Human Activity Recognition Data Analysis: History, Evolutions, and New Trends.

The Assisted Living Environments Research Area-AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems-ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

Machine Learning Applied to Datasets of Human Activity Recognition: Data Analysis in Health Care.

BACKGROUND: In order to remain active and productive, older adults with poor health require a combination of advanced methods of visual monitoring, optimization, pattern recognition, and learning, which provide safe and comfortable environments and serve as a tool to facilitate the work of family members and workers, both at home and in geriatric homes. Therefore, there is a need to develop technologies to provide these adults autonomy in indoor environments. OBJECTIVE: This study aimed to generate a prediction model of daily living activities through classification techniques and selection of characteristics in order to contribute to the development in this area of knowledge, especially in the field of health. Moreover, the study aimed to accurately monitor the activities of the elderly or people with disabilities. Technological developments allow predictive analysis of daily life activities, contributing to the identification of patterns in advance in order to improve the quality of life of the elderly. METHODS: The vanKasteren, CASAS Kyoto, and CASAS Aruba datasets were used to validate a predictive model capable of supporting the identification of activities in indoor environments. These datasets have some variation in terms of occupation and the number of daily living activities to be identified. RESULTS: Twelve classifiers were implemented, among which the following stand out: Classification via Regression, OneR, Attribute Selected, J48, Random SubSpace, RandomForest, RandomCommittee, Bagging, Random Tree, JRip, LMT, and REP Tree. The classifiers that show better results when identifying daily life activities are analyzed in the light of precision and recall quality metrics. For this specific experimentation, the Classification via Regression and OneR classifiers obtain the best results. CONCLUSION: The efficiency of the predictive model based on classification is concluded, showing the results of the two classifiers, i.e., Classification via Regression and OneR, with quality metrics higher than 90% even when the datasets vary in occupation and number of activities.

Analítica de datos: incidencia de la contaminación ambiental en la salud pública en Medellín (Colombia) / Data Analytics: incidence of air pollution on public health in Medellin, Colombia

RESUMEN Objetivo Analizar el impacto de la contaminación del aire por material particulado PM2,5 y su relación con el número de asistencias a entidades de salud por enfermedades respiratorias por medio de analítica de datos. Métodos Se analizaron datos del Área Metropolitana de Medellín, Colombia, ciudad ubicada en un valle estrecho densamente poblado e industrializado y que ha presentado episodios críticos de contaminación en los últimos años. Se analizaron tres fuentes de datos: datos meteorológicos aportados por el SIATA (Sistema de Alerta Temprana de Medellín y el Valle de Aburrá); datos de contaminación por material particulado PM2,5 aportados por SIATA; y reportes de los RIPS (Registros Individuales de Prestación de Servicios de Salud) aportados por la Secretaría de Salud. Resultados Se evidenció la relación entre la concentración de PM2,5 con las asistencias médicas por los diagnósticos de IRA, EPOC y asma. En un episodio crítico de contaminación por PM2,5, se encontraron los siguientes retardos en la atención médica: entre 0 y 2 días para el IRA, 0 y 7 días para el EPOC y 0 y 5 días para el asma. Discusión Se encontraron coeficientes de correlación que evidencian la asociación de la concentración de PM2,5 con las asistencias por los diagnósticos de IRA, EPOC y asma. La mayor correlación entre las tres morbilidades se presentó para el asma. La variable meteorológica de mayor correlación con la variable objetivo es la temperatura del aire para el caso de EPOC y asma. En el caso de IRA, la variable con mayor correlación es la velocidad del viento. Por otro lado, el día de la semana es una variable de gran importancia a la hora de realizar un estudio de atenciones por enfermedades.

