Innovative Monitoring Research Articles

Autonomous, Multisensory Soil Monitoring System

The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system’s performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m2 to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification.

Development and Application of an Innovative Planning and Monitoring Tool to Optimize Construction Projects

Development and Application of an Innovative Planning and Monitoring Tool to Optimize Construction Projects

Cloud-based adolescent physical health research incorporating cell molecular biomechanics insights into predictive modeling and biometric algorithms

The physical health of adolescents is critical to their future development and quality of life. However, there is still much room for improvement in monitoring and intervention of adolescent physical health in colleges and universities. Based on the National Standard for Student Physical Fitness and Health, this study proposes a physical health management framework for adolescents that combines a cloud platform and real-time data analysis. Through an innovative health monitoring and early warning system and feedback mechanism, the effectiveness of health intervention was significantly improved. Body mass index is related to the balance between energy intake and expenditure at the cellular level. Excessive calorie intake can lead to an increase in adipose cells, which secrete molecules that can affect overall metabolism and biomechanical stress on tissues. Lung capacity is linked to the elasticity and strength of the alveolar cells and the connective tissue in the lungs. he proper functioning of these cells, regulated by intracellular signaling pathways and molecular interactions, determines the efficiency of gas exchange. The 50-meter run, seated forward bending, standing long jump, pull-ups/sit-ups, and 800-meter/1000-meter run all involve muscle contractions. Muscle cells contain actin and myosin filaments, and the sliding of these filaments, regulated by calcium ions and other molecular signals, generates the force required for movement. The graded early warning parameters established by the system can thus be seen as indicators of potential disruptions in these cell molecular biomechanical processes. The innovative health monitoring and early warning system and feedback mechanism not only help students recognize the weaknesses in their physical functions from a macroscopic level but also potentially identify areas where cell molecular biomechanical imbalances may exist. This enables physical education teachers to formulate personalized training plans that can target and correct these imbalances, as experimentally validated by the system's ability to accurately identify health risks and enhance the overall health of students.

Bibliometric Analysis of the Effects of Prolonged Preoperative Fasting on Patients

ABSTRACT Preoperative fasting is a common practice in the surgical field, which aims to minimize complications and ensure patient safety. Understanding its impact and optimizing protocols are essential in clinical practice. This study analyzed the bibliometric and network overview of publications on the effects of extended preoperative fasting on patients, using VOSViewer. This bibliometric analysis explores the research landscape on the effects of extended preoperative fasting duration on patients. Using VOSViewer, this study identified key contributors and clusters in the literature, highlighting significant research gaps. Bibliometric methods were used to analyze publications regarding the effects of extended preoperative fasting. Using VOSViewer, this study identified key contributors and groups in the literature, which highlighted significant research gaps. Bibliometric methods were used to analyze publications regarding the effects of extended preoperative fasting. Data were aggregated and visualized using VOSViewer to map the publication network and identify thematic clusters. China and the UK emerged as the main contributors with 7 articles each, followed by Brazil and India. Three main clusters were identified: a red cluster focusing on fasting duration, side effects, dehydration risk, and intraoperative management; a blue cluster exploring the effects of fasting on surgical outcomes, hunger, and procedural aspects; and a green cluster addressing guideline development, elective surgery, and preoperative preparation. These findings underscore the importance of extended preoperative fasting in clinical practice and highlight significant research opportunities. Future research could focus on personalized fasting protocols, long-term effects, psychological impact, and innovative monitoring technologies to improve the quality of patient care. Keywords: Preoperative Fasting, Effects Of Prolonged Fasting, Bibliometric Analysis, Vosviewer, Surgical Outcomes, Patient Care

Scalable and Secure IoT-Driven Vibration Monitoring: Advancing Predictive Maintenance in Industrial Systems

The rapid evolution of Industry 4.0 has positioned Internet of Things (IoT) technologies as key enablers for smarter industrial operations, particularly in predictive maintenance and machine monitoring. This research proposes an innovative IoT-driven vibration monitoring system that addresses limitations in traditional approaches such as high costs, limited scalability, and insufficient real-time capabilities. Employing low-cost sensors, edge computing, and LoRaWAN-based communication, the framework enables efficient fault detection and operational analysis. Data from industrial machinery was collected over two months and analyzed using advanced signal processing and machine learning techniques to extract meaningful insights. The system demonstrated an accuracy rate of 92%, a detection latency of 150 milliseconds, and extended sensor life to 12 months, marking significant improvements over conventional methods. Furthermore, scalability tests showed stable performance across setups involving up to 500 sensors, even in challenging industrial conditions. This study also highlights cost reductions of 30% and a 25% decline in machine downtime, reinforcing its practical value for industrial applications. By delivering an adaptable, energy-efficient, and secure solution, this research advances the integration of IoT into industrial systems. It lays the groundwork for future enhancements, including real-world testing and multimodal data integration

