Real-time Feedback Research Articles

High Sensitivity Triboelectric Based Flexible Self-Powered Tactile Sensor with Bionic Fingerprint Ring Structure.

Flexible self-powered tactile sensors, with applications spanning wearable electronics, human-machine interaction, prosthetics, and soft robotics, offer real-time feedback on tactile interactions in diverse environments. Despite advances in their structural development, challenges persist in sensitivity and robustness, particularly when additional functionalities, such as high transparency and stretchability. In this study, we present a novel approach integrating a bionic fingerprint ring structure with a PVDF-HFP/AgNWs composite fiber electrode membrane, fabricated via 3D printing technology and electrospinning, respectively, yielding a triboelectric nanogenerator (TENG)-based self-powered tactile sensor. The sensor demonstrates high sensitivity (5.84 V/kPa in the 0-10 kPa range) and rapid response time (10 ms), attributed to the microring texture on its surface, and exhibits exceptional robustness, maintaining electrical output integrity even after 24,000 cycles of loading. These findings highlight the potential of the microring structures in addressing critical challenges in flexible sensor technology.

Feasibility and safety report on robotic assistance for cervical pedicle screw fixation: a cadaveric study

This cadaveric study aimed to evaluate the safety and usability of a novel robotic system for posterior cervical pedicle screw fixation. Three human cadaveric specimens and C2-T3 were included. Freshly frozen human cadaver specimens were prepared and subjected to robot-assisted posterior cervical pedicle screw fixation using the robotic system. The accuracy of screw placement, breach rate, and critical structure violations were evaluated. The results were statistically compared with those of previous studies that used different robotic systems for cervical pedicle screw fixation. The robotic system demonstrated a high accuracy rate in screw placement. A significant number of screws were placed within predetermined safe zones. The total entry offset was 1.08 ± 0.83 mm, the target offset was 1.86 ± 0.50 mm, and the angle offset was 2.14 ± 0.77°. Accuracy rates comparable with those of previous studies using different robotic systems were achieved. The system was also feasible, allowing precise navigation and real-time feedback during the procedure. This cadaveric study validated the safety and usability of the novel robotic system for posterior cervical pedicle screw fixation. The system exhibited high precision in screw placement, and the results support the extension of the indications for robot-assisted pedicle screw fixation from the lumbar spine to the cervical spine.

Image analysis and teaching strategy optimization of folk dance training based on the deep neural network

To improve the recognition effect of the folk dance image recognition model and put forward new suggestions for teachers’ teaching strategies, this study introduces a Deep Neural Network (DNN) to optimize the folk dance training image recognition model. Moreover, a corresponding teaching strategy optimization scheme is proposed according to the experimental results. Firstly, the image preprocessing and feature extraction of DNN are optimized. Secondly, classification and target detection models are established to analyze the folk dance training images, and the C-dance dataset is used for experiments. Finally, the results are compared with those of the Naive Bayes classifier, K-nearest neighbor, decision tree classifier, support vector machine, and logistic regression models. The results of this study provide new suggestions for teaching strategies. The research results indicate that the optimized classification model shows a significant improvement in classification accuracy across various aspects such as action complexity, dance types, movement speed, dance styles, body dynamics, and rhythm. The accuracy, precision, recall, and F1 scores have increased by approximately 14.7, 11.8, 13.2, and 17.4%, respectively. In the study of factors such as different training images, changes in perspective, lighting conditions, and noise interference, the optimized model demonstrates a substantial enhancement in recognition accuracy and robustness. These findings suggest that, compared to traditional models, the optimized model performs better in identifying various dances and movements, enhancing the accuracy and stability of classification. Based on the experimental results, strategies for optimizing the real-time feedback and assessment mechanism in folk dance teaching, as well as the design of personalized learning paths, are proposed. Therefore, this study holds the potential to be applied in the field of folk dance, promoting the development and innovation of folk dance education.

