Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO algorithm was tested for different resolutions of working area representations. In all cases, it was possible to find optimal or near-optimal minimum-length paths from the initial to final desired positions without collision with obstacles or wall-borders.

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. Co...