Development Of Monitoring System Research Articles

System of Monitoring the Financial Activity of the Enterprise

Monitoring of financial activity is one of the key functions of enterprise management, because it, in particular, ensures control of current costs and income; contributes to the analysis of the financial state of the enterprise; supports planning of future financial flows. The purpose of the article is the research of information systems for monitoring the financial activity of enterprises, the identification of problems of financial activity, the analysis of the ways of using the appropriate software and the development of the system for monitoring the financial activity of enterprises. The research methods are the main methodological approaches and technological tools for the development of financial monitoring systems. Such methods, in particular, are: systematic and comparative analyzes – to identify the peculiarities of the creation of systems for monitoring the financial activity of enterprises; the method of expert assessments, which involves the analysis of literary sources and information resources, conducting interviews and surveys of experts, as well as the processes of development and testing of scalable and high-performance financial monitoring systems. The novelty of the research is the creation of a complex system for monitoring financial activity, which includes an intuitive interface, a flexible database and the ability to generate various reports, giving users the opportunity to effectively manage their financial data, simplifying the process of entering, editing and analyzing information through the use of a stack of modern technologies. The conclusion of the research carried out in the article is that the work was: identified the main problems of monitoring the financial activity of enterprises, which are solved with the help of modern financial monitoring systems; an analysis of existing software solutions was carried out; the advantages and disadvantages of the existing software for monitoring the financial activities of enterprises were analyzed; the main so-called «bottlenecks» of the automation of financial monitoring, which need to be solved, have been identified. On the basis of the conducted analysis, an author’s system for monitoring the company’s financial activity was developed, focused on ease of use and efficiency of financial data management. The development of the software of the author’s system included: the selection of the appropriate technological stack; system architecture development; implementation of basic functional modules (for example, such as transaction management, categories and reports); system database development; system interface development. The results of the conducted research open up new opportunities for improving financial management and making informed decisions based on the received data.

Development of intelligent systems for monitoring and management of agricultural enterprises

Modern agriculture faces a number of challenges, among which are the growing demand for food, the impact of climate change, limited natural resources and the need to ensure the environmental sustainability of production. In this context, the introduction of new technologies, in particular intelligent systems, becomes an important tool for improving the efficiency and competitiveness of agro-industrial enterprises. Intelligent Systems Based on Use artificial intelligence, machine learning, the Internet of Things (IoT) and big data are opening up new opportunities for monitoring and managing agricultural processes. The article presents the results of a study aimed at the development of intelligent systems that provide comprehensive monitoring of the state of fields and management of production processes at agricultural enterprises. The proposed system integrates data from various sources, such as sensors, drones, satellite images and other IoT devices, which allows you to create a detailed and up-to-date view of the state of crops, soils and climatic conditions. This, in turn, allows you to make prompt and sound management decisions that increase the productivity and efficiency of agricultural production. Particular attention is paid to the development of algorithms for processing big data and machine learning methods, which are used to analyze the data obtained and generate recommendations for optimizing the use of resources. This includes managing water resources, fertilizers, crop protection products, as well as predicting yields and identifying potential threats such as plant diseases or pests. The implementation of such systems allows not only to reduce resource costs, but also to minimize the environmental impact of agriculture by reducing the use of chemicals and water. Thus, the article makes a significant contribution to the development of intelligent technologies in agriculture, offering new approaches to the management and monitoring of agricultural enterprises. These approaches can form the basis for the development of future innovations in this area, contributing to improving the resilience and efficiency of agro-industrial production in response to modern challenges.

Development of an intraoperative monitoring system for microwave ablations

Abstract Microwave ablation therapy is frequently used to treat liver malignancies. To ensure proper tumor treatment, intraoperative feedback regarding ablation performance and lesion size is required. By employing an electrode array around the ablation needle, changes in electrical impedance of ex vivo liver are measured in real time. Time-series trends of magnitude and phase are measured for 90 °C and 110 °C ablation temperatures. A finite element model is additionally configured to simulate the underlying biological processes. Gradients in the magnitude and phase trends can indicate the growth of the ablation zone. In combination with a preoperative simulation, impedance-based ablation monitoring can be a possible tool to improve future treatments.

