Cálculo da eficiência da simetria usando a relação de simetria da marcha, aplicando deformação dinâmica normalizada ao longo do tempo

In this paper we propose a new method for symmetry calculation in wearable devices. The problem in this domain is that only discrete features such as stride length, stride duration, or duration of gait phases are used for the symmetry calculation. However, this can lead to failures, since the use of features can result in partial loss of information from the time series. From this we present a possibility to calculate the symmetry by using Dynamic Time Warping (DTW). DTW uses the complete time series for the analysis and is therefore independent of certain features.En este artículo proponemos un nuevo método para el cálculo de la simetría para la resistencia sensible a la fuerza (FSR) en dispositivos portátiles. El problema en este dominio es que solo se utilizan características discretas como la longitud de la zancada, la duración de la zancada o la duración de las fases de la marcha para el cálculo de la simetría. Sin embargo, esto puede conducir a fallas, ya que el uso de funciones puede resultar en una pérdida parcial de información de la serie temporal. A partir de esto, presentamos la posibilidad de calcular la simetría utilizando el método de Dynamic Time Warping (DTW). El DTW utiliza la serie de tiempo completa para el análisis y, por lo tanto, es independiente de ciertas características.Neste artigo propomos um novo método para cálculo de simetria para resistência sensível à força (FSR) em dispositivos portáteis. O problema neste domínio é que apenas são utilizadas características discretas, como comprimento da passada, duração da passada ou duração da fase da marcha para o cálculo de simetria. No entanto, isso pode levar a falhas, pois o uso de funções pode resultar em perda parcial de informações da série temporal. A partir disso, apresentamos a possibilidade de calcular a simetria utilizando o método Dynamic Time Warping (DTW). O DTW usa toda a série temporal para a análise e, portanto, é independente de determinados recursos

Addressing the challenges posed by human machine interfaces based on force sensitive resistors for powered prostheses

Despite the advancements in the mechatronics aspect of prosthetic devices, prostheses control still lacks an interface that satisfies the needs of the majority of users. The research community has put great effort into the advancements of prostheses control techniques to address users’ needs. However, most of these efforts are focused on the development and assessment of technologies in the controlled environments of laboratories. Such findings do not fully transfer to the daily application of prosthetic systems. The objectives of this thesis focus on factors that affect the use of Force Myography (FMG) controlled prostheses in practical scenarios. The first objective of this thesis assessed the use of FMG as an alternative or synergist Human Machine Interface (HMI) to the more traditional HMI, i.e. surface Electromyography (sEMG). The assessment for this study was conducted in conditions that are relatively close to the real use case of prosthetic applications. The HMI was embedded in the custom prosthetic prototype that was developed for the pilot participant of the study using an off-the-shelf prosthetic end effector. Moreover, prostheses control was assessed as the user moved their limb in a dynamic protocol.The results of the aforementioned study motivated the second objective of this thesis: to investigate the possibility of reducing the complexity of high density FMG systems without sacrificing classification accuracies. This was achieved through a design method that uses a high density FMG apparatus and feature selection to determine the number and location of sensors that can be eliminated without significantly sacrificing the system’s performance. The third objective of this thesis investigated two of the factors that contribute to increased errors in force sensitive resistor (FSR) signals used in FMG controlled prostheses: bending of force sensors and variations in the volume of the residual limb. Two studies were conducted that proposed solutions to mitigate the negative impact of these factors. The incorporation of these solutions into prosthetic devices is discussed in these studies.It was demonstrated that FMG is a promising HMI for prostheses control. The facilitation of pattern recognition with FMG showed potential for intuitive prosthetic control. Moreover, a method for the design of a system that can determine the required number of sensors and their locations on each individual to achieve a simpler system with comparable performance to high density FMG systems was proposed and tested. The effects of the two factors considered in the third objective were determined. It was also demonstrated that the proposed solutions in the studies conducted for this objective can be used to increase the accuracy of signals that are commonly used in FMG controlled prostheses

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Body composition and functional assessment of nutritional status in adults: a narrative review of imaging, impedance, strength and functional techniques

This is the peer reviewed version of the following article: S, Smith & A. M. Madden (2016) ‘Body Composition and functional assessment of nutritional status in adults: a narrative review of imaging, impedance, strength and functional techniques’, Journal of Human Nutrition and Dietetics, 29 (6): 714-732, which has been published in final form at https://dx.doi.org/10.1111/jhn.12372. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.The accurate and valid assessment of body composition is essential for the diagnostic evaluation of nutritional status, identifying relevant outcome measures, and determining the effectiveness of current and future nutritional interventions. Developments in technology and our understanding of the influences of body composition on risk and outcome will provide practitioners with new opportunities to enhance current practice and to lead future improvements in practice. This is the second of a two-part narrative review that aims to critically evaluate body composition methodology in diverse adult populations, with a primary focus on its use in the assessment and monitoring of under-nutrition. Part one focused on anthropometric variables [Madden and Smith (2016) J Hum Nutr Diet 29: 7–25] and part two focuses on the use of imaging techniques, bioelectrical impedance analysis, markers of muscle strength and functional status, with particular reference to developments relevant to practice.Peer reviewe

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation