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
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
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
Objective: Stepping exergames designed to stimulate physical and cognitive skills can provide important information concerning individuals’ performance. In this study, we investigated the potential of stepping and gameplay metrics to assess the motor-cognitive status of older adults. Methods: Stepping and gameplay metrics were recorded in a longitudinal study involving 13 older adults with mobility limitations. Game parameters included games’ scores and reaction times. Stepping parameters included length, height, speed, and duration, measured by inertial sensors placed on the shoes while interacting with the exergames. Parameters measured on the first gameplay were correlated against standard cognitive and mobility assessments, including the Montreal Cognitive Assessment (MoCA), gait speed, and the Short Physical Performance Battery. Based on MoCA scores, patients were then stratified into two groups: cognitively impaired and healthy controls. The differences between the two groups were visually inspected, considering their within-game progression over the training period. Results: Stepping and gameplay metrics had moderate-to-strong correlations with cognitive and mobility performance indicators: faster, longer, and higher steps were associated with better mobility scores; better cognitive games’ scores and reaction times, and longer and faster steps were associated with better cognitive performance. The preliminary visual analysis revealed that the group with cognitive impairment required more time to advance to the next difficulty level, also presenting slower reaction times and stepping speeds when compared to the healthy control group. Conclusion: Stepping exergames may be useful for assessing the cognitive and motor status of older adults, potentially allowing assessments to be more frequent, affordable, and enjoyable. Further research is required to confirm results in the long term using a larger and more diverse sample. © The Author(s) 2023.