Interactive System Research Articles

System interactive reader using eye-tracker technology in ebook reader

Interest in using ebooks by the academic community is very high. Still, there is a problem when readers are reading through screens, tend to read fast, only scan the necessary parts, and don't focus on paying attention to the content they read, so this reduces the quality of reading because readers don't study the overall meaning of the sentence. Hence, this research aims to build an interactive reader system by integrating eye tracker technology with a webcam which is expected to solve the problem of decreasing the quality of reading through the screen by helping readers stay focused on their reading and providing an interactive system that makes it easier for readers to control the computer while reading. This research adopts the waterfall method and is divided into six stages. The system is designed using class diagrams, use case diagrams, and activity diagrams. Also, the system is built using the Python language with the Django framework. Then, the interactive reader system was tested using black box testing and usability testing methods. Based on the test results, it is shown that the interactive reader system that was built can help improve the quality and concentration when reading activities take place.

Safety and efficacy of inebilizumab for the treatment of neuromyelitis optica spectrum disorder: end-of-study results from the open-label period of the N-MOmentum trial

Inebilizumab, an anti-CD19 B-cell-depleting antibody, demonstrated safety and efficacy in neuromyelitis optica spectrum disorder in the randomised controlled period of the N-MOmentum trial. Here, end-of-study data, including the randomised controlled period and open-label extension period, are reported. In the double-blind, randomised, placebo-controlled, phase 2/3 N-MOmentum trial, adults aged 18 years and older with an neuromyelitis optica spectrum disorder diagnosis, Expanded Disability Status Scale score of 8·0 or less, and history of either at least one acute inflammatory attack requiring rescue therapy in the past year or two attacks requiring rescue therapy in the past 2 years, were recruited from 81 outpatient specialty clinics or hospitals in 24 countries. Eligible participants were randomly assigned (3:1), using a central interactive voice system or interactive web response system, and a permuted block randomisation scheme (block size of 4), to receive intravenous inebilizumab (300 mg) or identical placebo on days 1 and 15 of the randomised period, which lasted up to 197 days. Participants and all study staff were masked to treatment assignment. The primary endpoint of the randomised period of the trial was time to onset of adjudicated neuromyelitis optica spectrum disorder attack on or before day 197. Participants in the randomised controlled period who had an adjudicated attack, completed 197 days in the study, or were in the randomised controlled period when enrolment stopped, could voluntarily enter the open-label period. In the open-label period, participants either initiated inebilizumab if assigned placebo (receiving 300 mg on days 1 and 15 of the open-label period) or continued treatment if assigned inebilizumab (receiving 300 mg on day 1 and placebo on day 15, to maintain B-cell depletion and masking of the randomised controlled period). All participants subsequently received inebilizumab 300 mg every 6 months for a minimum of 2 years. The end-of-study analysis endpoints were time to adjudicated attack and annualised attack rate (assessed in all participants who received inebilizumab at any point during the randomised controlled period or open-label period [any inebilizumab population] and the aquaporin-4 [AQP4]-IgG seropositive subgroup [any inebilizumab-AQP4-IgG seropositive population]) and safety outcomes (in all participants who were exposed to inebilizumab, analysed as-treated). This study is registered with ClinicalTrials.gov, NCT02200770, and is now complete. Between Jan 6, 2015, and Sept 24, 2018, 467 individuals were screened, 231 were randomly assigned, and 230 received at least one dose of inebilizumab (n=174) or placebo (n=56). Between May 19, 2015, and Nov 8, 2018, 165 (95%) of 174 participants in the inebilizumab group and 51 (91%) of 56 in the placebo group entered the open-label period (mean age 42·9 years [SD 12·4], 197 [91%] of 216 were female, 19 [9%] were male, 115 [53%] were White, 45 [21%] were Asian, 19 [9%] were American Indian or Alaskan Native, and 19 [9%] were Black or African American). As of data cutoff for this end of study analysis (Dec 18, 2020; median exposure 1178 days [IQR 856-1538], total exposure of 730 person-years) 225 participants formed the any inebilizumab population, and 208 (92%) participants were AQP4-IgG seropositive. Overall, 63 adjudicated neuromyelitis optica spectrum disorder attacks occurred in 47 (21%) of 225 treated participants (60 attacks occurred in 44 [21%] of 208 in the AQP4-IgG seropositive subgroup); 40 (63%) of 63 attacks occurred in 34 (15%) of 225 treated participants during the first year of treatment. Of individuals who had an adjudicated attack while receiving inebilizumab, 36 (77%) of 47 were subsequently attack-free at the end of 4 years. Annualised attack rates decreased year-on-year, with end-of-study adjusted annualised attack rates being similar in the any inebilizumab-AQP4-IgG seropositive subgroup (0·097 [95% CI 0·070-0·14]) and any inebilizumab populations (0·092 [0·067-0·13]). Overall, 208 (92%) of 225 participants who received any inebilizumab had at least one treatment-emergent adverse event, the most frequent of which were urinary tract infection (59 [26%]), nasopharyngitis (47 [21%]), and arthralgia (39 [17%]). Infection rates did not increase over 4 years. Three (1%) of 225 participants in the any inebilizumab population died during the open-label period (one each due to a CNS event of unknown cause and pneumonia, respiratory insufficiency resulting from an neuromyelitis optica spectrum disorder attack and viral pneumonia related to COVID-19), all of which were deemed to be unrelated to treatment. Data from the end-of-study analysis of the N-MOmentum trial showed continued and sustained clinical benefits of long-term inebilizumab treatment in individuals with neuromyelitis optica spectrum disorder, which supports the role of inebilizumab as a CD19+ B-cell-depleting therapy in neuromyelitis optica spectrum disorder. MedImmune and Viela Bio/Horizon Therapeutics, now part of Amgen.

