Worldwide, chronic pain has established as one of the foremost medical issues due to its 35% of comorbidity with depression and many other psychological problems. Traditionally, self-report (VAS scale) or physicist inspection (OPI scale) perform the pain assessment; nonetheless, both methods do not usually coincide [14]. Regarding self-assessment, several patients are not able to complete it objectively, like young children or patients with limited expression abilities. The lack of objectivity in the metrics draws the main problem of the clinical analysis of pain. In response, various efforts have tried concerning the inclusion of objective metrics, among which stand out the Prkachin and Solomon Pain Intensity (PSPI) metric defined by face appearance [5]. This work presents an in-depth learning approach to pain recognition considering deep facial representations and sequence analysis. Contrasting current state-of-the-art deep learning techniques, we correct rigid deformations caught since registration. A preprocessing stage is applied, which includes facial frontalization to untangle facial representations from non-affine transformations, perspective deformations, and outside noises passed since registration. After dealing with unbalanced data, we fine-tune a CNN from a pre-trained model to extract facial features, and then a multilayer RNN exploits temporal relation between video frames. As a result, we overcome state-of-the-art in terms of average accuracy at frames level (80.44%) and sequence level (84.54%) in the UNBC-McMaster Shoulder Pain Expression Archive Database.

Currently, IoT has evolved to such an extent to extend to all corners of each place through devices that are connected to a network and generate information. In most cases, to be processed for a specific purpose or storage as historical data, an IoT ecosystem is implemented to manage those tasks between different devices, frameworks, or applications. Besides, more complex IoT ecosystems require more complex architecture to manage the information flow; at this level, we found a problem called interoperability. This problem is not limited to the compatibility of adding/removing devices to an ecosystem; it is also expected that the information generated by devices and processed complies with a standard optimizing the data transmission. In this work, we present a new software architecture pattern to avoid the problem of interoperability through the process of exchange information between devices and prevent and store heterogeneous information coming from different layers of an IoT ecosystem.

Big data visualization is a main task for data analysis. Due to its complexity in terms of volume and variety, very large datasets are unable to be queried for similarities among entries in traditional Database Management Systems. In this paper, we propose an effective approach for indexing millions of elements with the purpose of performing single and multiple visual similarity queries on multidimensional data associated with geographical locations. Our approach makes use of Z-Curve algorithm to map into 1D space considering similarities between data. Additionally, we present a set of results using real data of different sources and we analyze the insights obtained from the interactive exploration.