Q-switched temperature fiber sensing device based on graded index and D-shaped multimode fiber

In the paper, a Q-switched temperature sensing device is proposed and verified, which consists of a modulator based on a large core multimode fiber and a graded index multimode optical fiber (LMF-GIMF), and a sensing unit formed by sandwiching a D-shaped multimode fiber (MMF) between two single mode fibers (SMF). Utilizing the modulator to transform continuous light into pulsed laser, a stable Er3+-doped Q-switched laser with a repetition frequency of 23.04 kHz and a modulation depth of 7.47 % has been achieved. Under its pumping, the SMF-MMF-SMF sensing structure performed thermal detection with a sensitivity of −81.5 pm/℃ as temperature varies in the range of 25 °C to 65 °C. The temperature sensing device based on Q-switching can be used as an independent pulse laser to realize the switch between the light source and the sensing device. At the same time, the sensing device can provide real-time feedback according to the change of the central wavelength, which allows for immediate adjustments in the operational state, enhancing the flexibility and responsiveness. In addition, the characteristics of the sensor based on Q-switched laser can reduce thermal damage and extends the service life of the sensor. As far as we know, this is the first report on Q-switched temperature sensing based on the LMF-GIMF modulator. The proposed sensing device has the advantages of high signal-to-noise ratio, low operating threshold, minimal thermal damage, and serves as an independent Q-switched laser source, which can be applied to pulse laser measurement with high precision and time resolution in the future.

The application of artificial intelligence in Unmanned Underwater Vehicle communication systems

Artificial Intelligence (AI) has the potential to significantly enhance decision-making techniques in Unmanned Underwater Vehicle (UUV) communication systems by utilizing real-time sensor feedback and environmental data. To address the challenges posed by high data volume and error rates, we propose an advanced methodology known as the Variational Deep Network (VDN). This method integrates Variational Modal Decomposition (VMD) algorithms with the Deep Convolutional Neural Network (DCNN) to create a realizable AI decision-making system. In conjunction with the DCNN and VMD algorithms, our research focuses on accurately identifying received signals under data constraints. Considering the spectral efficiency and robustness, the Filter-Bank Multicarrier (FBMC) technology have been employed in this paper. Our results demonstrate that the VDN significantly reduces the volume of data needed for neural network training and effectively recognizes FBMC signals. These insights confirm the potential of VDN decision-making systems to drive advancements in UUV communication technologies.

The development and impact of an app for a smart drug interaction reminder system.

Improved access to media and medical knowledge has elicited stronger public health awareness. This study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload. This study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction. The results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions. Using information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future.

Automated Code Review Tool

As software development undergoes continual evolution, the assurance of code quality remains paramount in the development lifecycle. This research paper introduces an innovative Automated Code Review Tool engineered to augment and streamline the code review process. The tool capitalizes on advanced static code analysis, machine learning algorithms, and industry best practices to automate the detection of code quality issues, security vulnerabilities, and compliance with coding standards. The Automated Code Review Tool addresses the inherent challenges of manual code reviews, including human error, time constraints, and inconsistencies. Through automation, developers can expedite the identification and resolution of potential issues, thereby enhancing overall code quality and software reliability. Key features of the tool encompass intelligent pattern recognition, real-time feedback, and customizable rule sets, enabling development teams to tailor the tool to their project-specific requirements. Leveraging machine learning algorithms facilitates the tool's continuous learning from past reviews, thereby adapting to evolving coding practices and emerging security threats. This paper presents the architectural framework and design principles underpinning the Automated Code Review Tool, emphasizing its capacity to foster collaboration among development teams, reduce time-to-market, and ultimately contribute to the development of more robust and secure software. A comprehensive evaluation of the tool's efficacy across diverse development environments is conducted, offering insights into the practical implications of embracing automated solutions in real-world contexts. Keywords: Automated Code Review Tool, Code quality, Static code analysis, Machine learning algorithms, Coding standards, Manual code reviews, Human error, Time constraints, etc.