Advances in Bioprocess Engineering for Optimising Chlorella vulgaris Fermentation: Biotechnological Innovations and Applications.

Chlorella vulgaris, a unicellular green microalga, has obtained significant attention due to its high protein content, abundance of bioactive compounds, and broad biotechnological potential. Used in nutraceuticals, pharmaceuticals, and functional foods, it is now gaining traction in cosmetics, biopharmaceuticals, and environmental applications. Recent advancements in fermentation technology, such as the development of high-density fermentation strategies, adaptive evolution of strains, and real-time monitoring systems, have greatly improved the efficiency, scalability, and sustainability of C. vulgaris production, enhancing bioavailability and product quality. This review explores developments in C. vulgaris fermentation, highlighting advancements in strain improvement through genetic engineering, metabolic optimization, mutagenesis, and adaptive evolution, alongside bioprocess engineering and the optimization of fermentation parameters. Key considerations include bioreactor design, downstream processing, and innovative monitoring technologies aimed at maximizing biomass yield and bioactive compound production. Emerging applications of fermented C. vulgaris across industries are also highlighted, along with future perspectives on scaling up production, addressing regulatory challenges, and ensuring biosafety. These insights provide a comprehensive outlook on the future of C. vulgaris fermentation in biotechnological applications.

AIHEMAF-P: An Innovative Healthcare Model for Atrial Fibrillation Patients.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias of clinical relevance and a major cause of cardiovascular morbidity and mortality. Following a diagnosis of AF, patients are directed towards therapy with anticoagulant drugs to reduce the thromboembolic risk and antiarrhythmics to control their cardiac rhythm, with periodic follow-up checks. Despite the great ease of handling these drugs, we soon realized the need for follow-up models that would allow the appropriateness and safety of these pharmacological treatments to be monitored over time. This pilot study was conducted at a rural pharmacy. The study comprised 47 patients (average age 71.22 years) with nonvalvular atrial fibrillation (68% being paroxysmal) on NOACs. Twenty percent of the enrolled subjects lived alone and fifty-four percent of the participants stated that they were not independent in managing their treatment. The primary aim was to describe the implementation and the outcomes of an innovative smart clinic model in which a local trained pharmacist is a case manager, and the patient carries out the required checks via telemedicine and point-of-care testing systems (POCT) under the service pharmacy regime; the results of the checks could be shared in real time with the attending general practitioner and the relevant specialist. The secondary aims of this study were to evaluate adherence to the planned controls, the prescriptive appropriateness of the dosages and drugs and adherence to the prescribed therapy, the occurrence of pharmacological problems linked to drug type interactions, the occurrence of hemorrhagic and/or thromboembolic complications, the acceptance by the general practitioners and/or the specialists of the reports made by the pharmacist on the subsequent actions undertaken, the economic and social impact of this model on the National Health Service and on the patient, and the impact on the quality perceived by the patients involved in this innovative monitoring process. Compliance with the planned checks was 93%. The dosage of the anticoagulant drug during enrollment was found to be inappropriate, without apparent clinical reasons, in 11% of the sample. Adherence to the anticoagulant therapy was found to be 98%. In total, 214 drug-drug interactions of varying clinical relevance were detected. No embolic events were detected; however, 13% of the sample reported a major hemorrhagic event, which came to light thanks to the close monitoring of hemoglobinemia. A total of 109 reports were made to the patients' referring doctors in relation to the summarized anomalies, and 84% were accepted by the referring clinicians. Therefore, community pharmacists and pharmacy services represent ideal actors and contexts that, when integrated into the care network, can really favor individual care plan adherence and achieve daily morbidity reductions and cost savings through proper disease control and the early diagnosis of complications.

Exploring Pharmacological and Non-Pharmacological Approaches to Managing Hypertension During Pregnancy: A Systematic Review.