Design of Interactive Online Teaching System for Big Data Computer Information System Based on Mobile Sensor Network

The combination of the Internet and computers has resulted in many new products, including smart homes. The emergence of smart homes can satisfy people's detection of the internal environment of the home, not only to ensure the continuity and safety of indoor monitoring, but also to provide convenience for people to manage their family life. This paper analyzes the current status of smart home research at home and abroad, and people's needs for homes, and builds and designs smart home systems based on Ethernet and ZigBee protocol wireless sensor networks connected to TCP/IP protocol. In this article, the ARM chip is used in the Android system to ensure that the system and functions are matched, and information is transmitted through the serial port of the development board. After receiving the corresponding sending signal, the wireless module will use ZigBee technology to send the corresponding signal to achieve the goal of controlling home appliances. We divides the control system into two parts, the Android Voice Control Plateform and the data collection terminal. The former is based on the Android operating system and is designed for voice recognition control; the latter uses a ZigBee microcontroller to achieve control and feedback. This article uses the built-in voice control system of the smart home to subtly replace the conventional control method of the smart home. Moreover, the dialogue with users is very high, easy to use, low cost, and very practical. In addition, the research methods used in this article can also be applied to other related fields.

Real-Time Object Detection and Recognition with Computer Vision

Abstract: Object detection, a fundamental aspect of computer vision, is essential for identifying and localizing objects within images or video frames, leveraging advancements in deep learning, particularly convolutional neural networks (CNNs), to enhance precision and speed. Its applications span diverse domains, from autonomous vehicles and surveillance systems to augmented reality and human-computer interaction. Our project focuses on engineering a real-time object detection system, integrating deep learning and computer vision methodologies. Anchored on the robust Single Shot Multibox Detector (SSD) architecture and reinforced by the efficiency and accuracy of the MobileNetV3 backbone, our system utilizes a pre-trained SSD MobileNetV3 model and comprehensive annotations from the COCO dataset to adeptly detect and recognize a wide array of objects within live video streams or archived footage. It seamlessly processes video frames from various sources, annotating detected objects in real-time to provide instant visual feedback. Offering customizable confidence thresholds and support for multiple video sources, our project showcases the transformative potential of deep learning and computer vision, advancing realtime object detection across domains like surveillance and interactive systems. By pushing the boundaries of object detection technology, our project aims to enhance safety, efficiency, and user experiences in various applications, promising to redefine the landscape of computer vision with innovation and advancement.

Enhancing Interactive IVR Using NLP

Abstract: This paper suggests a fresh approach to IVR design by incorporating natural language processing (NLP) techniques, enhancing user experiences. Traditional IVRs often frustrate users due to their rigid menu structures. By integrating NLP, we aim to create dynamic and interactive IVR systems, leading to increased user satisfaction, streamlined interactions, and improved business performance. Our self-deployment and user testing demonstrate the system's effectiveness in providing personalized and responsive support, transforming traditional IVRs into engaging communication channels.