ABSTRACT Objective To analyze the impact of air pollution by PM2,5 particulate matter and its relationship with the number of attendances to health entities for respiratory diseases through data analytics. Methods Data from the Metropolitan Area of Medellín, Colombia, a city located in a densely populated and industrialized narrow valley and that has presented critical episodes of contamination in recent years, were analyzed. Three data sources were analyzed: meteorological data provided by SIATA (Early Warning System of Medellín and the Aburra Valley), PM2,5 particulate matter contamination data provided by SIATA, and RIPS reports (Individual Registers for the Provision of Health Services) provided by the health department. Results The relationship between the concentration of PM2,5 and medical care for the diagnoses of ARI, COPD and asthma was evidenced. In a critical episode of PM2,5 contamination, the following delays in medical care were found: between 0-2 days for IRA, 0-7 days for COPD, and 0-5 days for asthma. Discussion Correlation coefficients were found that show the association of the concentration of PM2,5 with the attendances for the diagnoses of ARI, COPD, and asthma. The highest correlation between the three morbidities was found for asthma. The meteorological variable with the highest correlation with the objective variable is air temperature in the case of COPD and asthma. In the case of IRA, the variable with the highest correlation is wind speed. On the other hand, the day of the week is a variable of great importance when carrying out a study of care for diseases.

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review.

Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.

[Data Analytics: incidence of air pollution on public health in Medellin, Colombia]. / Analítica de datos: incidencia de la contaminación ambiental en la salud pública en Medellín (Colombia).

OBJECTIVE: To analyze the impact of air pollution by PM2,5 particulate matter and its relationship with the number of attendances to health entities for respiratory diseases through data analytics. METHODS: Data from the Metropolitan Area of Medellín, Colombia, a city located in a densely populated and industrialized narrow valley and that has presented critical episodes of contamination in recent years, were analyzed. Three data sources were analyzed: meteorological data provided by SIATA (Early Warning System of Medellín and the Aburra Valley), PM2,5 particulate matter contamination data provided by SIATA, and RIPS reports (Individual Registers for the Provision of Health Services) provided by the health department. RESULTS: The relationship between the concentration of PM2,5 and medical care for the diagnoses of ARI, COPD and asthma was evidenced. In a critical episode of PM2,5 contamination, the following delays in medical care were found: between 0-2 days for IRA, 0-7 days for COPD, and 0-5 days for asthma. DISCUSSION: Correlation coefficients were found that show the association of the concentration of PM2,5 with the attendances for the diagnoses of ARI, COPD, and asthma. The highest correlation between the three morbidities was found for asthma. The meteorological variable with the highest correlation with the objective variable is air temperature in the case of COPD and asthma. In the case of IRA, the variable with the highest correlation is wind speed. On the other hand, the day of the week is a variable of great importance when carrying out a study of care for diseases.

OBJETIVO: Analizar el impacto de la contaminación del aire por material particulado PM2,5 y su relación con el número de asistencias a entidades de salud por enfermedades respiratorias por medio de analítica de datos. MÉTODOS: Se analizaron datos del Área Metropolitana de Medellín, Colombia, ciudad ubicada en un valle estrecho densamente poblado e industrializado y que ha presentado episodios críticos de contaminación en los últimos años. Se analizaron tres fuentes de datos: datos meteorológicos aportados por el SIATA (Sistema de Alerta Temprana de Medellín y el Valle de Aburrá); datos de contaminación por material particulado PM2,5 aportados por SIATA; y reportes de los RIPS (Registros Individuales de Prestación de Servicios de Salud) aportados por la Secretaría de Salud. RESULTADOS: Se evidenció la relación entre la concentración de PM2,5 con las asistencias médicas por los diagnósticos de IRA, EPOC y asma. En un episodio crítico de contaminación por PM2,5, se encontraron los siguientes retardos en la atención médica: entre 0 y 2 días para el IRA, 0 y 7 días para el EPOC y 0 y 5 días para el asma. DISCUSIÓN: Se encontraron coeficientes de correlación que evidencian la asociación de la concentración de PM2,5 con las asistencias por los diagnósticos de IRA, EPOC y asma. La mayor correlación entre las tres morbilidades se presentó para el asma. La variable meteorológica de mayor correlación con la variable objetivo es la temperatura del aire para el caso de EPOC y asma. En el caso de IRA, la variable con mayor correlación es la velocidad del viento. Por otro lado, el día de la semana es una variable de gran importancia a la hora de realizar un estudio de atenciones por enfermedades.