Assessment System Research Articles

Integration of AI in Education and Didactic Childhood Advancement and Progress

The state of education in our country has been a contentious topic for many years. Numerous reforms have been done but there are still some problems – inefficient learning resources, is but one of the many. Due to inexperience on this topic this problem continues to plague the education system. However, taking an informed and educated approach with the interests of the students prioritized, we believe that many of these problems could be solved with the use of Artificial Intelligence (AI). Its proper integration into the education system could aid in the improvement of efficiency of learning resources, through the use of various tools like chatbots, adaptive learning systems and intelligent assessment systems. By implementing the use of AI in a world where learning resources are scarce, there can be standardized learning throughout the country regardless of availability of learning materials. The main agenda of this study is to explore ways of implementing the use of Artificial Intelligence into educational systems to integrate the use of learning materials, hence bringing about improved quality of education. We are fully aware of the problems facing the implementations of this study but we are ready to conduct rigorous research to combat them.

Application of Electroencephalography Sensors and Artificial Intelligence in Automated Language Teaching.

This study developed an automated language learning teaching assessment system based on electroencephalography (EEG) and differential language large models (LLMs), aimed at enhancing language instruction effectiveness by monitoring learners' cognitive states in real time and personalizing teaching content accordingly. Through detailed experimental design, the paper validated the system's application in various teaching tasks. The results indicate that the system exhibited high precision, recall, and accuracy in teaching effectiveness tests. Specifically, the method integrating differential LLMs with the EEG fusion module achieved a precision of 0.96, recall of 0.95, accuracy of 0.96, and an F1-score of 0.95, outperforming other automated teaching models. Additionally, ablation experiments further confirmed the critical role of the EEG fusion module in enhancing teaching quality and accuracy, providing valuable data support and theoretical basis for future improvements in teaching methods and system design.

Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction.

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a ±1 scale point with 97% accuracy. These results demonstrate the system's potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously.

Adopting dual-tasks in small-sided games training in youth soccer: The influence of experience level on tactical performance

This study assessed the influence of experience level on the cost of tactical performance due to a secondary task in soccer players during Small-Sided Games (SSG). The study involved 36 less experienced soccer athletes (under-13 category) and 36 more experienced athletes (under-17 category), totaling 72 male players from two soccer teams. Participants performed, in a randomized and balanced order, four experimental conditions composed of the following protocols: (1) Single-Task (ST) the 3 vs. 3 SSG, (2) Motor Dual-Task (MDT): SSG while balancing a basketball with a small cone, (3) Cognitive Dual-Task 1 (CDT1): SSG while performing mathematical operations, (4) Cognitive Dual-Task 2 (CDT2): SSG while counting opponent's passes. The SSGs were filmed for players’ tactical assessment using the Football Tactical Assessment System. The task cost was analyzed using two-way mixed ANOVA and Bonferroni post-hoc tests. The comparison between categories indicated that the under-17 category showed a lower cost in the tactical principles of penetration (p = 0.007) and concentration (p = 0.025). In comparison, the under-13 category showed a lower cost in the tactical principles of width and length without the ball (p = 0.007), delay (p = 0.001), and defensive balance (p = 0.025). In comparing protocols, MDT showed a higher cost in the tactical principle of width and length without the ball (p = 0.002). There was no significant difference between CDT1 and CDT2 protocols (p > 0.05). The results suggest that experience level reduces the cost of the secondary task involving actions in progression with the ball, and the MDT, CDT1, and CDT2 protocols appear to generate similar costs to performance.

Credit Anomaly Detection Method based on Bayesian Networks

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.

