Accelerometer and GPS sensor combination based system for human activity recognition

On the Personalization of Classification Models for Human Activity Recognition

“…due to their wide diffusion among the population and to the presence of various types of sensors integrated in the devices (e.g., accelerometer, gyroscope, orientation, and GPS). HAR techniques based on signals from sensors of wearable devices have been proposed for several application domains: sport tracking uses signals such as GPS and accelerometer for evaluating the activity of the user [8], detection of falls exploits wearable devices preventing mortality of elder people [5], [7], behavior analysis, based on physical measurements, prevents dementia diseases [9], and many others. Most of these HAR techniques rely on accelerometers because they have low power consumption and permit continuous sensing over a complete day [10].…”

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

“…They implemented the J48 decision tree, bagging, decision table, and Naïve Bayes methods, reporting an accuracy of 94% [38].In [39], the authors implemented a decision tree classifier with several features, such as mean, median, maximum, minimum, RMS, standard deviation, median deviation, interquartile range, energy, entropy, skewness, and kurtosis of the accelerometer data, for the recognition of running, walking, standing, sitting, and laying activities with a reported accuracy of 99.5%.In [40], the authors implemented a SVM method with several features, such as RMS, variance, correlation and energy of the accelerometer data, for the recognition of walking, running, cycling, and hopping with a reported average accuracy of 97.69%.The authors of [41] implemented an ANN with mean, standard deviation, and percentiles of the magnitude of the accelerometer data as features, with a reported accuracy of 92% in the recognition of standing, walking, running, going up stairs, going down stairs, and running.In addition, the authors of [42] implemented an ANN with some features, such as mean, maximum, minimum, difference between maximum and minimum, standard deviation, RMS, Parseval's Energy, correlation between axis, kurtosis, skewness, ratio of the maximum and minimum values in the FFT, difference between the maximum and minimum values in the FFT, median of peaks, median of troughs, number of peaks, number of troughs, average distance between two consecutive peaks, average distance between two consecutive troughs, and ratio of the average values of peaks and troughs based on a window of the accelerometer data. The activities recognized by the method are resting, walking, cycling, jogging, running, and driving [42], reporting an accuracy between 57.53% to 97.58%.The authors of [43] implemented a SVN method with mean, minimum, maximum, standard deviation, energy, mean absolute deviation, binned distribution, and percentiles of the magnitude of acceleration as features, with a reported overall accuracy of 94.3% in the recognition of running, staying, walking, going up stairs, and going down stairs.In [44], a method that combines the J48 decision tree, MLP, and Likelihood Ratio (LR) models was implemented and it uses the accelerometer data for the extraction of the minimum, maximum, mean, standard deviation, and zero crossing rate for each axis, and the correlation between axis for the application in the model, in order to recognize the going down stairs, jogging, sitting, standing, going up stairs, and walking activities with a reported accuracy of 97%.The SVM method is also implemented with accelerometer and Global Positioning System (GPS) data for the recognition of walking, standing, and running activities, but the accelerometer features used are minimum, maximum, mean, standard deviation, correlation, and median crossing [68], reporting an accuracy of 97.51%.Another study that uses SVM method makes use of accelerometer, gyroscope, and barometer sensors for the identification of walking, going up stairs, going down stairs, standing, going elevator up, and going elevator down, extraction the mean, mean of 1 st half, mean of 2 nd half, difference of means, slope, variance, standard deviation, RMS, and Signal Magnitude Area [69], reporting an accuracy between 87.45% and 99.25%.The SVM method is also used with the accelerometer, the gyroscope, the barometer, and the GPS sensors...…”

Detectando aplicaciones maliciosas en Smartphone con sistema Android a través del uso de una aplicación

“…Entre las técnicas utilizadas se encuentran: 1) Hooking, que se basa en la inyección de código, para la detección y tratamiento de aplicaciones maliciosas (Tinajero-Manjarez et al, 2017); 2) Máquinas de Soporte Vectorial (Support Vector Machines, por sus siglas en inglés SVM), utilizadas para clasificar aplicaciones desconocidas como maliciosas o benignas y en la detección de actividad de los usuarios, mencionadas en (Kaghyan & Sarukhanyan, 2013) y (Spreitzenbarth et al, 2015); 3) Redes Neuronales para localización en interiores y exteriores, aplicado a tráfico y demografía, descritas en (Su et al, 2014), (Huang et al, 2014), (Paul & George, 2015), (Mugagga & Winberg, 2015), (Mugagga & Winberg, 2015), (Carlos E. Galván-Tejada et al, 2015), y (Dou et al, 2015); 4) K-Means y Local Outlier Factor (por sus siglas en inglés LOF) para detección de anomalías, utilizado en dominios como: medicina, detección de intrusos y fraude, abordadas en (Karim et al, 2014) y (Pasillas D. & Ratté, 2016); 5) Árboles de Decisión, Redes Bayesianas y Regresión, en cómputo forense y para la localización y detección de actividad utilizado en la detección de acoso escolar o bullying, analizadas en (Moço & Lobato-Correira, 2014) y (Garcia-Ceja et al, 2014), y; 6) Big Data y Autómatas Celulares para localización en interiores/exteriores y demografía, cubierta en (Tosi et al, 2014), (Asri et al, 2015), y análisis de comportamiento humano en entornos sociales (Human Social Behavior, por sus siglas en inglés HSB), estudiado en (More & Lingam, 2015) y (Dou et al, 2015).…”