Experimental Investigation of Chloride Ion Monitoring in Concrete Considering the Coupling Effect of Temperature and Humidity

Ag/AgCl electrodes have acquired significant research attention due to their crucial role in the electrochemical monitoring of chloride ion concentration. However, the electrode potential is susceptible to changes in temperature and humidity, and comprehensive investigations regarding the influence of moisture are lacking. Existing studies on Ag/AgCl electrodes have been qualitative, primarily focusing on analyzing the trend of electrode potential to detect variations in Cl- concentration. This study proposes an experimental method that enables precise control of temperature and humidity within concrete. The effects of temperature and humidity coupling on Ag/AgCl electrode potential in a concrete environment and a fundamental model were established correlating Ag/AgCl electrode potential with temperature, humidity, and chloride ion concentration, thereby enhancing the sensing accuracy. The developed model corrected errors from temperature and humidity coupling effects on the Ag/AgCl electrode potential. A quantitative investigation on using Ag/AgCl electrodes for monitoring Cl- concentration was conducted, facilitating their application in engineering and the development of non-destructive monitoring systems for chloride ions in concrete structures.

Design and Implementation of a Temperature Monitoring System in Industrial Environments

Abstract The proposed paper presents the development of a temperature monitoring system designed for industrial environments, based on a Raspberry Pi 3 microcomputer equipped with a touchscreen and a K-type thermocouple connected via the MAX6675 module. The system enables temperature monitoring and recording through an intuitive interface, providing users with real-time visualization and data logging functionalities. The touchscreen interface allows for easy and efficient interaction, enabling operators to quickly access essential information. The system was implemented and tested on an industrial oven capable of reaching temperatures up to 800°C, demonstrating stability and reliability in extreme environmental conditions. Experimental results show that the system provides precise monitoring and enhanced control over high-temperature industrial processes.

Ambrosia (Ragweed) pollen - a growing aeroallergen of concern in South Africa

BackgroundRagweed is an invasive, highly allergenic weed predicted to expand its habitat with warming global temperatures. Several Ambrosia species have been identified in South Africa for well over a century, however, its presence remained undetected by allergists and aerobiologists until the development of an extensive aerospora monitoring system across South African urban areas since 2019. This paper presents the inventory of preliminary investigation of the Ambrosia airborne pollen and the taxonomic identification of ragweed species. MethodsBurkard volumetric spore traps for collecting pollen samples are set up in nine South African cities (Johannesburg, Cape Town, Pretoria, Kimberley, Durban, Potchefstroom, Ermelo, Bloemfontein and Gqeberha). Light microscopic identification was combined with environmental DNA metabarcoding analysis to confirm the species level of airborne Ambrosia at selected monitoring stations. Ragweed sensitization was examined in Cape Town between February 2019 and February 2024, using Allergy Xplorer (ALEX2) multicomponent allergen array. ResultsAmbrosia pollen was detected in five aerobiological monitoring stations over the sampling period (Durban, Kimberley, Pretoria, Potchefstroom, Johannesburg). Periods of four consistent pollination years were observed in Kimberley (min: 1; max: 16 p.g/m3) and Durban (min: 26; max: 66 p.g/m3). In Pretoria, ragweed pollen was detected for two years (2020-2021; 2022-2023) with average total annuals (5-17 p.g/m3). A peak flowering period between March and April was observed in Potchefstroom, and several ragweed pollen peaks were present between the end of December and the beginning of May in Durban. The highest number of Ambrosia pollen grains was recorded in Potchefstroom, with 308 grains, and a maximum peak of 47 p.g/m3. eDNA metabarcoding confirmed the presence of Ambrosia artemisiifolia and A.trifida species. The overall prevalence of Ambrosia-sensitisation amongst 673 tests (age range 7-72 years) was 8.2% (55/673), with no significant difference in sensitisation patterns between age groups. ConclusionOur study confirms the need to monitor the spread of ragweed, and an increasing awareness of Ambrosia as an allergen of concern in Southern Africa. Extension of aerobiological networks and testing for Ambrosia sensitisation across urban and rural sites will be required.