A Comparative Study on ERNIE Bot 4.0 Turbo and ChatGPT 4o’s Performance in Evaluating First-Year Undergraduate Persuasive Essays

This study compares the performance of two prominent AI language models, ERNIE Bot 4.0 Turbo and ChatGPT 4o, in evaluating first-year undergraduate persuasive essays within the social sciences domain. Drawing from the Louvain Corpus of Native English Essays, a comprehensive collection of academic writings by British and American university students, this study aims to examine the models’ capabilities in assessing the grammatical correctness, vocabulary usage, coherence, content depth, and writing style of the essays. This study adopts a structured evaluation framework based on IELTS writing criteria to assess the models’ performance. A 40 persuasive essays from the Louvain Corpus were evaluated by both AI models and compared with human raters’ evaluations to ensure validity. The findings reveal distinct differences in the assessment styles of the two models. ChatGPT 4o exhibits a more critical approach, pinpointing areas for improvement, such as lack of argument development, coherence issues, and grammatical errors. Conversely, ERNIE Bot 4.0 Turbo offers a more balanced assessment, acknowledging essays’ strengths and suggesting improvement areas. Notably, ERNIE Bot’s evaluation highlights potential biases in AI-based assessment systems, particularly in its unequal emphasis on viewpoints. This comparative examination offers valuable perspectives on the advantages and constraints of AI models in assessing scholarly compositions, underscoring the significance of amalgamating varied AI functionalities to establish more all-inclusive and efficient feedback systems for learners. By understanding these differences, researchers and educators can better utilize AI-assisted essay evaluation systems to enhance student learning experiences.

Student-Centered Learning: Some Issues and Recommendations for Its Implementation in a Traditional Curriculum Setting in Health Sciences

This review article discussed the challenges faced during the implementation of active learning methods within the traditional teaching environment at the Complutense University of Madrid. These challenges encompass interactions with colleagues, students, and the classroom itself. It is essential for educators to have confidence that the outcomes achieved through these teaching approaches will not be adversely impacted by the quality assessment system of surveys. The university should adopt a teaching quality assessment framework that differentiates between the instructor’s efficiency and the effectiveness of teaching methods. Students need to recognize the significance of developing critical thinking skills over rote memorization throughout their academic journey and understand the importance of this for their future professional development. Suggestions are also made for improving teamwork in traditional classrooms, which typically follow standard teaching methods.

Artificial Intelligence-Assisted Music Education: A Critical Synthesis of Challenges and Opportunities

Artificial intelligence (AI) is a hot topic that presents new challenges and opportunities for the improvement of educational processes. The disruptive and transformative force of this new technological development implies the adaptation of educational ecosystems for its use and integration as a didactic and pedagogical resource. From this perspective, a systematic literature review has been conducted to analyze the didactic potential of generative AI tools in the field of promoting artistic creativity in music education. The research results confirm that the incorporation of AI in music education is paving the way for a more personalized, interactive and efficient learning experience. In addition, the analysis suggests nine fundamental fields of IA implementation in music education: virtual and augmented reality (VR; VA); learning personalization, intelligent tutoring systems; composition assistants; improved historical and contextual learning; assessment systems; interactive ear training and music theory systems; tools for music collaboration and performance; and assistive technologies. Furthermore, the challenges presented by the intersection of AI and digital didactics in the field of music education are discussed.

An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images

In recent years a significant demand to develop computer-assisted diagnostic tools to assess prostate cancer using whole slide images has been observed. In this study we develop and validate a machine learning system for cancer assessment, inclusive of detection of perineural invasion and measurement of cancer portion to meet clinical reporting needs. The system analyses the whole slide image in three consecutive stages: tissue detection, classification, and slide level analysis. The whole slide image is divided into smaller regions (patches). The tissue detection stage relies upon traditional machine learning to identify WSI patches containing tissue, which are then further assessed at the classification stage where deep learning algorithms are employed to detect and classify cancer tissue. At the slide level analysis stage, entire slide level information is generated by aggregating all the patch level information of the slide. A total of 2340 haematoxylin and eosin stained slides were used to train and validate the system. A medical team consisting of 11 board certified pathologists with prostatic pathology subspeciality competences working independently in 4 different medical centres performed the annotations. Pixel-level annotation based on an agreed set of 10 annotation terms, determined based on medical relevance and prevalence, was created by the team. The system achieved an accuracy of 99.53% in tissue detection, with sensitivity and specificity respectively of 99.78% and 99.12%. The system achieved an accuracy of 92.80% in classifying tissue terms, with sensitivity and specificity respectively 92.61% and 99.25%, when 5x magnification level was used. For 10x magnification, these values were respectively 91.04%, 90.49%, and 99.07%. For 20x magnification they were 84.71%, 83.95%, 90.13%.