Assessing the reliability of a computer-vision tool for curing postural deformities

Postural deformities, such as rounded shoulders, kyphosis, and lordosis, are a prevalent issue affecting individuals of all ages. These deformities can lead to pain, discomfort, and reduced mobility, significantly impacting one's quality of life. Traditional methods for addressing postural deformities include physiotherapy, exercise, and postural braces, which can be time-consuming, expensive, and difficult to adhere to. To solve these problems computer vision technology has emerged as a promising tool for evaluating and correcting postural deformities. Computer vision algorithms can analyze images and videos of an individual's posture, identifying postural abnormalities and providing real-time feedback. The fusion of computer vision technology with AI-driven analytics not only revolutionizes postural correction methodologies but also paves the way for comprehensive, accessible, and efficient solutions to address the spectrum of postural deformities. As this technology continues to evolve, it holds immense potential in reshaping the landscape of posture-related healthcare, offering tailored, accessible, and effective interventions to enhance individuals' postural well-being. Keywords: Postural deformities, AI-driven analytics, Computer vision technology, Dashboard

A Method to Redesign and Simplify Schedules of Assessment and Quantify the Impacts. Applications to Merck Protocols.

The growing complexity of biopharmaceutical sponsored trials has adverse impacts on increased burdens on participants, clinical sites, and sponsors, including greater difficulty recruiting and retaining participants, difficulty engaging sites to participate in trials, excessive cost of trials, and increased cycle times. The schedule of assessments (SoAs) is the origin of and blueprint for complexity that is often generated by copying and pasting from previous SoAs. We developed an approach, termed Lean Design, for redesigning the assessments in SoAs that generate data, the 'Data SoA.' It starts with a simple "ground zero" SoA. Any addition is challenged using several principles of trial design. We employed a system, the Faro Trial Designer Tool, to quantify the impacts of changes in an SoA to provide real-time feedback to the team and sponsor. We applied the approach in workshops with teams for six clinical trials in various stages of design and implementation. The approach resulted in recommendation for substantial potential savings in participant and site staff time, costs, and complexity of the trials. Application of this approach to very early stages of protocol design has the potential to reduce the complexity of biopharmaceutical sponsored trials and its consequences.

Implementation of a Smart Teaching and Assessment System for High-Quality Cardiopulmonary Resuscitation

The purpose of this study is to develop a smart training and assessment system called SmartCPR, for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during CPR meet the standard of high-quality CPR by simply using a device such as a smart phone. Through the system, trainees are able to obtain real-time feedback on the quality of compressions so that they can adjust the cycle, depth, frequency, and posture of compressions to meet the standard of high-quality CPR. In addition, the SmartCPR system is convenient for CPR trainers. Trainers can instantly and accurately assess whether the trainee’s compressions meet the standard of high-quality CPR, which reduces the risk of manual assessment errors and also reduces the trainer’s teaching pressures. Therefore, the SmartCPR system developed in this study can be an important tool for CPR teaching and training for physicians, which can provide training and guidance for high-quality CPR maneuvers and enable trainees to become more proficient in CPR and self-training.

Tailoring the wrap: intraoperative functional lumen imaging probe (FLIP) during hiatal hernia repair

Abstract Introduction The introduction of the functional lumen imaging probe (FLIP) has provided objective, real-time feedback on the geometric variations with each component of a hiatal hernia repair (HHR). The utility of this technology in altering intraoperative decision-making has been scarcely reported. Herein, we report a single-center series of intraoperative FLIP during HHR. Methods A retrospective review of electronic medical records between 2020 and 2022 was conducted and all patients undergoing non-recurrent HHR with FLIP were queried. Patient and hernia characteristics, intraoperative FLIP values and changes in decision-making, as well as early post-operative outcomes were reported. Both diameter and distensibility index (DI) were measured at 40 ml and 50 ml balloon inflation after hiatal dissection, after hiatal closure, and after fundoplication when indicated. Results Thirty-three patients met inclusion criteria. Mean age was 62 ± 14 years and mean BMI was 28 ± 6 kg/m2. The majority (53%) were type I hiatal hernias. The largest drop in DI occurred after hiatal closure, with minimal change seen after fundoplication (mean DI of 4.3 ± 2. after completion of HH dissection, vs 2.7 ± 1.2 after hiatal closure and 2.3 ± 1 after fundoplication when performed). In 13 (39%) of cases, FLIP values directly impacted intraoperative decision-making. Fundoplication was deferred in 4/13 (31%) patients, the wrap was loosened in 2/13 (15%); the type of fundoplication was altered to achieve adequate anti-reflux values in 2/13 (15%) patients, and in 1/13 (3%) the wrap was tightened. Conclusion FLIP measurements can be used intraoperatively to guide decision-making and alter management plan based on objective values. Long-term outcomes and further prospective studies are required to better delineate the value of this technology.