Using random forests to explore the feasibility of groundwater knowledge transfer between the contiguous US and Denmark

Abstract Groundwater is our largest freshwater reservoir, playing an important role in the global hydrologic cycle. Lack of reliable groundwater data restricts the development of global groundwater monitoring systems linking observations with modeling at spatial scales relevant for local decision making. Despite the growing interests in machine learning (ML) for groundwater resource modeling, taking ML models to the global scale is still outstanding due to sparse groundwater data. The contiguous US (CONUS) has extensive groundwater information covering a wide range of hydrogeologic settings. We hypothesize that a ML model trained on the CONUS is transferable to other regions, and thus can be used to produce a global water table depth (WTD) map within the bounds of transferability. To test this hypothesis, we conduct a study on transferring groundwater knowledge between the CONUS and Denmark, using several random forest models trained against ∼30 m resolution long-term mean WTD data. The joint model trained on data from the CONUS and Denmark outperforms the individual models trained separately, implying similarities within global groundwater systems. The largest improvement occurs in Denmark, where the testing Nash-Sutcliffe efficiency rises from 0.68 to 0.95. SHapley Additive exPlanations (SHAP) values are utilized to express the importance of input variables. While annual mean precipitation plays a key role in the joint model and the model for the CONUS, it is the second least important input variable in the model for Denmark where local processes dominate. Moreover, Köppen-Geiger climate classification shows a significant impact on the model testing performance and the importance ranking of input variables, which might be a missing input variable in the applied random forest models. This study provides unique insights into future ML model developments towards global groundwater monitoring and improves our confidence in producing a hyper-resolution global WTD map for sustainable freshwater management.

Photoelectric sensor-based belt-type high-speed seed guiding device performance monitoring method and system

The belt-type high-speed seed guiding device encounters difficulties in seed guidance due to its distinctive seed transporting belt structure. The monitoring point is situated between the seed belt support plate and the seeds, which pass through it, making it difficult to distinguish the characteristics of the seed pulses. This presents challenges in monitoring the device’s performance. This study proposes a monitoring method for the belt-type high-speed seed guiding device using photoelectric sensors. The monitoring module employs a photoelectric sensor as a signal source and designs hardware and software for the monitoring module. The research introduces the deviation pulse extraction algorithm (DPEA), which utilises the seed guidance performance monitoring mechanism. This achieves precise monitoring of seed transport on the transporting seed belt, thereby eliminating the increased multiple index caused by “pulse sinking”. Simulation experiments demonstrate that the optimal deviation pulse extraction algorithm (ODPEA) is highly effective in identifying and eliminating “pulse sinking”, resolving the issue of increased multiple index. The DPEA is unable to achieve the same effect. The results of comparative tests of seeding monitoring accuracy indicate that both algorithms exhibit average monitoring accuracy above 96% across varying operating speeds. The DPEA, however, demonstrates heightened sensitivity to changes in operating speed. It can be observed that the operating speed has a significant impact on the monitoring accuracy of both algorithms. The monitoring system performance evaluation comparative test reveals that the DPEA-based monitoring system registers the smallest deviations in qualified feed index, miss index, and multiple index at 0.25 percentage points, 0.10 percentage points, and 0.35 percentage points, respectively. In contrast, the ODPEA-based system shows minimum deviations in qualified feed index and miss index at 0.12 percentage points each, and no deviation in multiple index. Both algorithms demonstrate minimal influence of operating speed on missing and multiple seeding rates. The impact of changes in operating speed on these metrics is significantly lower. The efficacy of the ODPEA in eliminating ’pulse sinking’ enhances the algorithm’s ability to assess multiple seeding rates and overall accuracy. The ODPEA-based monitoring system exhibits superior performance, providing theoretical support for the development of monitoring systems in high-speed belt-type seed guiding devices.

Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions

Monitoring and accurately quantifying greenhouse gas (GHG) emissions from point sources via satellite measurements is crucial for validating emission inventories. Numerous studies have applied varied methods to estimate emission intensities from both natural and anthropogenic point sources, highlighting the potential of satellites for point source quantification. To promote the development of the space-based GHG monitoring system, it is pivotal to assess the satellite’s capacity to quantify emissions from distinct sources before its design and launch. However, no universal method currently exists for quantitatively assessing the ability of satellites to quantify point source emissions. This paper presents a parametric conceptual model and database for efficiently evaluating the quantification capabilities of satellites and optimizing their technical characteristics for particular detection missions. Using the model and database, we evaluated how well various satellites can detect and quantify GHG emissions. Our findings indicate that accurate estimation of point source emissions requires both high spatial resolution and measurement precision. The requirement for satellite spatial resolution and measurement precision to achieve unbiased emission estimation gradually decreases with increasing emission intensity. The model and database developed in this study can serve as a reference for harmonious satellite configuration that balances measurement precision and spatial resolution. Furthermore, to progress the evaluation model of satellites for low-intensity emission point sources, it is imperative to implement a more precise simulation model and estimate method with a refined mask-building approach.

Automatic Drainage Monitoring and Control System

This paper presents the development and implementation of an automatic monitoring and control system for manholes. The system integrates sensors to monitor temperature, altitude, pressure, and gas levels in drain water. An ultrasonic sensor, controlled by Arduino, detects water levels, triggering a motor to pump out water when a specific threshold is exceeded, thereby preventing clogs. A GPS module provides real-time location data, aiding municipal personnel in efficient management. The system aims to enhance urban drainage maintenance, ensuring prompt response to potential hazards and maintaining optimal functionality of the drainage network.

Effects of noise intensity on early warning indicators of thermoacoustic instability: An experimental investigation on a lean-premixed combustion system

In this work, we experimentally investigate the noise-induced dynamics of a lean premixed combustion system operating on natural gas–air mixtures that exhibit thermoacoustic instability via a subcritical Hopf bifurcation. The investigation is done before the bistable region with equivalence ratio (ϕ) as the control parameter. We analyze the acoustic pressure oscillations (p′) in the combustor and fluctuations in the heat release rate (q′) from the laminar quasi-flat flame at increasing levels of white noise. We show the effects of noise intensity on the reliability of various types of early warning indicators (EWIs) to predict the onset of the impending thermoacoustic oscillations. We investigate the indicators based on statistical measures (variance, skewness, and kurtosis), autocorrelation and spectral properties (coherence factor), system identification (growth/decay rates of p′), multi-fractality (Hurst exponent), and time series complexity (permutation entropy and Jensen–Shannon complexity). The coherence factor, variance, and decay rates of p′ always increases as the system approaches thermoacoustic instability, indicating their robustness as an EWI under most noise levels. An increase in kurtosis cannot be employed as an EWI. Implementing autocorrelation, skewness, Hurst exponent, permutation entropy and Jensen–Shannon complexity as effective EWIs has limitations: they can be estimated accurately only from pressure oscillations (p′) data and work only above a particular threshold value of noise intensity. Our results have direct implication on early prediction and control of thermoacoustic instability in practical gas turbine combustors.Novelty and significance statementDeveloping effective early warning indicators (EWIs) to anticipate the onset of thermoacoustic instability is crucial for preventing potential damage and ensuring the reliable operation of lean premixed gas turbine combustion systems. In such systems, inherent noise dynamics undergo variations with changing operating conditions and combustor designs. Specifically, noise intensity increases as the system becomes more turbulent. In this study, we demonstrate that the inherent noise dynamics in a lean premixed combustion system play a crucial role in influencing the trends observed in early warning indicators of thermoacoustic instability. We address several key questions, including (a) whether comparative reliability assessments of different classes of EWIs exist, (b) the effect of variations in noise properties on the efficacy of EWIs, and (c) which EWIs are most reliable considering that noise characteristics depend on the system and may even change with operating conditions within a specific system. This information is critical for engine designers/users, facilitating the development of robust and effective monitoring systems for gas turbine combustion systems.

Soil Health Metric: IoT-based Agriculture Monitoring System

Agriculture has been an essential component of human civilization, experiencing considerable evolution over the centuries. In contemporary society, technological advancements, particularly the Internet of Things (IoT), have emerged as transformative forces, facilitating precision farming and enhancing resource management efficiency. This research examines the development and implementation of an IoT-based agricultural monitoring system. By utilizing components such as sensors, wireless networks, and data analytics, this system offers real-time insights into soil moisture, temperature, humidity, and water levels. The automation will be integrated into optimal resource utilization, minimization of waste, and increased productivity. This paper outlining the operation, parts, and benefits of IoT-based farming systems with a perspective on its transformation of agriculture and the problem of sustainability and its relation to the environment.

Intelligent Inspection Method for Rebar Installation Quality of Reinforced Concrete Slab Based on Point Cloud Processing and Semantic Segmentation

The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the “diameter calculation region” and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems.

Development of a Light Intensity Monitoring System in Broiler Chicken Coops Based on Arduino IoT Cloud

The lighting system significantly influences the growth and productivity of broiler chickens, necessitating an efficient tool for monitoring light intensity in chicken coops. This study aims to design and develop an Arduino IoT Cloud-based light intensity monitoring system for broiler chicken coops. The system employs two BH1750 light sensors, positioned inside and outside the coop, to measure light intensity, which is then processed by an ESP32 microcontroller. The collected data is displayed on a 16x2 LCD screen and uploaded in real time to the Arduino IoT Cloud platform for remote monitoring. Testing conducted over a 6-hour period revealed that light intensity inside the coop remained stable at approximately 30 lux from 09:30 to 12:52, while the highest external light intensity reached 400 lux between 12:06 and 14:40, followed by a decrease after 14:50. These findings indicate that the system can assist farmers in timely actions, such as opening or closing coop curtains, through effective monitoring via the Arduino IoT Cloud platform.

The development of physical state and exhaustion factors monitoring system with capability of collecting, estimating and analysing data for military personnel

The purpose of the research analysis of known invasive and non-invasive examination methods, determination of the necessary criteria for assessing the degree of fatigue of submariners, a survey of Navy personnel involved in service on submarine ships was conducted, on the basis of which a justification of research directions was carried out for the subsequent development of a system for collecting, evaluating and analyzing the physiological state and factors affecting the fatigue of military personnel.Methods: the use of an experimental non-invasive research method to determine the degree of fatigue and physiological condition of submariners based on three criteria during the month, the analysis of the results obtained, the establishment of a comparative analysis method during the development, a structural analysis of the system based on previously known devices and sensors.Results. The dependence of the studied indicators on the time of watch keeping and their connection with the increase in fatigue were found. It has been established that in order to develop a system for monitoring the fatigue of submariners, a systematic analysis and objective assessment should be carried out, including: determination of methods for monitoring the condition of military personnel; application of knowledge from medicine, physiology, biophysics, biomechanics, bioengineering, electronics, circuitry and programming, to develop effective methods for monitoring physical condition and fatigue. Based on the analysis of domestic and foreign techniques and devices, a proprietary method for implementing a system for collecting assessments, analyzing the physiological state and factors affecting the fatigue of military personnel is proposed.Conclusion. It is concluded that it is necessary to conduct non-invasive studies of the degree of fatigue, before proposing the development and proposal of its own device and methodology for assessing the degree of fatigue, capable of implementing individual features of the system for operational monitoring in order to improve combat readiness, situational awareness, health, safety, mobility of submariners.The development of such monitoring systems and devices is necessary in the modern world to prevent or prevent disasters without reducing the efficiency of submariners.