LANDSLIDE DETECTION AND MONITORING TECHNIQUES: A BIBLIOMETRIC ANALYSIS AND FUTURE DIRECTIONS

The devastating consequences of landslides demand rigorous assessment and effective management strategies to minimise risks. In this context, landslide detection and monitoring are crucial in identifying potential hazards and implementing proactive mitigation measures. This study uses the Scopus database to present a bibliometric analysis of 1,112 peer-reviewed journal articles on landslide detection and monitoring techniques published between 2002 and 2022. The analysis reveals a remarkable surge in publications, from 4 in 2002 to 180 in 2022, with the last five years (2018-2022) accounting for over 60% of the total publications. Over 3,259 unique researchers from 75 countries contributed to this field, with a significant portion originating from Asia, Europe, and North America. China and Italy emerged as the leading countries, producing over half of the articles. Nicola Casagli, from Italy, authored the most publications and garnered the highest number of citations. Remote Sensing emerged as the most preferred journal for authors, while Landslides garnered the highest total citations, and Geomorphology has the most normalised citations per article. The Chinese Academy of Sciences, China University of Geosciences, and the University of Florence in Italy were the most productive research institutions. The Natural Science Foundation of China (NSFC) emerged as the top funding agency. “Landslide”, “landslide monitoring”, and “early warning systems” were the top three most frequently used keywords. Keyword analysis identified landslide detection and monitoring advancements in remote sensing, analytical methods, and in-situ techniques. Co-occurrence analysis of trending research terms highlighted three distinct thematic clusters: remotely sensed data with machine learning and image analysis for landslide detection, field instrumentation for continuous deformation monitoring, and development of early warning systems for landslide hazard assessment and mitigation.

Curricular Redundancy in Medical Undergraduate Course: Critical Insights from Various Stakeholders in Medical Institutes in India

Background Medical education is continuously evolving to keep pace with the dynamic field of medicine. This study addresses the issue of curricular redundancy in medical education, highlighting the necessity for periodic reviews to eliminate outdated or irrelevant topics. Method A descriptive qualitative approach was employed, involving participants from various medical schools across India. An online questionnaire was used to gather data on redundant topics, suggested replacements, and improvements in the curriculum for both didactic and non-didactic subjects, along with inputs related to assessment and evaluation methods. Content analysis was used for thematic identification and qualitative interpretation. Results Out of 71 respondents from a wide geographic distribution, 30% were female, and 70% were male, with an age range of 17 to 36 years. Participants expressed concerns about redundant theoretical (32%) and practical (51%) topics. Qualitative analysis highlighted the need for integrating different subjects and placing a stronger focus on practical clinical skills. Participants emphasized the importance of a curriculum that keeps pace with advancements in medicine, such as genomics and artificial intelligence, while also addressing mental health. Specifically, they suggested combining anatomy and surgery courses, incorporating more active learning techniques, and utilizing ongoing assessments to gauge progress. Conclusion The study highlights the necessity of eliminating curricular redundancy in medical education. Recommendations include developing a flexible curriculum, emphasizing region-specific content, and implementing a formative assessment system. Additionally, the importance of faculty development and stakeholder involvement in curriculum design is emphasized. Key Message Key insights for updating the medical undergraduate curriculum include the following:

Development of a cardiotoxicity evaluation system using a Heart-on-a-Chip Microdevice with aligned fiber device