Smart Wheel Chair using IOT

The microcontroller based wheelchair using android is a device designed to assist individuals with mobility impairments. The system utilizes an Android application installed on a smartphone to control the movement of the wheelchair. The smartphone communicates with a microcontroller via Bluetooth, which is responsible for processing the received commands and controlling the movement of the wheelchair's motors. The system includes obstacle detection sensors that detect any obstacles in the wheelchair's path and prevent collisions. The microcontroller based wheelchair using android is a device that combines modern technologies to help individuals with mobility impairments. It utilizes an Android application and a microcontroller to control the movement of the wheelchair. Here are some more details about this innovative device: Android Application: The Android application is installed on a smartphone and acts as a remote control for the wheelchair. The user can use the application to move the wheelchair forward, backward, left, and right. Additionally, the application provides the user with real-time feedback on the wheelchair's battery level, speed, and direction. Microcontroller: The microcontroller is responsible for processing the commands received from the smartphone and controlling the movement of the wheelchair's motors. The microcontroller is programmed to interface with the wheelchair's motors and control their speed and direction. Improving the quality of life for the elderly and disabled people and giving them the proper care at the right time is one the most important roles that are to be performed by us being a responsible member of the society. It's not easy for the disabled and elderly people to maneuver a mechanical wheelchair, which many of them normally use for locomotion. Hence there is a need for designing a wheelchair that is intelligent and provides easy maneuverability. In this context, an attempt has been made to propose a thought controlled wheelchair, which uses the captured signals from the brain and eyes and processes it to control the wheelchair. Electroencephalography (EEG) technique deploys an electrode cap that is placed on the user's scalp for the acquisition of the EEG signals which are captured and translated into movement commands by the arduino microcontroller which in turn move the wheelchair

Decoding Posture: Distinguishing Between Good and Bad Body Alignment

On the grounds of artificial intelligence sensors, the research focuses on achieving precise detection of intelligent posture to minimize the risk of injuries resulting from incorrect body positioning and to mitigate the negative impact on both performance and physical well-being. Utilizing an attitude analysis and recognition system that relies on the nertial measurement unit enables not only the measurement of human body motion information but also the acquisition of data pertaining to motion characteristics and body movement states through the examination of posture data. A proposition is presented for intelligent posture training that is centered on accuracy and real-time feedback. Furthermore, it is elucidated that the accuracy of posture recognition through machine learning is significantly influenced by variations in user BMI. This paper will introduce the most recent advancements in posture recognition techniques, providing an overview of the various methodologies and algorithms that have emerged in recent years. Additionally, it delves into enhanced approaches such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The study thoroughly dissects and consolidates the general procedures and datasets involved in posture recognition, comparing several enhanced CNN methodologies alongside three principal recognition techniques. Moreover, it delves into the utilization of advanced neural networks in posture recognition, including but not limited to transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks. Researchers have observed that CNN has made significant strides in the realm of posture recognition and has garnered favor, yet emphasize the necessity for further exploration into areas such as feature extraction and information fusion.

A geriatric game to strengthen older adult healthcare delivery and outcomes.

