Abstract

Abstract
The continuous monitoring of human gait would allow to more objectively verify the abnormalities that arise from the most common pathologies. Therefore, this manuscript proposes a real-time tool for human gait detection from lower trunk acceleration. The vertical acceleration signal was acquired through an IMU mounted on a waistband, a wearable device. The proposed algorithm was based on a finite state machine (FSM) which includes a set of suitable decision rules and the detection of Heel-Strike (HS), Foot-flat (FF), Toe-off (TO), Mid-Stance (MS) and Heel-strike (HS) events for each leg. Results involved 7 healthy subjects which had to walk 20 m three times with a comfortable speed. The results showed that the proposed algorithm detects in real-time all the mentioned events with a high accuracy and time-effectiveness character. Also, the adaptability of the algorithm has also been verified, being easily adapted to some gait conditions, such as for different speeds and slopes. Further, the developed tool is modular and therefore can easily be integrated in another robotic control system for gait rehabilitation. These findings suggest that the proposed tool is suitable for the real-time gait analysis in real-life activities. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
Obstructive sleep apnea (OSA) is a significant sleep problem with various clinical presentations that have not been formally characterized. This poses critical challenges for its recognition, resulting in missed or delayed diagnosis. Recently, cluster analysis has been used in different clinical domains, particularly within numeric variables. We applied an extension of k-means to be used in categorical variables: k-modes, to identify groups of OSA patients. Demographic, physical examination, clinical history, and comorbidities characterization variables (n=46) were collected from 318 patients; missing values were all imputed with k-nearest neighbors (k-NN). Feature selection, through Chi-square test, was executed and 17 variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 having an age between 65 and 90 years (54%), 78% of males, with the presence of diabetes and gastroesophageal reflux, and high OSA prevalence; Cluster 2 presented a lower percentage of OSA (46%), with middle-aged women without comorbidities, but with gastroesophageal reflux; and Cluster 3 was very similar to cluster 1, only differing in age (45-64) and comorbidities were not present. Our results suggest that there are different groups of OSA patients, creating the need to rethink the baseline characteristics of these patients before being sent to perform polysomnography (gold standard exam for diagnosis). © 2018 IEEE.

Abstract
This paper addresses the issue of calibration (or normalization) of macroarray (dotblot) images. It proposes 3 parameters for the evaluation of the impact on the recorded markers of under- and over-exposure during the experimental acquisition of dotblot images – volume (V), saturation (S) and apparent radius (R). These parameters were evaluated using 101 dotblot images obtained from 16 different experiments, with 404 control markers in total. A procedure to simulate the changes on markers by increasing and decreasing exposure times is also presented. This can be the basis of a normalization procedure for dot blot images, which would be an important improvement in the current laboratory image acquisition protocol, reducing the subjectivity both at the acquisition level and at the subsequent image analysis stage. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract

Abstract