Abstract Background and Purpose Cardiovascular cells differentiated from human induced pluripotent stem (iPS) cells, such as cardiomyocytes, are expected to be useful for evaluating cardiac pharmacology and toxicity in humans. In recent years, myocardial damage caused by anticancer drugs has begun to attract attention. Being able to evaluate human cardiotoxicity early in the development of novel drugs like anticancer agents confers advantages in terms of cost and development speed. The aim of this study was to develop a highly sensitive bioassay system that can evaluate the physiological function of the human heart, and to validate the efficacy of the system toward detection of cardiotoxicity of drugs. Methods We differentiated human iPS cells into cardiovascular cells (cardiomyocytes, endothelial cells and mural cells) and applied dynamic culture training to generate vascularized microtissues (VCM) with vascular network structures. By integrating this VCM with a microfluidic chip containing microchannels, we developed a heart-on-a-chip microdevice (HMD) specialized for evaluating cardiac function (Figure 1). Furthermore, through a collaborative study with the Stem Cell & Device Laboratory (SCAD), application of their aligned fiber device improved throughput and stability, allowing development of a SCAD-HMD capable of evaluating longer-term cardiotoxicity. Results SCAD-HMD can quantify physiological parameters of VCM such as ejection volume and force based on displacement of particles within the microchannels. Our results showed that SCAD-HMD not only exhibited pharmacological responses to representative circulatory stimulants, but also reproduced clinical events occurring in actual practice, demonstrating that the anticancer drug trastuzumab, commonly used for breast cancer treatment, causes myocardial disfunction when combined with doxorubicin. Conclusions This study demonstrated that SCAD-HMD can stably evaluate the toxicity of drugs on the human heart. In the future, establishing SCAD-HMD using iPS cells derived from individual patients and assessing responses to specific drugs may enable "individualized cardiotoxicity assessment systems" that can preemptively avoid cardiac complications associated with anticancer drugs and other pharmacological agents.

PEDAGOGICAL PRINCIPLES FORMATION OF INCLUSIVE COMPETENCE OF FUTURE SPECIAL TEACHERS

In the last decade, inclusive education has become the majorintegral partsof our lives. There are many studies on its implementation in the education system of Kazakhstan. However, there are very few studies of the inclusive competence of an inclusive education special teachers(defectologists). In the context of Kazakhstan, itis important to study the development of a model of knowledge and competencies necessary for the training of special teachers in inclusive education.How we can inclusive education be related to the inclusion of scientific research in the quality assessment system of an educational institution. Questions arise how to link profiles in inclusive education with inclusive education. The main aim of the research is to distinguish inclusive education as a new model of education and propose an individual methodology or a set of recommendations on the development of special teachers’ professional competencies.Therefore, we consider themodel and principles for the development of special competencies of future special teachers in the context of inclusive education.As a result of analysis and formation of a model we gave recommendations for analyzing the pedagogical issuesand formation of future special teachers' inclusive competencies.The results of the research can be used in creating educational programs in aninclusive education and consideration of methodology development for inclusive training special teachers for inclusive schools.

Wearable smart glasses for first‐person video analysis to evaluate nursing skills: A pilot study with a mixed method design

AbstractBackgroundComprehensive assessment of skills and performance are necessary to improve the quality of care in nursing education. Various factors pose challenges to accurate assessments, including high student–teacher ratio and observer bias.ObjectivesTo establish an assessment system based on first‐person video of smart glasses and validate its usability and feasibility.MethodsWe used two cameras (smart glasses for first‐person perspective and a web cam) to perform video analysis for skill and performance assessments. Usability tests were conducted using quantitative and qualitative questionnaires for the usefulness and acceptability of the current assessment system. Data from standardised checklists for three clinical nursing skills (intramuscular injection, endotracheal suction, and Levin tube feeding) were used to analyse the inter‐rater reliability in addition to Fleiss' Kappa coefficient analysis. A total of 22 participants (18 students and 4 reviewers) were recruited. They completed the study. Apart from physical discomfort component, the proposed smart glasses‐based first‐person video assessment system achieved good usability scores from both students and reviewers.Results and ConclusionsFleiss' Kappa values for individual items ranged from 0.450 to 0.808, with an overall average value of 0.592. Results of this study validated the potential of smart glasses‐based assessment system where a set of beneficial mechanisms may meaningfully support the formative learning process. However, issues regarding upgrading the device and need for guidelines for roles of raters and students still remain.