Serious games enhance learner engagement and knowledge, yet few medical education games target older adults' healthcare. Addressing this gap, we developed Geri-POP (Geriatrics Population Health), focusing on the Age-Friendly Health System (AFHS) framework and Geriatric 4Ms. Geri-POP, a healthcare game is aimed at educating health profession learners about the AFHS framework. Geri-POP employs plan-do-study-act cycles of rapid improvement to apply AFHS principles and explore evidence-based geriatric practices within the game environment while garnering points for insight, trust and outcomes. Faculty and medical students were surveyed for feedback on an alpha version of Geri-POP. Players manage patient panels across three age groups (65-74, 75-84, and 85 years and older) while engaging in plan-do-study-act (PDSA) cycles, applying Geriatric 4Ms (What Matters, Medications, Mentation, and Mobility), and refining strategies based on resource utilization, health outcomes, and real-time feedback. Alpha testing of the game received mixed perceptions on graphics, with faculty endorsing the game for training and integration into the curriculum, while students prioritize academic commitments. Suggestions include enhancing graphics and refining dialogue for a more professional tone. Geri-POP demonstrates the potential of gamifying older adult population health and quality improvement around AFHS. Feedback on a prototype game revealed different attitudes between faculty and students, thus emphasizing the importance of game development as an iterative process that accounts for educator and learner-centric needs. A future consideration is whether the game informs user's clinical practices and changes healthcare outcomes for older adults.

Analysis and Implementation of Web Advertisement Optimization Using UCB Reinforcement Learning

Web advertisements are vital in digital marketing for connecting businesses with customers; yet optimizing their placement faces challenges from evolving user preferences. This project explores leveraging the Upper Confidence Bound (UCB) algorithm, a reinforcement learning technique, to automate ad placement strategies based on real-time feedback. Through analysis and implementation, this study investigates UCB's potential for web advertisement optimization. It examines existing methods, delves into reinforcement learning theory, and develops a simulation environment reflecting real-world dynamics. Experimental results comparing the UCB approach against baselines demonstrate its efficacy in improving ad placement and enhancing online advertising campaigns.

Advanced Dietitian Using Machine Learning

The "Advanced Artificial Intelligence Dietitian" (AI-Dietitian) project explores the potential of AI to revolutionize personalized nutrition. Traditional dietary approaches often struggle with individual complexities and lack continuous guidance. We propose an AI-powered system that transcends these limitations, offering: · Comprehensive Data Integration: The AI-Dietitian analyzes diverse data sets, including medical records, genetic information, activity trackers, and food intake sensors, building a holistic understanding of individual needs and preferences. · Adaptive & Personalized Plans: Leveraging advanced machine learning algorithms, the AI-Dietitian generates dynamic, personalized meal plans that adapt to real-time data and user feedback, ensuring continued effectiveness and adherence. · Continuous Coaching & Support: The AI-Dietitian acts as a virtual coach, providing real-time feedback, nutritional education, and motivational support through natural language interaction and personalized content. · Predictive & Preventative Healthcare: The AI-Dietitian leverages its predictive capabilities to identify potential health risks and recommend preventative dietary interventions, promoting long-term health and well-being. This project addresses the limitations of traditional dietetics and explores the ethical considerations of AI in healthcare. We believe the AI-Dietitian has the potential to democratize personalized nutrition, promoting healthier communities and reducing healthcare costs. Keywords: Artificial Intelligence, Machine Learning, Personalized Nutrition, Health Coaching, Predictive Healthcare, Ethical AI

Motion Capture Technology in Sports Scenarios: A Survey.

Motion capture technology plays a crucial role in optimizing athletes' skills, techniques, and strategies by providing detailed feedback on motion data. This article presents a comprehensive survey aimed at guiding researchers in selecting the most suitable motion capture technology for sports science investigations. By comparing and analyzing the characters and applications of different motion capture technologies in sports scenarios, it is observed that cinematography motion capture technology remains the gold standard in biomechanical analysis and continues to dominate sports research applications. Wearable sensor-based motion capture technology has gained significant traction in specialized areas such as winter sports, owing to its reliable system performance. Computer vision-based motion capture technology has made significant advancements in recognition accuracy and system reliability, enabling its application in various sports scenarios, from single-person technique analysis to multi-person tactical analysis. Moreover, the emerging field of multimodal motion capture technology, which harmonizes data from various sources with the integration of artificial intelligence, has proven to be a robust research method for complex scenarios. A comprehensive review of the literature from the past 10 years underscores the increasing significance of motion capture technology in sports, with a notable shift from laboratory research to practical training applications on sports fields. Future developments in this field should prioritize research and technological advancements that cater to practical sports scenarios, addressing challenges such as occlusion, outdoor capture, and real-time feedback.