YOLOv5 driven smart glasses for visually impaired

This project introduces an innovative AI-based interactive shopping assistance system tailored specifically for visually impaired individuals, aiming to enhance their shopping experience and promote inclusivity in retail environments. Leveraging advanced artificial intelligence algorithms, the system provides personalized assistance, navigation support, and seamless interaction for users facing visual challenges. Through intuitive interfaces and adaptive technologies, disabled individuals are empowered to navigate retail spaces with greater independence and confidence. The system utilizes a camera module and YOLO-based deep learning algorithms on a Raspberry Pi 4 for real-time object classification, converting processed information into accessible audio output. In addition to these features, the system incorporates a language customization capability using OCR and gTTS technology, enabling conversion of audio output into multiple languages based on user preference. By prioritizing the needs of visually impaired individuals, this groundbreaking system aims to enhance accessibility and promote an inclusive shopping experience for all.

Interactive Surgical Training in Neuroendoscopy: Real-Time Anatomical Feature Localization using Natural Language Expressions.

This study addresses challenges in surgical education, particularly in neuroendoscopy, where the demand for optimized workflow conflicts with the need for trainees' active participation in surgeries. To overcome these challenges, we propose a framework that accurately identifies anatomical structures within images guided by language descriptions, facilitating authentic and interactive learning experiences in neuroendoscopy. Utilizing the encoder-decoder architecture of a conventional transformer, our framework processes multimodal inputs (images and language descriptions) to identify and localize features in neuroendoscopic images. We curate a dataset from recorded endoscopic third ventriculostomy (ETV) procedures for training and evaluation. Utilizing evaluation metrics, including "R@n," "IoU= θ," "mIoU," and top-1 accuracy, we systematically benchmark our framework against state-of-the-art methodologies. The framework demonstrates excellent generalization, surpassing the compared methods with 93.67 % accuracy and 76.08 % mIoU on unseen data. It also exhibits better computational speed compared with other methods. Qualitative results affirms the framework's effectiveness in precise localization of referred anatomical features within neuroendoscopic images. The framework's adeptness at localizing anatomical features using language descriptions positions it as a valuable tool for integration into future interactive clinical learning systems, enhancing surgical training in neuroendoscopy. The exemplary performance reinforces the framework's potential in enhancing surgical education, leading to improved skills and outcomes for trainees in neuroendoscopy.

Supporting Implementation Through Online Learning Communities: Lessons Learned From a National Training and Technical Assistance Network.

Recent implementation science frameworks highlight the role of training and technical assistance (TTA) in building workforce capacity to implement evidence-based practices (EBPs). However, evaluation of TTA is limited. We describe three case examples that highlight TTA by three regional centers in the national Mental Health Technology Transfer Center (MHTTC) network. Each MHTTC formed Learning Communities (LCs) to facilitate connections among behavioral health professionals with the goals of sharing implementation strategies, discussing best-practices, and developing problem solving techniques. Data on outcomes were collected through a combination of self-report surveys and qualitative interviews. LC participants reported strong connectedness, gains in knowledge and skills, improvements in implementation capacity, and intentions to advocate for organizational and systems-level change. Furthermore, across the case examples, we identified LC characteristics that are associated with participant perceptions of outcomes, including tailoring LC content to workforce needs, providing culturally relevant information, engaging leaders, forming connections among participants and trainers, and challenging participants' current workplace practices. These findings are interpreted through the lens of the Interactive Systems Framework, which focuses on how TTA, such as LCs, can facilitate connections between the theoretical and empirical foundations of interventions and the practices of implementing interventions in real-world settings to advance workforce capacity.

Adapting To The New Normal: Assessing Levers Of Control Implementation In Indonesian Organizations Post-COVID

This systematic literature review investigates the application of the Four Levers of Control (4LOC) framework in Indonesian organizations post-COVID-19. The research method involved a systematic search of electronic databases and analysis of relevant literature. Major findings indicate that effective implementation of the 4LOC framework contributes to organizational resilience, performance improvement, and competitive advantage across various sectors. Cultural factors, leadership styles, and strategic alignment emerge as critical determinants of success. The study underscores the versatility and effectiveness of the 4LOC framework in navigating challenges posed by dynamic environments. Conclusions highlight the importance of adaptive strategies, cultural sensitivity, and integrated control systems in driving sustainable growth and organizational success in Indonesia. Results indicate that LoC implementation has positively impacted organizational performance across sectors such as hospitality, start-ups, state-owned enterprises, government organizations, manufacturing, and the service industry. Notably, effective utilization of belief systems, boundary systems, diagnostic control systems, and interactive control systems has been instrumental in fostering innovation, resilience, and strategic alignment. These findings underscore the importance of robust management control systems in achieving organizational objectives and navigating uncertainties. The review concludes by recommending further research to explore longitudinal effects, contextual influences, and the role of emerging technologies in enhancing LoC implementation.