This systematic review aimed to explore the efficacy of both pharmacological and non-pharmacological interventions in managing hypertension during pregnancy. It analyzed high-quality randomized controlled trials (RCTs), focusing on outcomes related to maternal and fetal health. The findings demonstrated that antihypertensive medications, particularly labetalol and nifedipine, effectively reduced the risks of severe preeclampsia (PE), preterm birth, and other complications. Remote monitoring of blood pressure (BP) showed promise in improving postpartum care and addressing health disparities. While dietary interventions such as the Dietary Approaches to Stop Hypertension (DASH) diet offered metabolic benefits, their impact on preventing PE was inconclusive. The review highlights the need for a comprehensive approach to hypertension management, integrating medication, lifestyle interventions, and innovative monitoring strategies. It also emphasizes the importance of further research to refine non-pharmacological interventions and assess their long-term effectiveness. We believe these insights will help guide clinical practice, enhance maternal and fetal outcomes, and inform future research directions.

Innovations in Limnospira platensis Fermentation: From Process Enhancements to Biotechnological Applications

The cyanobacterium Limnospira platensis, vulgarly Spirulina, has gained significant attention due to its high protein content, rich bioactive compounds, and health benefits, making it a valuable resource in biotechnology, nutraceuticals, food supplements, biopharmaceuticals, and cosmetics. Recent advancements in fermentation technology have considerably improved the efficiency, scalability, and cost-effectiveness of L. platensis production while addressing environmental sustainability and enhancing product quality. Based on well-recognized databases (Google Scholar, PubMed, Scopus, Web of Science), this review explores the latest developments in L. platensis fermentation, emphasizing strain improvement, bioprocess engineering, and optimization of fermentation parameters. It also examines key factors such as bioreactor design, downstream processing, and innovative monitoring technologies aimed at maximizing biomass yield and bioactive compound production. Additionally, emerging applications of fermented L. platensis in various industries and future perspectives, including large-scale production, regulatory barriers, and biosafety considerations, are discussed. These insights provide a comprehensive outlook on the future of L. platensis fermentation in biotechnological applications.

Postimplementation Evaluation in Assisted Living Facilities of an eHealth Medical Device Developed to Predict and Avoid Unplanned Hospitalizations: Pragmatic Trial.

The proportion of very old adults in the population is increasing, representing a significant challenge. Due to their vulnerability, there is a higher frequency of unplanned hospitalizations in this population, leading to adverse events. Digital tools based on artificial intelligence (AI) can help to identify early signs of vulnerability and unfavorable health events and can contribute to earlier and optimized management. This study aims to report the implementation in assisted living facilities of an innovative monitoring system (Presage Care) for predicting the short-term risk of emergency hospitalization. We describe its use and assess its performance. An uncontrolled multicenter intervention study was conducted between March and August 2022 in 7 assisted living facilities in France that house very old and vulnerable adults. The monitoring system was set up to provide alerts in cases of a high risk of emergency hospitalization. Nurse assistants (NAs) at the assisted living facilities used a smartphone app to complete a questionnaire on the functional status of the patients, comprising electronic patient-reported outcome measures (ePROMs); these were analyzed in real time by a previously designed machine learning algorithm. This remote monitoring of patients using ePROMs allowed notification of a coordinating nurse or a coordinating NA who subsequently informed the patient's nurses or physician. The primary outcomes were the acceptability and feasibility of the monitoring system in the context and confirmation of the effectiveness and efficiency of AI in risk prevention and detection in practical, real-life scenarios. The secondary outcome was the hospitalization rate after alert-triggered interventions. In this study, 118 of 194 (61%) eligible patients were included who had at least 1 follow-up visit. A total of 38 emergency hospitalizations were documented. The system generated 92 alerts for 47 of the 118 (40%) patients. Of these 92 alerts, 46 (50%) led to 46 health care interventions for 14 of the 118 (12%) patients and resulted in 4 hospitalizations. The other 46 of the 92 (50%) alerts did not trigger a health care intervention and resulted in 25 hospitalizations (P<.001). Almost all hospitalizations were associated with a lack of alert-triggered interventions (P<.001). System performance to predict hospitalization had a high specificity (96%) and negative predictive value (99.4%). The Presage Care system has been implemented with success in assisted living facilities. It was well accepted by coordinating nurses and performed well in predicting emergency hospitalizations. However, its use by NAs was less than expected. Overall, the system performed well in terms of performance and clinical impact in this setting. Nevertheless, further work is needed to improve the moderate use rate by NAs. ClinicalTrials.gov NCT05221697; https://clinicaltrials.gov/study/NCT05221697.