Eco Carbon Navigator for Tracking Carbon Emission in Computing Systems

The goal of the Eco Carbon Navigator project is to provide an effective and user-friendly application that will help people track and lessen their carbon footprint. It is now essential to monitor and reduce one's own carbon emissions in light of the growing environmental concerns in order to promote a sustainable future. This creative initiative gives people the ability to make knowledgeable judgments about their lifestyle choices by utilizing state-of-the-art technologies. The Eco Carbon Navigator offers real-time feedback on a range of factors that contribute to carbon emissions, such as energy use, food choices, and transportation, through user-friendly interfaces and thorough data analysis. Users may easily identify areas for improvement and implement real adjustments to reduce their environmental impact by using individualized insights and recommendations.In addition, the Eco Carbon Navigator incorporates social networking functions to promote a feeling of community and group efforts. The project's influence can be increased beyond individual efforts by allowing users to collaborate on sustainability initiatives, interact with peers, and share accomplishments. Tracker project is to transform how people view andplanthealthmonitoringbyexpandingitscoveragetoincludeavarietyofvegetables andautomatingdiseasediagnosis. KEYWORDS: Carbon footprint, data analysis, emission reduction

Integrating AI with emotional and social learning in primary education: Developing a holistic adaptive learning ecosystem

This paper highlights the significance, potential benefits, challenges, and proposed solutions associated with integrating AI-driven tools and platforms into SEL initiatives. The importance of integrating AI with SEL in primary education lies in its ability to foster the holistic development of students. By equipping students with the tools to navigate academic challenges, interpersonal relationships, and emotional regulation, schools can create dynamic learning environments that prioritize the whole child. The potential benefits of integrating AI with SEL are manifold. AI-powered adaptive learning platforms can personalize instruction, provide targeted support, and promote the development of emotional intelligence and social skills among students. Additionally, AI-driven tools and platforms can facilitate collaborative learning experiences, promote active engagement, and provide real-time feedback to students and educators. However, the integration of AI with SEL also presents various challenges that must be addressed. Ethical considerations, such as data privacy, algorithmic bias, and the digital divide, require careful attention to ensure equitable access and outcomes for all students. Additionally, educators may lack the necessary knowledge and skills to effectively utilize AI tools and platforms for SEL purposes, highlighting the need for training and professional development programs. To address these challenges, collaborative efforts among educators, policymakers, technologists, and researchers are essential. By working together, stakeholders can develop evidence-based practices and solutions that align with the goals and values of primary education. Training and professional development programs for educators, robust policies and safeguards for the ethical use of AI technologies, and equitable access to technology for all students are critical components of successful integration. Keywords: AI, Learning, Emotions, Social.

FABRICATION AND ANALYSIS OF AUTOMATIC LINEAR DISTANCE ADJUSTER

The Automatic linear distance adjustment system tailored for robotics arms, capitalizing on ultrasonic sensor technology. The system's design, implementation, and evaluation are comprehensively explored, highlighting its ability to facilitate precise and autonomous adjustments of linear positions within the robotic arm's operational envelope. Real-time feedback mechanisms are integrated, enabling dynamic regulation of the distance between the end-effector and target objects or surfaces. This ensures optimal positioning and operational efficiency during various tasks such as pick-and-place operations, assembly processes, and object manipulation. By reducing reliance on manual adjustments, the system enhances automation and augments robotics arms' capabilities across diverse industrial and research applications. The seamless integration of the proposed system empowers robotics arms with heightened flexibility, accuracy, and adaptability, thereby fostering advancements in robotic automation and control. This innovation holds promise for streamlining manufacturing processes, enhancing productivity, and facilitating intricate tasks that demand precision and efficiency in robotic operations. Through rigorous evaluation and validation, the system demonstrates its potential to revolutionize robotic arm functionalities and pave the way for future developments in automation technologies Keywords: Arduino Uno , Ultrasonic sensor,Stepper Motor, Linear guide rail .