Search Results (121)

This research investigated how detergent type and concentration, solution temperature, and flow rate affect ultrasonic cleaning efficiency in jewelry manufacturing. A silver bracelet without gemstones served as the test sample, and the study combined harmonic response analysis to assess acoustic pressure distribution with computational fluid dynamics to examine fluid flow patterns inside an ultrasonic cleaning machine. Cleaning tests were performed under real factory conditions to verify the simulations. Results showed that cleaning efficiency depends on the combined chemical and ultrasonic effects. Adding detergent lowered surface tension, encouraging cavitation bubble formation; higher temperatures (up to 60 °C) softened dirt, making removal easier; and moderate solution flow improved the cleaning, helping to carry dirt away from jewelry surfaces. Too much flow, however, decreased cavitation activity. The highest cleaning efficiency (93.890%) was achieved with 3% U-type detergent at 60 °C and a flow rate of 5 L/min, while pure water at room temperature (30 °C) without flow had the lowest efficiency (0.815%), confirmed by weighing and scanning electron microscope measurements. Interestingly, maximum ultrasonic power concentration did not always match the highest cleaning efficiency. The study supports sustainable practices by limiting detergent use to 3%, in line with Sustainable Development Goal (SDG) 9 (Industry, Innovation, and Infrastructure). Full article

Purpose: To explore the potential relationship between salivary amino acid concentrations and 8 km running performance in male undergraduate students. Methods: Thirty male undergraduate students were recruited. Participants completed an 8 km run while wearing smart bracelets. Saliva samples were collected before, immediately after, and 24 h after the run. Ultra-High Performance Liquid Chromatography (UHPLC) was used to quantify salivary amino acids. Results: The fast group (average speed > 12.80 km/h) had a significantly shorter running time (35.66 ± 1.30 min, p < 0.001) and higher speed (13.59 ± 0.46 km/h, p < 0.001) than the slow group. Before the run, salivary serine concentration (20.19 µg/mL, p = 0.013) was higher in the fast group. After 24 h, salivary glutamine concentration (6.65 µg/mL, p = 0.047) was lower in the fast group. Salivary threonine concentration was positively correlated with running speed. For every 1 µg/mL increase in salivary threonine concentration, average running speed increased by 0.011 km/h, and this correlation persisted after adjusting for age and heart rate. Conclusions: This study found a positive correlation between salivary threonine and 8 km running speed, along with differences in serine and glutamine concentrations among runners with different speeds. These findings provide preliminary evidence for the relationship between salivary amino acid concentrations and running performance, though further research with larger samples and diverse exercise types is needed. Full article

The purpose of this paper is to present a vibration dissipation device that sends vibration signals from the manipulated tool in the workplace to the operator’s hand by improving its ergonomic appearance and materials. Compared with another vibration dissipation device, this one was previously designed and patented. This paper is based on two studies: one theoretical and the other experimental (the latter regarding the material used). The first part is represented by the design and simulation of the device and a static analysis by FEA in SolidWorks 2022. The stresses to which the materials of the device’s bracelets are subjected are studied in this article; then, the resistance of the device’s materials to the tensile and deformation stresses they are subjected to is presented. All these studies complement DIAV equipment’s functionality in terms of its components’ design and assembly. Emphasis is placed on its shape and ease of assembly and the operator’s dexterity in mechanical processes so that DIAV does not limit them. In addition, from the point of view of its design, emphasis is placed on its fixation so that it is easy to assemble and is within reach of any operator. The appropriate choice of materials for the components of the DIAV device is of major importance both in terms of strength, ergonomics, and weight. The experimental results validate the theoretical results obtained through the FEA simulation in SolidWorks, and this fact confirms the usefulness and functionality of the DIAV device from the perspective of vibration attenuation. Full article

(1) Background: Several tools have been proposed to measure elastic band tension and time under tension (TUT) during elastic band exercise performance. However, current methods are often indirect, non-objective, or expensive. The Elastic Force Evaluation Bracelet (EFEB) is a simple, wearable system designed to estimate both variables. Therefore, the aim of this study was to evaluate the concurrent validity and test–retest reliability of the EFEB as a portable measurement device for application in a therapeutic exercise context. (2) Methods: Thirty-five healthy volunteers were recruited. Exercises with elastic bands were performed on the dominant upper and lower limbs in two sessions with a one-week interval between them, and peak elastic force values were obtained. Validity was assessed in the first session by comparing the force values obtained simultaneously using a force gauge, and the TUT compared to a linear encoder. Test–retest reliability was examined by comparing the measurements obtained between the two sessions. (3) Results: EFEB showed excellent correlation with the force gauge for elastic force (r = 0.883 for shoulder abduction and r = 0.981 for knee extension) and with the linear encoder for TUTs (r = 0.873 and r = 0.883, respectively). EFEB showed good levels of reliability for all four of the following parameters measured: elastic force for shoulder abduction and knee extension (ICC = 0.880 and 0.855, respectively), and TUT in both movements (ICC = 0.768 and 0.765, respectively). (4) Conclusions: In conclusion, EFEB is a valid and reliable device for the measurement of TUT during shoulder abduction and knee extension exercises performed with elastic bands. Full article