The measurement of agricultural disaster vulnerability in China and implications for land-supported agricultural resilience building

Climate change has recently induced an increase in natural disasters worldwide, which has affected agriculture, a sector highly sensitive to climatic alterations. The paper measures agricultural disaster vulnerability of China in the period 2000–2020 by establishing vulnerability assessment system, and examines how agricultural infrastructure correlates with agricultural disaster vulnerability by constructing grey relational analysis model. Research results indicate that China's agricultural disaster vulnerability presents an overall decreasing trend during the research period. This is mainly attributed to the decreased disaster exposure and increased adaptability to disaster. Spatially, the agricultural disaster vulnerability in the northeastern, central and southwestern regions of China is higher than other regions. Agricultural transportation and communication infrastructure are found to be more influential to agricultural disaster vulnerability followed by irrigation and power facilities. Implications are proposed to enhance agricultural resilience to climate disasters in China.

A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

Customer maintenance is of vital importance to the enterprise management. Valuable assessment and efficient prediction for customer ordering behavior can offer better decision-making and reduce business costs significantly. According to existing studies about customer behavior regularity segment and demand prediction most focus on e-commerce and other fields with large amount of data, making them not suitable for small enterprises and data features like sparsity and outliers are not mined when doing regularity quantification. Additionally, more and more complex network structures for demand prediction are proposed, which builds on the assumption that all the samples have predictive value, ignoring the fine-grained analysis of different time series regularity with high cost. To deal with the above issues, a multi-step regularity assessment and joint prediction system for ordering time series is proposed. For extracting features, comprehensive assessment of customer regularity based on entropy weight method with the result of predictability quantification using K-Means clustering algorithm, real entropy, LZW algorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to ‘how high the regularity of customers is’, filling the gap in the field of regularity quantification, but also provides a theoretical basis for demand prediction models selection. Prediction models: Random Forest regression, XGBoost, CNN and LSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of the proposed regularity quantitation method. Moreover, a merged CNN-BiLSTM neural network model is established for predicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms, which performs better on the data set compared to others. Random Forest is still used for prediction of customers with high regularity due to its high training efficiency. Finally, the results of prediction, regularity quantification, and classification are output from the intelligent system, which is capable of providing scientific basis for corporate strategy decision and has highly extendibility in other enterprises and fields for follow-up research.

Incorporating policy mixes to multimodal network capacity considering second-best constraints

In the pursuit of capacity-oriented urban sustainable development (SD), this study aims to enhance the multimodal transport network by making it more accessible, efficient, and environmentally friendly. A sustainable framework combining policy mixes and second-best constraints is developed as a multimodal transportation network capacity (TNC) model with the bi-level programming (BLP) formulation. The upper-level model is to maximise the total origin-destination (OD) demand with considering second-best constraints. The lower-level model is a combined mode split and traffic assignment with elastic demand (CMSTA-ED) model which considers policy mixes. Numerical experiments are conducted to demonstrate the effectiveness of the hybrid PSO–GWO algorithm, the synergy of the method for selecting and mixing policy instruments based on the proposed assessment system, and a methodology to establish a policy mix. The findings of this research offer practical guidance for formulating well-informed and no-redundant policy mixes, contributing to the improvement of sustainable multimodal transportation network performance.

Clinical study on the effects of cluster needling at scalp points with needle retaining combined with the training of the upper-limb intelligent rehabilitation robot on shoulder function in stroke patients during convalescence