AI-boosted and motion-corrected, wireless near-infrared sensing system for continuously monitoring laryngeal muscles

Neuromuscular diseases pose significant health and economic challenges, necessitating innovative monitoring technologies for personalizable treatment. Existing devices detect muscular motions either indirectly from mechanoacoustic signatures on skin surface or via ultrasound waves that demand specialized skin adhesion. Here, we report a wireless wearable system, Laryngeal Health Monitor (LaHMo), designed to be conformally placed on the neck for continuously measuring movements of underlying muscles. The system uses near-infrared (NIR) light that features deep-tissue penetration and strong interaction with myoglobin to capture muscular locomotion. The incorporated inertial measurement unit sensor further decouples the superposition of signals from NIR recordings. Integrating a multimodal AI-boosted algorithm based on recurrent neural network, the system accurately classifies activities of physiological events. An adaptive model enables fast individualization without enormous data sources from the target user, facilitating its broad applicability. Long-term tests and simulations suggest the potential efficacy of the LaHMo platform for real-world applications, such as monitoring disease progression in neuromuscular disorders, evaluating treatment efficacy, and providing biofeedback for rehabilitation exercises. The LaHMo platform may serve as a general noninvasive, user-friendly solution for assessing neuromuscular function beyond the anterior neck, potentially improving diagnostics and treatment of various neuromuscular disorders.

Editorial: Innovative process, monitoring and control in the wire arc additive manufacturing

Editorial: Innovative process, monitoring and control in the wire arc additive manufacturing

Safety Dynamic Monitoring and Rapid Warning Methods for Mechanical Shaft

In the context of urban space constraints, subway and underground projects have become crucial strategies to alleviate urban congestion and enhance residents’ quality of life. However, pit engineering, a frequent accident area in geotechnical engineering, urgently requires innovative safety monitoring technologies. Traditional monitoring methods face challenges such as high labor costs, lengthy monitoring cycles, high-risk working environments, and over-reliance on human judgment. To address these issues, this paper introduces an innovative monitoring system integrating Fiber Bragg Grating (FBG) sensing technology based on a subway pit project in Guangzhou. This system not only achieves fully automated data acquisition but also includes an intelligent monitoring cloud platform, providing unprecedented automated and intelligent monitoring solutions for support structures and the surrounding environment during mechanical shaft construction. The key findings of this paper include the following: (1) The breakthrough application of distributed optical fiber monitoring technology, including successfully deploying this advanced technology in complex pit engineering environments, enabling the precise and continuous monitoring of support structures and surrounding changes, and demonstrating its high effectiveness and intelligence in practical engineering. (2) The innovative design of an intelligent safety monitoring system. By integrating sensors and wireless communication technology, an efficient data networking architecture is constructed, supporting remote configuration and flexible adjustment of monitoring equipment, significantly enhancing data collection‘s real-time performance and continuity while greatly reducing safety risks for field staff, achieving an intelligent upgrade of monitoring work. (3) Comprehensive and accurate empirical analysis. During shaft excavation, the monitoring data collected by the system were stable and reliable, with all indicators maintained within reasonable ranges and closely matching expected changes caused by construction activities, validating the system’s practical application effectiveness in complex construction environments and providing a scientific basis for pit engineering safety management.

Application of AMIS-optimized vision transformer in identifying disease in Nile Tilapia