Background/Objectives: A 2-year-old male presented to the emergency department (ED) with vomiting and abdominal discomfort following ingestion of multiple magnets from a sibling’s bracelet. This case highlights the risks associated with magnet ingestion and the need for coordinated multidisciplinary care and public health intervention. Methods: Radiographs revealed magnets in the oropharynx, stomach, and small bowel. Emergency physicians coordinated care with otolaryngology, gastroenterology, and general surgery. Results: Laryngoscopy successfully removed two magnets from the uvula, and endoscopy retrieved 30 magnets from the stomach. General surgery performed a diagnostic laparoscopy, identifying residual magnets in the colon. Gastroenterology attempted a colonoscopy but was unable to retrieve magnets due to formed stool, leading to bowel preparation and serial imaging. The patient eventually passed 12 magnets per rectum without surgical intervention. Conclusions: This case emphasizes the importance of multidisciplinary collaboration in managing magnet ingestion, a preventable cause of serious gastrointestinal injury. Recent studies highlight the increasing incidence and severity of such cases due to accessibility and inadequate regulation. These findings underscore the need for public awareness and adherence to management protocols to mitigate morbidity and mortality in pediatric patients. Full article

The growing implementation of digital platforms and mobile devices in educational environments has generated the need to explore new approaches for evaluating the learning experience beyond traditional self-reports or instructor presence. In this context, the NPFC-Test dataset was created from an experimental protocol conducted at the Experiential Classroom of the Institute for the Future of Education. The dataset was built by collecting multimodal indicators such as neuronal, physiological, and facial data using a portable EEG headband, a medical-grade biometric bracelet, a high-resolution depth camera, and self-report questionnaires. The participants were exposed to a digital test lasting 20 min, composed of audiovisual stimuli and cognitive challenges, during which synchronized data from all devices were gathered. The dataset includes timestamped records related to emotional valence, arousal, and concentration, offering a valuable resource for multimodal learning analytics (MMLA). The recorded data were processed through calibration procedures, temporal alignment techniques, and emotion recognition models. It is expected that the NPFC-Test dataset will support future studies in human–computer interaction and educational data science by providing structured evidence to analyze cognitive and emotional states in learning processes. In addition, it offers a replicable framework for capturing synchronized biometric and behavioral data in controlled academic settings. Full article

As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. The device integrates MAX30100 sensors for heart rate monitoring and MPU-6050 for step counting and sleep quality analysis (deep and superficial sleep). The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. The system is connected to the Azure cloud infrastructure, ensuring secure data transmission, preprocessing, and the automatic generation of alerts for prompt medical interventions. Studies conducted over two years demonstrated a sensitivity of 100% and an accuracy of 98.5%, with a tendency to generate additional alerts to avoid overlooking critical events. The results outline the importance of personalizing the analysis, adapting algorithms to individual characteristics, and the system’s potential to prevent medical complications and improve the quality of life for elderly individuals. Full article

Cancer survivors face numerous challenges, and digital health interventions can empower them by enhancing self-efficacy and patient activation. This prospective study aimed to assess the impact of a mHealth app on self-efficacy and patient activation in 166 breast and colorectal cancer survivors. Participants received a smart bracelet and used the app to access personalized care plans. Data were collected at baseline and follow-ups, including patient-reported outcomes and clinician feedback. The study demonstrated positive impacts on self-efficacy and patient activation. The overall trial retention rate was 75.3%. Participants reported high levels of activation (PAM levels 1–3: P = 1.0; level 4: P = 0.65) and expressed a willingness to stay informed about their disease (CASE-Cancer factor 1: P = 0.98; factor 2: P = 0.66; factor 3: P = 0.25). Usability of the app improved, with an increase in participants rating the system as having excellent usability (from 14.82% to 22.22%). Additional qualitative analysis revealed positive experiences from both patients and clinicians. This paper contributes significantly to cancer survivorship care by providing personalized care plans tailored to individual needs. The PERSIST platform shows promise in improving patient outcomes and enhancing self-management abilities in cancer survivors. Further research with larger and more diverse populations is needed to establish its effectiveness. Full article

In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performance in a system designed to detect frailty insights. We introduce missing data mechanisms—Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR)—into a dataset collected from smart bracelets, simulating real-world conditions. Imputation methods, including Expectation–Maximization (EM), matrix completion, Bayesian networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Generative Adversarial Imputation Networks (GAINs), Variational Autoencoder (VAE), and GRU-D, were evaluated based on normalized Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² metrics. The results demonstrate that KNN and SVM consistently outperform other methods across all three mechanisms due to their ability to adapt to diverse patterns of missingness. Specifically, KNN and SVM excel in MAR conditions by leveraging observed data relationships to accurately infer missing values, while their robustness to randomness enables superior performance under MCAR scenarios. In MNAR contexts, KNN and SVM effectively handle unobserved dependencies by identifying underlying patterns in the data, outperforming methods like GRU-D and VAE. These findings highlight the importance of selecting imputation methods based on the characteristics of missing data mechanisms, emphasizing the versatility and reliability of KNN and SVM in healthcare applications. This study advocates for hybrid approaches in healthcare applications like the cINnAMON project, which supports elderly individuals at risk of frailty through non-intrusive home monitoring systems. Full article

In the medical field, there are several very different movement disorders, such as tremors, Parkinson’s disease, or Huntington’s disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson’s disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington’s Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel. Full article

Objective: To examine the associations between gradients of average daily temperature and sunlight hours with daily steps over a 35-week period in school-aged children and to evaluate whether there were differences by sex. Methods: We conducted a follow-up study involving 655 children (50.8% girls, mean age 10.45 ± 0.95 years) from six public primary schools in Cuenca, Spain. We measured daily steps using Xiaomi Mi Band 3 Smart Bracelets (Xiaomi Corporation, Beijing, China) from October 2022 to June 2023 (over 35 weeks). We collected the average daily temperature from the local weather station in Cuenca and the sunlight hours during the same period. We used ANCOVA models and LOESS regression to examine the associations between gradients of average daily temperature and daily hours of sunlight with daily steps. Additionally, we performed a multiple linear regression model. Results: Our findings revealed significant variations in daily steps across the 35 weeks. The relationship between environmental factors and daily steps was non-linear in both girls and boys. The optimal values for higher activity levels were an average temperature of 14 °C and 13 h of sunlight. Furthermore, a 1 °C increase in temperature was associated with an increase of 74 ± 130 steps/day, while an increase of one hour of sunlight was associated with an increase of 315 ± 237 steps/day. However, the sunlight hours may act as a moderating factor. Conclusions: Our study showed a non-linear association between average daily temperature and the sunlight hours with daily steps over a 35-week period. Appropriate strategies may be needed to promote physical activity during periods of extreme temperatures or sunlight exposure. Full article

Human gesture recognition using electromyography (EMG) signals holds high potential for enhancing the functionality of human–machine interfaces, prosthetic devices, and sports performance analysis. This work proposes a gesture classification system based on electromyography. This system has been designed to improve the accuracy of forearm gesture classification by leveraging advanced signal processing and deep learning techniques to optimize classification accuracy. The system is composed of two main modules: a signal processing module, able to perform two main transforms (short-time Fourier transform and Constant-Q-Transform), and a classification module based on convolutional neural networks (CNNs). The dataset employed in this study, entitled “Latent Factors Limiting the Performance of sEMG-Interfaces”, comprises EMG signals collected via a bracelet equipped with 8 distinct sensors, capable of capturing a wide range of forearm muscle activities. The experimental process is composed of two main phases. Firstly, we employed a k-fold cross-validation methodology to systematically assess and validate the model’s performance across different subsets of data for hyperparameter tunning. Secondly, the best system configuration was evaluated using a new subset, reporting significant improvements. The baseline neural network architecture reported an accuracy of 85.0 ± 0.13% when classifying gestures. Through rigorous hyperparameter tuning and the application of various mathematical transformations to the EMG features, we managed to enhance the classification accuracy to 90.0 ± 0.12% (an absolute improvement of 5% compared to the baseline for a 5-class problem). When making comparisons to previous works, we significantly improved the F-score from 85.5% to 89.3% for a 4-class problem (left, right, up, and down). Full article

Mobile applications, websites and social media networks are now widely used communication tools. With the emergence of communication-related technologies in our lives and, consequently, the rise of social media networks and mobile applications, nutrition-related applications have become popular. Smartphones and other artificial intelligence technologies have become very useful tools for delivering nutrition-related interventions because they are very accessible and cost-effective. Digital interventions are also able to serve a larger number of communities than traditional interventions. Nutrition is not a field that has remained on the sidelines of these technological advances, and numerous mobile applications and technological tools have emerged that are intended to provide dietary advice or guidelines on the process of recovering from a disease. However, many of these applications have limitations and barriers that are important to consider. The aim of this review was to analyze the most current and widely used mobile applications related to nutrition, as well as their complementary tools (activity bracelets and smart scales, among others), highlighting their importance in improving lifestyle habits. In addition, their advantages and disadvantages are discussed and future directions are proposed. Full article

Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device’s functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device’s functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching $92.3\pm 2.6\%$ on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands. Full article

Wireless power transfer (WPT) systems have ushered in a new era for wearable and implantable technologies, introducing opportunities for enhanced device functionality. A pivotal aspect in improving these devices is the optimization of electromagnetic transmission. This paper presents several solutions to improve electromagnetic transmission to an implantable/wearable device. Several scatterers are considered to mimic objects that can be easily worn by a patient, such as necklaces and bracelets, or easily integrated into textile fabric. An analytical method is employed to address the scattering by cylindrical objects above a biological tissue, modeled as a multilayer. Expansions into cylindrical waves, also represented through plane-wave spectra, are used to express the scattered fields in each medium. Numerical results for both the case of conducting and of dielectric cylindrical scatterers are presented at a frequency of the Industrial, Scientific and Medical band ($f=2.45$ GHz), showing possible configurations of worn objects for electromagnetic field intensification. Full article