Revisión del estado del arte sobre tendencias tecnológicas para el análisis del comportamiento y actividades humanas

The study of human behavior allows the knowledge about people's behaviors, behavior determined by multiple factors: cultural, social, psychological, genetic, religious, among others, which affect the relationships and interaction with the environment. The infinity of data in our lives and the search for behavioral patterns from that data has been an amazing work whose benefit is focused on the determined patterns and intelligent analysis that lead to new knowledge. A significant amount of resources from pattern recognition in human activities and daily life has had greater dominance in the management of mobility, health and wellness.The current paper presents a review of technologies for human behavior analysis and use as tools for diagnosis, assistance, for interaction in intelligent environments and assisted robotics applications. The main scope is to give an overview of the technological advances in the analysis of human behavior, activities of daily living and mobility, and the benefits obtained.El estudio del comportamiento humano permite el conocimiento sobre las conductas de las personas, conducta determinada por múltiples factores: culturales, sociales, psicológicos, genéticos, religiosos, entre otros; que inciden en las relaciones y la interacción con el entorno. La infinidad de datos en nuestras vidas y la búsqueda de patrones de comportamiento a partir de esos datos ha sido un trabajo asombroso cuyo provecho se centra en los patrones determinados y el análisis inteligente que conducen a nuevos conocimientos. Una cantidad significativa de recursos a partir del reconocimiento de patrones en las actividades humanas y de vida diaria ha tenido mayor dominio en la gestión de la movilidad, la salud y bienestar.El actual documento presenta una revisión de las tecnologías para el análisis del comportamiento humano y del uso como herramientas para el diagnóstico, asistencia, para la interacción en ambientes inteligentes y aplicaciones de robótica asistida. El alcance principal es dar una visión general de los avances tecnológicos en el análisis del comportamiento humano, actividades de la vida diaria y movilidad, y de los beneficios obtenidos

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition using Deep Convolutional Neural Networks

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train deep convolutional neural network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition using Deep Convolutional Neural Networks

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train deep convolutional neural network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy