Recent progress of bio-based smart wearable sensors for healthcare applications

Materials Today Electronics, Journal Year: 2023, Volume and Issue: 5, P. 100055 - 100055

Published: Aug. 11, 2023

As personal portable devices, wearable sensors supply a leading-edge pathway to diagnose various diseases through actuating biological, physical, and chemical sensing capabilities. This could be commonly carried out via recording continuous real-time of the patient's physiological statuses, as well pathophysiological information. Although sensor technology is in infancy stage, tremendous attempts have been devoted approaching flexible polymeric sensors. Among polymer candidates applicable for synthesizing sensors, bio-based ones piqued more interest due their biocompatibility, biodegradability, eco-friendly features, cost-effectiveness. Additionally, several fabrication techniques professed architect efficient frameworks, such films, hydrogels, aerogels, ferrogels, 3D layers, electrospun mats, textiles. In this review, different mechanisms declared engineer are overviewed. Then, regarding advantages observed polymers, focused studies on natural-based described. Notably, cellulose, chitosan, silk, gelatin, alginate's role functionality highlighted. Accordingly, review has opened new window ahead opportunities based natural polymers. It hoped that generation will launched by combining emerging achievements obtained from employing sustainable green elements miniaturized structures.

Language: Английский

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A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 8, P. 100255 - 100255

Published: June 1, 2023

This study proposes a feedforward deep neural network to predict the parameters of lithium-ion battery in electric vehicles. Correlation analysis is used select candidate for proposed model with no categorical variable. A direct artificial developed battery's charge state and develop inverse model. The predicted state-of-charge combined four virtual functions form input variables Furthermore, are incorporated enhance predicting capability function multi-output speed, mileage, voltage, velocity, state-of-charge. superior previously literature because its multiple output capabilities. Also, makes decision-making easier when design simulation than single-output networks, which only. mean square error as metric accurate measurement. During by (with functions), accuracy was 44.43 times higher traditional Redefined were verify findings result suggests that incorporating into model's can improve vehicle parameter predictions.

Language: Английский

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Predicting athletic injuries with deep Learning: Evaluating CNNs and RNNs for enhanced performance and Safety

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107692 - 107692

Published: Feb. 12, 2025

Language: Английский

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A bibliometric analysis of technology in sustainable healthcare: Emerging trends and future directions

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 8, P. 100292 - 100292

Published: July 25, 2023

Technology application in healthcare is a recent field devoted to sustainability the industry. However, research this sector has grown at rapid pace. While expansion been advantageous for discipline, it also made more difficult grasp its extent. As result, answering questions such as most important emerging trends technology sustainable research, critical breakthrough papers, influence of these and productive leading researchers have become challenging. Finally, understanding intellectual framework knowledge base on Sustainable Healthcare (TSH) difficult. This study attempted address issues by presenting an overview work TSH and, doing so, answer some previously listed problems. The PRISMA model, along with science mapping review process using bibliometric analysis tools VOSviewer Python, was employed analyze published works indexed Scopus database over span 24 years. Although had progressing rapidly before COVID-19 pandemic, current accelerated shift past four years may be attributed pandemic itself well advancements technologies artificial intelligence, machine learning, Internet Things. We discuss themes, prolific authors, institutions, journals, relationships among TSH. we present challenges prospects research. findings our would helpful working healthcare.

Language: Английский

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Neural Network System for Predicting Anomalous Data in Applied Sensor Systems

Applied System Innovation, Journal Year: 2024, Volume and Issue: 7(5), P. 88 - 88

Published: Sept. 23, 2024

This article advances the research on intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within engine expert system. A SARIMAX-based preprocessor model was developed to determine autocorrelation partial training data, accounting dynamic changes external factors, achieving accuracy up 97.9%. modified LSTM-based predictor with Dropout Dense layers predicted sensor tested error margin 0.218% predicting TV3-117 aircraft gas temperature values before compressor turbine during one minute flight. reconstructor restored missing time series replaced outliers synthetic values, 98.73% accuracy. An detector using concept dissonance successfully identified two anomalies: malfunction sharp drop minutes activity, type I II errors below 1.12 1.01% detection under 1.611 s. system’s AUC-ROC value 0.818 confirms its strong ability differentiate between normal anomalous ensuring reliable accurate detection. limitations involve dependency quality data from sensors, affected by malfunctions or noise, LSTM network’s (up 97.9%) varying conditions, model’s high computational demand potentially limiting real-time use resource-constrained environments.

Language: Английский

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An Efficient Approach to Sports Rehabilitation and Outcome Prediction Using RNN-LSTM

Mobile Networks and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 12, 2024

Language: Английский

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A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

Heliyon, Journal Year: 2024, Volume and Issue: 10(12), P. e33297 - e33297

Published: June 1, 2024

This study aims to enhance the precision of analyzing athlete behavior characteristics, thereby optimizing sports training and competitive strategies. introduces an innovative Ant Colony Optimization (ACO) clustering model designed address high-dimensional issues in data by simulating path selection mechanism ants searching for food. The development process this includes fine-tuning ACO parameters, features specific data, comparing it with traditional algorithms, similar research models based on neural network, support vector machines, deep learning. results indicate that significantly outperforms comparison algorithms terms silhouette coefficient (0.72) Davies-Bouldin index (1.05), demonstrating higher effectiveness stability. Particularly noteworthy is recall rate (0.82), a key performance indicator, where accurately captures different behavioral characteristics athletes, validating its reliability analysis. innovation lies not only application algorithm practical field but also showcasing advantages handling complex, data. However, generality efficiency larger scale or types still need further validation. In conclusion, through introduction optimization model, provides novel effective approach deeper understanding analysis characteristics. holds significant importance advancing science applications.

Language: Английский

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Challenges of sensor network in smart hospitals

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 617 - 636

Published: Jan. 1, 2025

Language: Английский

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Cloud-to-Thing continuum-based sports monitoring system using machine learning and deep learning model

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2539 - e2539

Published: Feb. 10, 2025

Sports monitoring and analysis have seen significant advancements by integrating cloud computing continuum paradigms facilitated machine learning deep techniques. This study presents a novel approach for sports monitoring, specifically focusing on basketball, that seamlessly transitions from traditional cloud-based architectures to paradigm, enabling real-time insights into player performance team dynamics. Leveraging algorithms, our framework offers enhanced capabilities tracking, action recognition, evaluation in various scenarios. The proposed Cloud-to-Thing continuum-based system utilizes advanced techniques such as Improved Mask R-CNN pose estimation hybrid metaheuristic algorithm combined with generative adversarial network (GAN) classification. Our significantly improves latency accuracy, reducing 5.1 ms achieving an accuracy of 94.25%, which outperforms existing methods the literature. These results highlight system's ability provide real-time, precise, scalable immediate feedback time-sensitive applications. research has improved event analysis, contributing evaluation, strategies, informed tactical adjustments.

Language: Английский

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Design of IoT Architecture and LLM Model for Personalized Training Recommendations for Athletes

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 306 - 320

Published: Jan. 1, 2025

Language: Английский

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