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

Mining user activity as a context source for search and retrieval

Nowadays in information retrieval it is generally accepted that if we can better understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval

Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.Comment: submitted to the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON

Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier

Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smart-phones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.Comment: Pre-print of the accepted version for the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 201