Worker’s physical fatigue classification using neural networks

Physical fatigue is not only an indication of the user’s physical condition and/or need for sleep or rest, but can also be a significant symptom of various diseases. This fatigue affects the performance of workers in jobs that involve some continuous physical activity, and is the cause of a large proportion of accidents at work. The physical fatigue is commonly measured by the perceived exertion (RPE). Many previous studies have attempted to continuously monitor workers in order to detect the level of fatigue and prevent these accidents, but most have used invasive sensors that are difficult to place and prevent the worker from performing their tasks correctly. Other works use activity measurement sensors such as accelerometers, but the large amount of information obtained is difficult to analyse in order to extract the characteristics of each fatigue state. In this work, we use a dataset that contains data from inertial sensors of several workers performing various activities during their working day, labelled every 10 min based on their level of fatigue using questionnaires and the Borg fatigue scale. Applying Machine Learning techniques, we design, develop and test a system based on a neural network capable of classifying the variation of fatigue caused by the physical activity collected every 10 min; for this purpose, a feature extraction is performed after the time decomposition done with the Discrete Wavelet Transform (DWT). The results show that the proposed system has an accuracy higher than 92% for all the cases, being viable for its application in the proposed scenario.European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Consejería de Economía, Conocimiento, Empresas y Universidad (Junta de Andalucía) US-126371

Perspective chapter: Internet of Things in Healthcare - New Trends, Challenges and Hurdles

Applied to health field, Internet of Things (IoT) systems provides continuous and ubiquitous monitoring and assistance, allowing the creation of valuable tools for diagnosis, health empowerment, and personalized treatment, among others. Advances in these systems follow different approaches, such as the integration of new protocols and standards, combination with artificial intelligence algorithms, application of big data processing methodologies, among others. These new systems and applications also should face different challenges when applying this kind of technology into health areas, such as the management of personal data sensed, integration with electronic health records, make sensing devices comfortable to wear, and achieve an accurate acquisition of the sensed data. The objective of this chapter is to present the state of the art, indicating the most current IoT trends applied to the health field, their contributions, technologies applied, and challenges faced

Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors

Nowadays, the study of the gait by analyzing the distribution of plantar pressure is a well-established technique. The use of intelligent insoles allows real-time monitoring of the user. Thus, collecting and analyzing information is a more accurate process than consultations in so-called gait laboratories. Most of the previous published studies consider the composition and operation of these insoles based on resistive sensors. However, the use of capacitive sensors could provide better results, in terms of linear behavior under the pressure exerted. This behavior depends on the properties of the dielectric used. In this work, the design and implementation of an intelligent plantar insole composed of capacitive sensors is proposed. The dielectric used is a polydimethylsiloxane (PDMS)-based composition. The sensorized plantar insole developed achieves its purpose as a tool for collecting pressure in different areas of the sole of the foot. The fundamentals and details of the composition, manufacture, and implementation of the insole and the system used to collect data, as well as the data samples, are shown. Finally, a comparison of the behavior of both insoles, resistive and capacitive sensor-equipped, is made. The prototype presented lays the foundation for the development of a tool to support the diagnosis of gait abnormalities.22 página

Diseño y desarrollo de sistema IoT para el control y monitorización de personas

Las TIC han avanzado sirviendo de apoyo a diferentes servicios, sin ser la salud una excepción. La telemedicina soluciona problemas como la demanda de atención médica o la asistencia. La telemonitorización destaca por su comodidad para controlar el estado del paciente a distancia; sin embargo, estos sistemas son costosos e implican portar un dispositivo voluminoso. En este trabajo se realiza el diseño e implementación de un sistema de monitorización basado en IoT, haciendo uso de una placa de bajo costo y sensores de medición de constantes vitales, que se conectan al dispositivo móvil del usuario para enviar información a distancia y en tiempo real, además de registrar la evolución y visualizar el estado en el dispositivo móvil. De igual forma y gracias al sistema IoT, se registra la posición GPS, que será transmitida al contacto de emergencia en caso de detectar una variación peligrosa de las constantes vitales

Diseño, desarrollo y testeo de copa menstrual inteligente con reconocimiento de posición y aviso de llenado

Existen diversos trastornos asociados a la menstruación que provocan anomalías en el ciclo, como el síndrome de ovario poliquístico, endometriosis, hiperprolactinemia o alteraciones en la glándula tiroidea. Todos ellos provocan un sangrado uterino anormal (SUA), cuyos síntomas incluye un sangrado abundante, denominado menorragia. La medición del volumen de sangre en una menstruación se realiza de forma aproximada y en base a la información aportada por la paciente; sin embargo, las pacientes tienen dificultades al valorar este término, y en ocasiones esta valoración no se corresponde con la realidad, siendo a veces por exceso o por defecto. En este trabajo se diseña, desarrolla y testea un sistema de medición cuantitativa y automática del sangrado en base a una copa menstrual inteligente que verifica la posición de la usuaria para realizar una medición correcta del volumen de sangre, enviarlo a una aplicación y registrarlo

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies

The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the use of these devices (regarding the general trends); we analyzed the research works and devices marketed in the last 10 years. This analysis extracted relevant information on the general trend of use, as well as more specific aspects, such as the use of sensors, communication technologies, and diseases. A comparison was made between the commercial and research aspects linked to wearables in the healthcare field, and upcoming trends were analyzed

On the feature extraction process in machine learning. An experimental study about guided versus non-guided process in falling detection systems

Falls are current events that can lead to severe injuries and even accidental deaths among the population, especially the elderly. Since them usually live alone and their contact with other people has decreased since pandemic, recent years studies have focused on automatic fall detection systems with wearable devices using machine learning algorithms. Overall, and according to other works, these systems can be classified as non-guided, if the machine learning model directly uses raw data without feature extraction, or as guided systems, if a previous step of feature extraction is needed to reduce complexity of the algorithm. However, no recommendations are made in the literature on which system could be more advantageous for detecting fall events. Therefore, in this work, a detailed comparison between both types of systems is carried out, using the same process for different machine learning models in order to obtain an accurate classification of activities of daily living, falling risks, and falls. This process includes the optimization of models’ hyperparameters to obtain the best classifiers, followed by an assessment using common evaluation metrics, confusion matrices, ROC curves and execution times. Results show a better classification of models’ three classes for the non-guided models. However, the guided models show more stable metrics and lower computational load.Andalusian Regional I+D+i FEDER DAFNE US-1381619Andalusian Regional I+D+i FEDER MSF-PHIA US-126371

Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies

The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the use of these devices (regarding the general trends); we analyzed the research works and devices marketed in the last 10 years. This analysis extracted relevant information on the general trend of use, as well as more specific aspects, such as the use of sensors, communication technologies, and diseases. A comparison was made between the commercial and research aspects linked to wearables in the healthcare field, and upcoming trends were analyzed