Efficient health monitoring in Nile tilapia aquaculture is critical due to the substantial economic losses from diseases, underlining the necessity for innovative monitoring solutions. This study introduces an advanced, automated health monitoring system known as the “Automated System for Identifying Disease in Nile Tilapia (AS-ID-NT),” which incorporates a heterogeneous ensemble deep learning model using the Artificial Multiple Intelligence System (AMIS) as the decision fusion strategy (HE-DLM-AMIS). This system enhances the accuracy and efficiency of disease detection in Nile tilapia. The research utilized two specially curated video datasets, NT-1 and NT-2, each consisting of short videos lasting between 3–10 s, showcasing various behaviors of Nile tilapia in controlled environments. These datasets were critical for training and validating the ensemble model. Comparative analysis reveals that the HE-DLM-AMIS embedded in AS-ID-NT achieves superior performance, with an accuracy of 92.48% in detecting health issues in tilapia. This system outperforms both single model configurations, such as the 3D Convolutional Neural Network and Vision Transformer (ViT-large), which recorded accuracies of 84.64% and 85.7% respectively, and homogeneous ensemble models like ViT-large-Ho and ConvLSTM-Ho, which achieved accuracies of 88.49% and 86.84% respectively. AS-ID-NT provides a non-invasive, continuous, and automated solution for timely intervention, successfully identifying both healthy and unhealthy (infected and environmentally stressed) fish. This system not only demonstrates the potential of advanced AI and machine learning techniques in enhancing aquaculture management but also promotes sustainable practices and food security by maintaining healthier fish populations and supporting the economic viability of tilapia farms.

Evaluation of IMPALA 2.0: Addressing Patient Monitoring in Low-Resource Hospitals in Malawi.

BACKGROUND Patient monitoring systems (PMSs) are essential for monitoring and managing the condition of critically ill patients. In low-resource settings, limited access to technology, low-level digital literacy, and power outage challenges are usability concerns. The main aim of this study was to evaluate the usability of the IMPALA (Innovative Monitoring in Paediatrics in Low-resource settings: an Aid to save lives) PMS optimized for use in low-resource settings by assessing the opinions and experiences of 24 healthcare professionals. MATERIAL AND METHODS The study used a mixed-method design, combining quantitative and qualitative approaches. Quantitatively, 24 participants (nurses and clinicians) completed the Usefulness, Satisfaction, and Ease of Use questionnaire to assess the PMS usability. Qualitatively, contextual inquiry and co-design sessions provided insights into users' experiences and identified usability issues. Data were analyzed using descriptive statistics and thematic analysis. RESULTS The PMS was rated 9.13 for usefulness, 8.49 for user satisfaction, 7.83 for ease of use, and 7.60 for ease of learning. Reported challenges included lack of knowledge/skills due to limited previous exposure (70.8%), frequent sensor detachment (58.3%), inaccurate SpO₂ readings (37.5%), and frequent/false alarms (33.3%). Contextual inquiry revealed that patient movement and poorly fitting sensors often caused inaccurate readings, leading to false alarms and potential patient safety risks. CONCLUSIONS Successful implementation of PMSs in a low-resource setting requires specific contextual user-centered design and training. Applying this, the IMPALA system yielded high usability scores. Further improvement should focus on expanded battery life, robust and durable SpO₂ sensors, and tailored training methods.

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

Aquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

Analysis of Football Pitch Performances Based on Different Cutting Systems: From Visual Evaluation to YOLOv8

The quality of sports facilities, especially football pitches, has gained significant attention due to the growing importance of sports globally. This study examines the effect of two different cutting systems, a traditional ride-on mower and an autonomous mower, on the quality and functional parameters of a municipal football field. The analysis includes visual assessments, measurements of grass height, and evaluations of surface hardness, comparing the performance of the two cutting systems. Additionally, studies of turfgrass composition and machine learning techniques, particularly with YOLOv8s and YOLOv8n, are conducted to test the capability of assessing weed and turfgrass species distribution. The results indicate significant differences in grass color based on the position (5.36 in the corners and 3.69 in the central area) and surface hardness between areas managed with a traditional ride-on mower (15.25 Gmax) and an autonomous mower (10.15 Gmax) in the central region. Higher height values are recorded in the area managed with the ride-on mower (2.94 cm) than with the autonomous mower (2.61 cm). Weed presence varies significantly between the two cutting systems, with the autonomous mower demonstrating higher weed coverage in the corners (17.5%). Higher overall performance metrics were obtained through YOLOv8s. This study underscores the importance of innovative management practices and monitoring techniques in optimizing the quality and playability of a football field while minimizing environmental impact and management efforts.

Unlocking Better Asthma Control: A Narrative Review of Adherence to Asthma Therapy and Innovative Monitoring Solutions.