Acetonitrile real gas phase behavior from quasi-ideal gas to nanodroplets: A microscopical view.

We have applied a recently developed general purpose acetonitrile force field based on first-principles calculations to simulate acetonitrile in the gas phase at different temperatures and densities. These conditions range from nearly ideal to real gas phase behavior and condensation. The molecular dynamics simulation results agree fairly well with the experimental studies available in the literature on the gas samples. The structural analysis of aggregates and their associated interaction energies is examined and related to the early model proposed on molecular association and equilibrium determining the non-ideal behavior. The formation of dimers is mainly responsible for the non-ideal behavior of the gas at very low density, confirming suggested models based on previous experimental studies. However, when the density of the sample rises, the level of aggregation increases and the simple concept of dimerization does not hold anymore. The real behavior adopted by the gas is related to the distribution of molecular structures observed. The macroscopical view of a real gas as a generic interparticle interaction system without a defined form may then be rationalized on the basis of a defined molecular association originated by a distribution of aggregates at the low density regime. The sample with the highest density (∼1.4 × 103 molm-3) at the lowest temperature exhibits a massive aggregation where most of the acetonitrile (ACN) molecules in the simulation box form a big cluster. Its radial distribution function is similar to that of the liquid ACN. This strongly inhomogeneous distribution in the box can be considered a condensation in the gas phase under specific density-T conditions. This formation opens the door to the potential tuning of its solvent properties as a function of its size in these nanodroplets that in turn are controlled by the density-T conditions.

Interval-valued spherical fuzzy quality function deployment methodology: Metaverse collaborative system design application

The metaverse collaborative system (MCS) aims to assist users in working together, sharing resources, and participating in joint activities in the metaverse environment. As an interactive system, identifying essential design requirements (DRs) based on user requirements (URs) during the conceptual design phase of MCS is challenging as it directly affects the cost of subsequent optimization design and user experimentation, as well as user experience. Therefore, we propose a quality function deployment (QFD) method based on multi-criteria decision-making (MCDM) in an interval-valued spherical fuzzy (IVSF) environment to establish a mapping relationship between URs and DRs and identify human-centered DRs to guide the conceptual design process of MCS. Firstly, we summarized the URs for MCS design and calculated their weights using IVSF based analytical hierarchy process (IVSF-AHP). Afterward, we determined DRs based on URs and established the UR-DR relationship in the house of quality of QFD. Next, we integrate the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in the IVSF environment into QFD to determine the priority of DRs. The results indicate that system functional design, scene layout design, compatibility design, and multi-user collaborative optimization design are important DRs for designing MCS and should be carefully considered. We validated the applicability of this method through an urban planning MCS design example and provided a conceptual design case including functional blueprints. Sensitivity analysis and comparative analysis demonstrate the flexibility and reliability of the proposed method.

Response prediction and probabilistic analysis of the vehicle-ballasted track system considering track irregularity based on long-short term memory neural network

It is necessary to efficiently and accurately predict the dynamic behavior of the train-track system subjected to massive random track geometric excitations for system evaluation and maintenance. This paper proposed an engineering practical approach for the train-track system behavior prediction by integrating the deep learning surrogate model-based long-short term memory (LSTM) and physical model of the train-ballasted track interaction (TBTI) system, and then the generalized probability density evolution method (GPDEM) is introduced to achieve the probabilistic assessment of the TBTI system under limited track irregularities, where the track irregularity probability model (TIPM) coupled with generalized-F discrepancy points sets is employed to compress the track irregularity sets to reduce the number of system responses (as training target) but without loss of the responses completeness, therefore, it can promote the efficiency and accuracy of training model. The prediction performance of the surrogate model is illustrated by comparing the time-frequency domain information, mean and standard deviation of the responses obtained by the physical model and surrogate model. The results indicated the proposed method can accurately predict the system response, especially the low and medium frequency response below 50 Hz, and improve the efficiency of stochastic analysis for the TBTI system by approximately 20 times. Furthermore, the applicability of the proposed method is further examined by investigating the influence of probability levels of track irregularity and train speeds on train performance. Its extended model is also implemented to predict the response of systems induced by any measured track irregularity in real-time, which promotes the generalization capability of the proposed surrogate model.

