Abstract

Abstract

Abstract
Due to the technological evolution on wearable devices, biosignals, such as inter-cardiac beat interval (RR) time series, are being captured in a non-controlled environment. These RR signals, derived from photoplethysmography (PPG), enable health status assessment in a more continuous, non-invasive, non-obstructive way, and fully integrated into the individual’s daily activity. However, PPG is vulnerable to motion artefacts, which can affect the accuracy of the estimated neurophysiological markers. This paper introduces a method for motion artefact characterization in terms of location and relative variation parameters obtained in different common daily activities. The approach takes into consideration interindividual variability. Data was analyzed throughout related-samples Friedman’s test, followed by pairwise comparison with Wilcoxon signed-rank tests with a Bonferroni correction. Results showed that movement, involving only arms, presents more variability in terms of the two analyzed parameters. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The use of robots in rehabilitation attempts an effective, compliant, and time-efficient gait recovery while adapting the assistance to the user's needs. Assist-as-needed strategies (AAN), such as adaptive impedance control, have been reported as prominent strategies to enable this recovery effects. This study proposes an interaction-based assist-as-needed impedance control strategy for an ankle robotic orthosis that adapts the robotic assistance by changing the Human-Robot interaction stiffness. The adaptability of the interaction stiffness allows the real-time passage from passive assistance to an active one, approaching AAN gait training. The interaction stiffness was successfully estimated by linear regression of the Human-Robot interaction torque vs angle trajectory curve. From the validation with seven able-bodied subjects, we verified the suitability of this adaptive impedance control for a more compliant, natural, and comfortable motion than the trajectory tracking control. Moreover, the proposed strategy considers the users' motion intention and encourages them to interact closely with the robotic device while guiding their ankle trajectory according to desired trajectories. These achievements contribute to AAN gait training.

Abstract
Text categorization is a supervised learning task which aims to assign labels to documents based on the predicted outcome suggested by a classifier trained on a set of labelled documents. The association of text classification to facilitate labelling reports/complaints in the economic and health related fields can have a tremendous impact in the speed at which these are processed, and therefore, lowering the required time to act upon these complaints and reports. In this work, we aim to classify complaints into the main 4 economic activities given by the Portuguese Economic and Food Safety Authority. We evaluate the classification performance of 9 algorithms (Complement Naïve Bayes, Bernoulli Naïve Bayes, Multinomial Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, AdaBoost and Logistic Regression) at different layers of text preprocessing. Results reveal high levels of accuracy, roughly around 85%. It was also observed that the linear classifiers (support vector machine and logistic regression) allowed us to obtain higher f1-measure values than the other classifiers in addition to the high accuracy values revealed. It was possible to conclude that the use of these algorithms is more adequate for the data selected, and that applying text classification methods can facilitate and help the complaints and reports processing which, in turn, leads to a swifter action by authorities in charge. Thus, relying on text classification of reports and complaints can have a positive influence in either economic crime prevention or in public health, in this case, by means of food-related inspections. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract