Abstract
Background and Objectives: Early detection of cognitive impairment is crucial for timely clinical interventions aimed at delaying progression to dementia. However, existing screening tools are not ideal for wide population screening. This study explores the potential of combining machine learning, specifically, one-class classification, with simpler and quicker motor-cognitive tasks to improve the early detection of cognitive impairment. Methods: We gathered data on gait, fingertapping, cognitive, and dual tasks from older adults with mild cognitive impairment and healthy controls. Using one-class classification, we modeled the behavior of the majority group (healthy controls), identifying deviations from this behavior as abnormal. To account for confounding effects, we integrated confound regression into the classification pipeline. We evaluated the performance of individual tasks, as well as the combination of features (early fusion) and models (late fusion). Additionally, we compared the results with those from two-class classification and a standard cognitive screening test. Results: We analyzed data from 37 healthy controls and 16 individuals with mild cognitive impairment. Results revealed that one-class classification had higher predictive accuracy for mild cognitive impairment, whereas two-class classification performed better in identifying healthy controls. Gait features yielded the best results for one-class classification. Combining individual models led to better performance than combining features from the different tasks. Notably, the one-class majority voting approach exhibited a sensitivity of 87.5% and a specificity of 75.7%, suggesting it may serve as a potential alternative to the standard cognitive screening test. In contrast, the two-class majority voting failed to improve the low sensitivities achieved by the individual models due to the underrepresentation of the impaired group. Conclusion: Our preliminary results support the use of one-class classification with confound control to detect abnormal patterns of gait, fingertapping, cognitive, and dual tasks, to improve the early detection of cognitive impairment. Further research is necessary to substantiate the method's effectiveness in broader clinical settings.

Abstract
This paper presents the application of textile based electrodes for surface electromyography embedded in a wearable locomotion data capture system for gait analysis. The system that is under development will allow the measurement of several locomotion-related parameters in a practical and non-invasive way, comfortable to the user, reusable which can be used by patients from light to severe impairments or disabilities. The present paper gives an overview of the research, regarding the design of the textile electrodes, the textile support, and communications.

Abstract
Os sistemas vestíveis são uma tendência crescente na aquisição de sinais fisiológicos e de parâmetros biomecânicos de forma não obstrutiva. A utilização de elétrodos têxteis tornou-se muito popular devido à simplicidade e homogeneidade providenciada pela sua introdução em têxteis e peças de vestuário. Neste trabalho foi realizada a validação de elétrodos têxteis para medições electromiográficas através da comparação com elétrodos convencionais de cloreto de prata. Os resultados evidenciam que os elétrodos têxteis são capazes de medir potenciais mioeléctricos de forma semelhante aos elétrodos convencionais.

Abstract
It is extremely difficult to simplify the relation between several body parts, which perform human motion, into one set of features. Mainly, the upper-limb is capable of a wider range of actions, going from fine manipulation to prehension and grasping. Aiming to describe its complexity, several studies have been conducted in order to better understand the upper-limb specificities. However, most of studies restrain the task to pointing, reaching, or grasping, which seems not enough to explain the wide range of tasks possible to be performed in a daily scenario.

Abstract
In the field of vital signs monitoring, there is a growing trend towards transposing monitoring technologies into people’s daily lives. Currently, the work revolves around wearables. However, wearables have known limitations, mainly the need for voluntary actions to acquire the signals of interest, low battery life, and abandonment, which leads to the search for new solutions. An evolution is the integration of sensors into the environment or everyday objects through invisible devices (also known as “off the person” sensing). Properly assessing and matching the patient and their caregiver to the appropriate monitoring technology, while considering the suitability of the home environment for device operation and maintenance is a challenge that depends on sound human factors principles. This article reviews the current key features and technologies to provide an overview of the existing invisibles landscape.

Abstract
In our previous work, we explored a new invisible ECG biometrics approach that uses signals collected at the thighs using polymeric dry electrodes and sensors integrated into a toilet seat. However, the performance of the biometric templates remains unexplored. In this paper we evaluate how the ECG templates evolve, and the impact that potential changes may have on performance, using one case-study subject monitored over 31 days. This work is organized into two main parts. The first explores the morphological and physical traits of the subject throughout the 31 days based on data collected daily, three times per day at 6-hour intervals; in more than 80% of the sessions, all the signals were successfully acquired without showing noise nor movement artefacts. The second part is focused on evaluating the performance of Support Vector Machine (SVM) and Binary Convolutional Neural Network (BCNN) classifiers in the identification of the case study subject within a population of 10 individuals, covering an age range of (24 to 35 years); the top performer was the BCNN, achieving a perfect accuracy rate of 100% when tested on a group of two individuals.