Advancing interactive systems with liquid crystal network-based adaptive electronics.

Achieving adaptive behavior in artificial systems, analogous to living organisms, has been a long-standing goal in electronics and materials science. Efforts to integrate adaptive capabilities into synthetic electronics traditionally involved a typical architecture comprising of sensors, an external controller, and actuators constructed from multiple materials. However, challenges arise when attempting to unite these three components into a single entity capable of independently coping with dynamic environments. Here, we unveil an adaptive electronic unit based on a liquid crystal polymer that seamlessly incorporates sensing, signal processing, and actuating functionalities. The polymer forms a film that undergoes anisotropic deformations when exposed to a minor heat pulse generated by human touch. We integrate this property into an electric circuit to facilitate switching. We showcase the concept by creating an interactive system that features distributed information processing including feedback loops and enabling cascading signal transmission across multiple adaptive units. This system responds progressively, in a multi-layered cascade to a dynamic change in its environment. The incorporation of adaptive capabilities into a single piece of responsive material holds immense potential for expediting progress in next-generation flexible electronics, soft robotics, and swarm intelligence.

Lymph Node-on-Chip Technology: Cutting-Edge Advances in Immune Microenvironment Simulation.

Organ-on-a-chip technology is attracting growing interest across various domains as a crucial platform for drug screening and testing and is set to play a significant role in precision medicine research. Lymph nodes, being intricately structured organs essential for the body's adaptive immune responses to antigens and foreign particles, are pivotal in assessing the immunotoxicity of novel pharmaceuticals. Significant progress has been made in research on the structure and function of the lymphatic system. However, there is still an urgent need to develop prospective tools and techniques to delve deeper into its role in various diseases' pathological and physiological processes and to develop corresponding immunotherapeutic therapies. Organ chips can accurately reproduce the specific functional areas in lymph nodes to better simulate the complex microstructure of lymph nodes and the interactions between different immune cells, which is convenient for studying specific biological processes. This paper reviews existing lymph node chips and their design approaches. It discusses the applications of the above systems in modeling immune cell motility, cell-cell interactions, vaccine responses, drug testing, and cancer research. Finally, we summarize the challenges that current research faces in terms of structure, cell source, and extracellular matrix simulation of lymph nodes, and we provide an outlook on the future direction of integrated immune system chips.

V4RIN: visual analysis of regional industry network with domain knowledge.

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.

DNA-empowered synthetic cells as minimalistic life forms.

Cells, the fundamental units of life, orchestrate intricate functions - motility, adaptation, replication, communication, and self-organization within tissues. Originating from spatiotemporally organized structures and machinery, coupled with information processing in signalling networks, cells embody the 'sensor-processor-actuator' paradigm. Can we glean insights from these processes to construct primitive artificial systems with life-like properties? Using de novo design approaches, what can we uncover about the evolutionary path of life? This Review discusses the strides made in crafting synthetic cells, utilizing the powerful toolbox of structural and dynamic DNA nanoscience. We describe how DNA can serve as a versatile tool for engineering entire synthetic cells or subcellular entities, and how DNA enables complex behaviour, including motility and information processing for adaptive and interactive processes. We chart future directions for DNA-empowered synthetic cells, envisioning interactive systems wherein synthetic cells communicate within communities and with living cells.

Theoretical and Experimental Investigations of Identifying Bridge Damage Using Instantaneous Amplitude Squared Extracted from Vibration Responses of a Two-Axle Passing Vehicle

Identifying bridge damage using a movable test vehicle is highly regarded for its mobility, cost-effectiveness, and broad monitoring coverage. Previous studies have shown that the residual contact-point (CP) response between connected vehicles is free of the impact of vehicle self-vibrations and road roughness, making it particularly suitable for the indirect extraction of bridge modal properties. However, most experimental campaigns regarding contact-point (CP) responses focus on a single-axle testing vehicle within a non-moving state. This study aims to theoretically and experimentally identify bridge damage using the instantaneous amplitude squared (IAS) extracted from the residual CP response of a two-axle passing vehicle. First, the closed-form solution of the residual CP acceleration was derived for a two-axle vehicle interacting with a simply supported beam. The IAS index was constructed from the driving frequency of the residual CP acceleration. Then, numerical investigations using finite element simulation were conducted to validate using the IAS index for indirect bridge damage identification. The application scope of the approach under various vehicle speeds and road roughness grades was examined. Finally, a laboratory vehicle–bridge interaction system was tested to validate the approach. Numerical studies demonstrated that bridge damage could be directly determined by observing the IAS abnormalities, which were baseline-free. The IAS from the residual CP response outperformed the IAS from CP responses in identifying bridge damage. However, it was better to use the IAS when the vehicle speed was no greater than 2 m/s and the grade of the road surface roughness was not high. Laboratory tests showed that it was possible to identify bridge damage using the IAS extracted from the residual CP acceleration under perfect road surfaces. However, it fell short under rough road surfaces. Hence, further experiments are required to fully examine the capacity of the IAS for bridge damage identification in practical applications.