A Comprehensive Review of Water Quality Analysis

The effluent discharge of domestic wastewater and industrial effluents into watercourses has necessitated the development of monitoring systems with the purpose of characterizing levels of pollution. Substantial levels of pollution in emerging nations have been exacerbated by rapidly growing populations and inefficient enforcement of sustainable management initiatives. The Water Quality Index (WQI) can help fill the gap between water quality reporting and monitoring by offering a simple and effective way to assimilate and communicate results from a large amount of data. WQIs are the kind of communication tools that can facilitate knowledge sharing between scholars and the general public. The degradation of natural water resources, such as lakes, streams, and estuaries, is the most significant problem facing humanity. Water that is not clean has far-reaching effects on all aspects of life. Water resource management is therefore essential if it is to maximize water quality. If data are analyzed and water quality can be predicted in advance, water pollution may be efficiently addressed. Although this subject has been the subject of numerous earlier studies, additional research is still required to fully understand the effectiveness, reliability, accuracy, and application of the present approaches to water quality management. In this research work, a comprehensive review has been conducted on water quality assessment using artificial techniques. It reviews 75 research works on water quality analysis. The data collected in each research work have been analyzed. Moreover, the type of water resource surface water, groundwater, drinking water as well has been analyzed. The parameters considered in each work are “Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD), Total Dissolved Solids (TDS), and Chemical Oxygen Demand (COD), chloride, hardness, alkalinity, nitrate–nitrite”. In addition, the Artificial Intelligence technique utilized for water quality analysis is also assessed. Finally, the research gaps identified in water quality detection are exhibited.

From dishwasher to river: how to adapt a low-cost turbidimeter for water quality monitoring.

This study presents the process of design and development of a low-cost turbidimeter for monitoring water quality, facilitating rigorous spatial-temporal variability analysis within large-scale hydrological systems. We propose a low-cost optical turbidimeter, modifying the existent SEN0189 turbidity sensor, Arduino boards, and additional sensors for temperature compensation. We compared a low-cost system with high-tech sensors, modifying the original low-cost SEN0189 probe for enhanced environmental performance. The three-step methodological framework involved prototype development, compensation for environmental factors, and preparation for future field deployment. Calibration equations with a high coefficient of determination and a temperature correction equation were established. We made adaptations to overcome field deployment challenges, including a 3-D printed sensor case, defining the relationship between measurement uncertainty and energy consumption, and specifying field installation guidelines. In summary, this study presents a comprehensive approach to a low-cost optical turbidity system, demonstrating its potential for accurate and affordable field deployment. We aim to address the critical need for sustainable inland water management tools, making this system a valuable contribution to environmental monitoring practices. We also aim to inspire similar development of open-source monitoring systems within our community.

Design Science Approach Comparing Ensemble Learning and Artificial Neural Networks for Uncertainty-Aware Helicopter Turbine Engines Health Monitoring

This work presents the development of an uncertainty-aware health monitoring system for helicopter turbine engines, focusing on improving operational availability and reducing maintenance costs. We address the critical issue of uncertainty quantification in data-driven fault detection and prognostics, essential for increasing system reliability. The project follows an iterative development cycle, incorporating multiple techniques for data processing, such as polynomial feature generation and data cleansing, and model development, including ensemble learning and artificial neural networks. Evaluation is performed using K-fold and group-fold cross-validation. The final solution consists of a cascaded ensemble learning model combining bagged linear regression built on polynomial features and random forest. This model demonstrates robust performance, achieving a test score of 0.955719 and a validation score of 0.886953, showcasing the effectiveness of uncertainty-aware machine learning methods in health monitoring systems.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Development of a digital monitoring system for fire blight in fruit orchards

Development of a digital monitoring system for fire blight in fruit orchards