Internet of Things (IoT) technology. This paper focuses on the data processing method and the sleep classification logic. The body sensor network representing our data acquisition system will be described in a separate publication. Our solution showed promising results and a good potential to overcome the limitations of existing products. Further improvements will be made and subjects with different age and health conditions will be tested

Sleep stages classification using vital signals recordings

To evaluate the quality of a person´s sleep it is essential to identify the sleep stages and their durations. Currently, the gold standard in terms of sleep analysis is overnight polysomnography (PSG), during which several techniques like EEG (eletroencephalogram), EOG (electrooculogram), EMG (electromyogram), ECG (electrocardiogram), SpO2 (blood oxygen saturation) and for example respiratory airflow and respiratory effort are recorded. These expensive and complex procedures, applied in sleep laboratories, are invasive and unfamiliar for the subjects and it is a reason why it might have an impact on the recorded data. These are the main reasons why low-cost home diagnostic systems are likely to be advantageous. Their aim is to reach a larger population by reducing the number of parameters recorded. Nowadays, many wearable devices promise to measure sleep quality using only the ECG and body-movement signals. This work presents an android application developed in order to proof the accuracy of an algorithm published in the sleep literature. The algorithm uses ECG and body movement recordings to estimate sleep stages. The pre-recorded signals fed into the algorithm have been taken from physionet1 online database. The obtained results have been compared with those of the standard method used in PSG. The mean agreement ratios between the sleep stages REM, Wake, NREM-1, NREM-2 and NREM-3 were 38.1%, 14%, 16%, 75% and 54.3%

Personal recommendation system for improving sleep quality

Sleep is an important aspect in life of every human being. The average sleep duration for an adult is approximately 7 h per day. Sleep is necessary to regenerate physical and psychological state of a human. A bad sleep quality has a major impact on the health status and can lead to different diseases. In this paper an approach will be presented, which uses a long-term monitoring of vital data gathered by a body sensor during the day and the night supported by mobile application connected to an analyzing system, to estimate sleep quality of its user as well as give recommendations to improve it in real-time. Actimetry and historical data will be used to improve the individual recommendations, based on common techniques used in the area of machine learning and big data analysis

Heart rate spectrum analysis for sleep quality detection

To evaluate the quality of sleep, it is important to determine how much time was spent in each sleep stage during the night. The gold standard in this domain is an overnight polysomnography (PSG). But the recording of the necessary electrophysiological signals is extensive and complex and the environment of the sleep laboratory, which is unfamiliar to the patient, might lead to distorted results. In this paper, a sleep stage detection algorithm is proposed that uses only the heart rate signal, derived from electrocardiogram (ECG), as a discriminator. This would make it possible for sleep analysis to be performed at home, saving a lot of effort and money. From the heart rate, using the fast Fourier transformation (FFT), three parameters were calculated in order to distinguish between the different sleep stages. ECG data along with a hypnogram scored by professionals was used from Physionet database, making it easy to compare the results. With an agreement rate of 41.3%, this approach is a good foundation for future research