Dreaming of seamless interfaces: media and friction from the feuilleton to personal computing

ABSTRACT In human–computer interaction, the notion of ‘seamless interface’ describes a smooth interactive system that eliminates any possibility of friction between users and digital devices or platforms. Although interface designers have developed sophisticated technologies and strategies to pursue this aspiration, a frictionless user experience remains an ideal but ultimately impossible goal. Relying on the critical exploration of a series of historical case studies – the emergence of the feuilleton or serialised novel in the nineteenth century, the development of TV scheduling in the second half of the twentieth century, and the rise of the personal computer industry in the 1980s –, this article contextualises this ideal within a wider historical trajectory. Through an in-depth exploration of these three cases, we show how the dream of building a seamless relationship between media and readers, viewers or users remained ultimately unattainable due to the inherent frictions that persist between these two sides. The gap between the aspiration and the actual experiences of interacting with media foregrounded the emergence of feelings of ambivalence, conceived as an intrinsic component of people’s engagement with media. The longer history of media frictions provides a useful entry point to the contemporary digital landscapes, where the ubiquity of digital platforms goes hand in hand with a feeling of deep ambivalence from users, as the growing public concerns about the social costs of digital connection demonstrate.

Contagious infection-free medical interaction system with machine vision controlled by remote hand gesture during an operation

Background and objectiveMedical image visualization is a requirement in many types of surgery such as orthopaedic, spinal, thoracic procedures or tumour resection to eliminate risk such as “wrong level surgery”. However, direct contact with physical devices such as mice or touch screens to control images is a challenge because of the potential risk of infection. To prevent the spread of infection in sterile environments, a contagious infection-free medical interaction system has been developed for manipulating medical images. MethodsWe proposed an integrated system with three key modules: hand landmark detection, hand pointing, and hand gesture recognition. A proposed depth enhancement algorithm is combined with a deep learning hand landmark detector to generate hand landmarks. Based on the designed system, a proposed hand-pointing system combined with projection and ray-pointing techniques allows for reducing fatigue during manipulation. A proposed landmark geometry constraint algorithm and deep learning method were applied to detect six gestures including click, open, close, zoom, drag, and rotation. Additionally, a control menu was developed to effectively activate common functions. ResultsThe proposed hand-pointing system allowed for a large control range of up to 1200 mm in both vertical and horizontal direction. The proposed hand gesture recognition method showed high accuracy of over 97% and real-time response. ConclusionThis paper described the contagious infection-free medical interaction system that enables precise and effective manipulation of medical images within the large control range, while minimizing hand fatigue.

Artificial intelligence in dance education: Using immersive technologies for teaching dance skills

Artificial intelligence (AI) has led to a shift in modern dance education. Immersive technologies have become increasingly common worldwide, helping educators to improve the quality of dance pedagogy and increase the effectiveness of dance training. The article investigates the ways of using immersive technologies powered by artificial intelligence in dance education. The research explores the theoretical literature on dance education and the use of artificial intelligence in dance education and dance choreography. The scholars examine the impact of innovative technology solutions used in dance pedagogical practice on the development of dance skills in students. The study also discusses the functionality of interactive and multimedia dance teaching systems, including AI-powered virtual mentoring and cognitive simulations of human mind operations. The research analysed the use of virtual reality (VR), augmented reality (AR), and mixed reality (MR) in dance education. This research also focuses on mobile applications used for teaching modern dance. The proposed framework for dance education is based on digital technologies, which help to develop dance skills and improve teaching practices. The scholars conclude that the development and improvement of dance skills are possible only if a teacher combines virtual and real environments in everyday practices. The findings can be used by dance teachers, professional dancers, software developers, and researchers who examine the innovative practices and the application of artificial intelligence in dance education. The ecosystem model reframes thinking about approaches to dance education and can serve as the basis for further development of dance courses and dance style teaching modes.