To explore the effects of cluster needling at scalp points with needle retaining combined with the training of the upper-limb intelligent rehabilitation robot on shoulder function in stroke patients during convalescence. Ninety stroke patients during convalescence were collected and randomized into scalp point group, robot training group and combined intervention group, with 30 cases each. In the scalp point group, the cluster needling was delivered at scalp points with the needles retained. In the robot training group, the patients were trained with the upper-limb intelligent rehabilitation robot. In the combined intervention group, the patients received both the cluster needling and the training of the upper-limb intelligent rehabilitation robot. In each group, the treatment was given once daily, 5 treatments a week with 2 days rest, for consecutive 4 weeks. Separately, before and after treatment, the score of Fugl-Meyer assessment upper extremity scale (FMA-UE) and the score of activity of daily living (ADL) were evaluated. Using the in-built assessment system of the upper-limb intelligent rehabilitation robot, the range of motion (ROM) of forward flexion, horizontal abduction and horizontal adduction of the affected shoulder joint were evaluated. With surface electromyogram (sEMG), the sEMG value of the deltoid muscle and the pectoralis major on the affected side were detected. Compared with the values before treatment, ADL score and FMA-UA score increased in patients of the three groups (P<0.05), and ROM of forward flexion, horizontal abduction and horizontal adduction of the affected shoulder joint was larger (P<0.05), the sEMG value of the fasciculi pectoralis major anterior of patients in the combined intervention group was reduced (P<0.05), and the sEMG of anterior deltoid tract was elevated in the three groups (P<0.05). When compared with the scalp point group and the robot group, in the combined intervention group, after treatment, ADL score and FMA-UA score were higher (P<0.05), ROM of forward flexion, horizontal abduction and horizontal adduction of the affected shoulder joint elevated (P<0.05) and sEMG value of the fasciculi pectoralis major anterior reduced (P<0.05), while which of the anterior deltoid tract was elevated (P<0.05). The cluster needling at scalp points with needle retaining, combined with the training of the upper-limb intelligent rehabilitation robot, improves the upper limb motor function and the range of motion of the shoulder joint in stroke patients during convalescence.

Adaptive behavior in primary agenesis of the corpus callosum

Background and aimsPrimary agenesis of the corpus callosum (ACC) is a congenital neurological disorder characterized by the absence, either partial or complete, of the corpus callosum in individuals who do not have intellectual disability and are otherwise neurologically asymptomatic. While mild to moderate neurocognitive deficits have been observed in individuals with primary ACC using neuropsychological assessments, the impact of this syndrome on adaptive behavior remains insufficiently understood. MethodsThis study used self- and informant-ratings on the Adaptive Behavior Assessment System, Second Edition (ABAS-II) to evaluate adaptive behavior in 35 adults diagnosed with primary ACC. ResultsWhile adults with primary ACC reported adaptive functioning comparable to an age-adjusted normative sample, family informants rated their adaptive ability below norms in several skill domains, particularly social skills. ConclusionsThis pattern of lower ratings by informants than self-ratings suggests adults with ACC may have poor understanding of their own behavior and its consequences. This study demonstrates that informants observe significant deficiencies in the conceptual, social, and practical aspects of adaptive behavior in persons with primary ACC, and that these deficiencies are not seen as clearly by the persons themselves.

Comprehensive validation of a compact laser speckle contrast imaging system for vascular function assessment: from the laboratory to the clinic.

Proper organ functioning relies on adequate blood circulation; thus, monitoring blood flow is crucial for early disease diagnosis. Laser speckle contrast imaging (LSCI) is a noninvasive technique that is widely used for measuring superficial blood flow. In this study, we developed a portable LSCI system using an 805-nm near-infrared laser and a monochrome CMOS camera with a 10 × macro zoom lens. The system achieved a high-resolution imaging (1280 × 1024 pixels) with a working distance of 10 to 35 cm. The relative flow velocities were visualized via a spatial speckle contrast analysis algorithm with a 5 × 5 sliding window. In vitro experiments demonstrated the system's ability to image flow velocities in a fluid model, and a linear relationship was observed between the actual flow rate and the relative flow rate obtained by the system. The correlation coefficient (R2) exceeded 0.83 for volumetric flow rates of 0 to 0.2 ml/min when channel widths were greater than 1.2 mm, and R2 > 0.94 was obtained for channel widths exceeding 1.6 mm. Comparisons with laser Doppler flowmetry (LDF) revealed a strong positive correlation between the LSCI and LDF results. In vivo experiments captured postocclusive reactive hyperemic responses in rat hind limbs and human palms and feet. The main research contribution is the development of this compact and portable LSCI device, as well as the validation of its reliability and convenience in various scenarios and environments. Future applications of this technology include evaluating blood flow changes during skin injuries, such as abrasions, burns, and diabetic foot ulcers, to aid medical institutions in treatment optimization and to reduce treatment duration.