This review addresses the ongoing challenges in asthma management, particularly focusing on patient adherence to inhaler therapy. Asthma, a chronic condition characterized by variable respiratory symptoms and airflow obstruction, can lead to significant morbidity and mortality if not properly managed. Despite advances in inhaler technology and therapeutic options, non-adherence remains a significant barrier to optimal asthma control. This review explores both intentional and unintentional non-adherence, influenced by factors such as age, socioeconomic status, and the complexity of inhaler devices. The Global Initiative for Asthma (GINA) provides guidelines aimed at improving adherence through targeted interventions, and this review examines their application. Common inhaler technique errors, including incorrect inhalation speed, not exhaling before inhaling, and failure to hold breath post-inhalation, are identified as major contributors to inadequate asthma control. Furthermore, the review explores the emerging role of electronic monitoring devices (EMDs), such as CapMedic and DigiHaler, which offer real-time feedback to enhance inhaler technique and adherence. The role of biomarkers in assessing adherence and the potential of personalized treatment strategies, including biologic therapies, are also discussed. Overall, addressing adherence requires a comprehensive approach that integrates patient education, tailored interventions, and technological innovations to achieve better clinical outcomes in asthma management.

Wearable device data-driven athlete injury detection and rehabilitation monitoring algorithm

In recent years, sports wearable technology has completely changed the way athletes prepare, compete, and recover. Wearable technology has a lot to offer in the rehabilitation process, which is essential to an athlete’s return to their best performance. Wearable devices for athlete injury detection pose potential challenges like data quality, security, and privacy, impacting accuracy, reliability, and effectiveness. To solve these problems, an innovative injury detection and rehabilitation monitoring (IDRM) system was proposed for athletes. By employing an adjustable recurrent neural network (ARNN) to detect anomalies in injury risks such as abnormal joint movements in athletes. In this study, biomechanics data was collected from sports athletes through wearable devices, and the wearable system provided feedback to the user. A redefined convolutional neural network (RCNN) was utilized to monitor the rehabilitation process. This system tracks athlete’s rehabilitation progress and ensures that progress monitors were performed correctly, and the system, feasibility was evaluated on 10 healthy subjects performing 4 different rehabilitation exercises. Each exercise was performed four times monitoring and validation. The data was preprocessed using a Gaussian filter to remove noise from the obtained data. Then the features are extracted using independent component analysis (ICA) for dimensionality reduction from preprocessed data. The proposed method is implemented using Python software. In comparative analysis, the performance of ARNN showed high performance, with an F1-measure of 91.6%, accuracy of 93.5%, recall of 92.8%, and precision of 91.4%. With a 95% accuracy rate, 98% F1 measure, 94% precision, and 93% recall, the RCNN model functioned effectively. The result showed the proposed method achieved better performance in athlete injury detection and accurately recognizing all rehabilitation monitoring. This study provides a complete approach to athlete health management by highlighting the integration of rehabilitation monitoring and injury detection into an overall structure.

Monitoring soil cracking using OFDR-based distributed temperature sensing framework

Soil cracking induced by extreme drought represents a widespread natural phenomenon occurring across the earth surface, capable of triggering multiple weakening mechanisms within surface soils, potentially leading to the instability and failure of slopes and agricultural infrastructures. This study proposes an innovative geophysical monitoring framework for detecting field soil cracking by combining the actively heated fiber-optic (AHFO) method and distributed fibre optical sensing (DFOS) based on optical frequency domain reflectometry (OFDR) technique, referred to as AH-OFDR framework. Laboratory calibration tests, field monitoring tests, numerical simulations, and sensitivity analyses were employed to comprehensively evaluate the feasibility, effectiveness, and limitations of the AH-OFDR framework for soil crack monitoring. Laboratory calibration confirmed that the DFOS-OFDR technique achieves a minimum spatial resolution and readout accuracy of 1 mm, along with a temperature measurement accuracy of ±0.1 °C. Field monitoring verified that the AH-OFDR framework can accurately detect soil cracks ranging in width from 0.01 m to 0.12 m. Additionally, numerical simulations not only validated the effectiveness of the AH-OFDR framework across a broader range of crack widths, from 0.01 m to 0.50 m, but also established a quantitative relationship between temperature changes and the spatial distribution of crack positions and widths. Notably, a critical crack width threshold of 0.30 m was identified within the AH-OFDR framework, significantly impacting the prediction of soil crack widths. Sensitivity analysis demonstrated the remarkable crack detection capabilities of the AH-OFDR framework, irrespective of the soil crack width and spacing. The AH-OFDR framework holds substantial potential as an innovative and high-resolution observational method for advancing our understanding of diverse geological and hydrogeological processes.