Intelligent Video Surveillance System for Elderly People Living Alone Based on ODVS

Intelligent video surveillance for elderly people living alone using Omni-directional Vision Sensor (ODVS) is an important application in the field of intelligent video surveillance. In this paper, an ODVS is utilized to provide a 360° panoramic image for obtaining the real-time situation for the elderly at home. Some algorithms such as motion object detection, motion object tracking, posture detection, behavior analysis are used to implement elderly monitoring. For motion detection and object tracking, a method based on MHoEI(Motion History or Energy Images) is proposed to obtain the trajectory and the minimum bounding rectangle information for the elderly. The posture of the elderly is judged by the aspect ratio of the minimum bounding rectangle. And there are the different aspect ratios in accordance with the different distance between the object and ODVS. In order to obtain activity rhythm and detect variously behavioral abnormality for the elderly, a detection method is proposed using time, space, environment, posture and action to describe, analyze and judge the various behaviors of the elderly in the paper. In addition, the relationship between the panoramic image coordinates and the ground positions is acquired by using ODVS calibration. The experiment result shows that the above algorithm can meet elderly surveillance demand and has a higher recognizable rate.

Intelligent Virtual Assistant System(IVAS) for Air Traffic Controllers

Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing due to the rapid growth of air traveling.Controllers are usually dealing with multiple aircrafts at a time and must make quick and accurate decisions to ensure the safety of aircrafts.Heavy workload and high responsibilities create air traffic control a stressful job that sometimes could be error-prone and time-consuming,since controlling and decision-making are solely dependent on human intelligence.To provide effective solutions for the mentioned on the job challenges of the controllers,this study proposed an intelligent virtual assistant system(IVAS)to assist the controllers thereby to reduce the controllers’workload.Consisting of four main parts,which are voice recognition,display conversation on screen,task execution,and text to speech,the proposed system is developed with the aid of artificial intelligence(AI)techniques to make speedy decisions and be free of human interventions.IVAS is a computer-based system that can be activated by the voice of the air traffic controller and then appropriately assist to control the flight.IVAS identifies the words spoken by the controller and then a virtual assistant navigates to collect the data requested from the controllers,which allows additional or free time to the controllers to contemplate more on the work or could assist to another aircraft.The Google speech application programming interface(API)converts audio to text to recognize keywords.AI agent is trained using the Hidden marko model(HMM)algorithm such that it could learn the characteristics of the distinct voices of the controllers.At this stage,the proposed IVAS can be used to provide training for novice air traffic controllers effectively.The system is to be developed as a real-time system which could be used at the air traffic controlling base for actual traffic controlling purposes and the system is to be further upgraded to perform the task by recognizing keywords directly from the pilot voice command.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

A Framework for Intelligent Decision Support System for Traffic Congestion Management System

Traffic congestion problem is one of the major problems that face many transportation decision makers for urban areas. The problem has many impacts on social, economical and development aspects of urban areas. Hence the solution to this problem is not straight forward. It requires a lot of effort, expertise, time and cost that sometime are not available. Most of the existing transportation planning software, specially the most advanced ones, requires personnel with lots practical transportation planning experience and with high level of education and training. In this paper we propose a comprehensive framework for an Intelligent Decision Support System (IDSS) for Traffic Congestion Management System that utilizes a state of the art transportation network equilibrium modeling and providing an easy to use GIS-based interaction environment. The developed IDSS reduces the dependability on the expertise and level of education of the transportation planners, transportation engineers, or any transportation decision makers.

Problems and Countermeasures of Intelligent Elderly Care Service in the Context of Fewer Children in China

With the intensification of population aging and the implementation of the three-child policy,the elderly care pressure of Chinese families continues to rise.Therefore,accelerating the construction of a new intelligent elderly care service model is an important measure to actively respond to population aging,ease the burden of family elderly care and promote high-quality economic development.In view of this,this study analyzed the intelligent elderly care service to explore the relevant countermeasures of the intelligent elderly care service in the context of fewer children.

On the Problems and Countermeasures of Urban Community Intelligence Service for the Elderly

The problem of aging population is a major trend in the twenty-first Century. As a new way of old-age pension, intelligent endowment service has been implemented in China, and gradually extended. But it has some problems： the contents of the service are not enough and it cannot fit other people＇s needs. It is the lack of linkage on every department of government and the funds and so on .In order to solve these problems, we should expand the service content and improve the service level, define the government＇s role and function, increase the sources of funding and so on. With the implementing of these measures, intelligent endowment services will get further development. And it will deal with the social problem of aging better.

Comparison of short-term efficacy between totally laparoscopic gastrectomy and laparoscopic assisted gastrectomy for elderly patients with gastric cancer

BACKGROUND Totally laparoscopic gastrectomy(TLG)entails both gastrectomy and gastrointestinal reconstruction under laparoscopy.Compared with laparoscopic assisted gastrectomy(LAG),TLG has been demonstrated in many studies to require a smaller surgical incision,result in a faster postoperative recovery and less pain and have comparable long-term efficacy,which has been a research hotspot in recent years.Whether TLG is equally safe and feasible for elderly patients remains unclear.AIM To compare the short-term efficacy of and quality of life(QOL)associated with TLG and LAG in elderly gastric cancer(GC)patients.METHODS The clinicopathological data of 462 elderly patients aged≥70 years who underwent LAG or TLG(including distal gastrectomy and total gastrectomy)between January 2017 and January 2022 at the Department of General Surgery,First Medical Center,Chinese PLA General Hospital were retrospectively collected.A total of 232 patients were in the LAG group,and 230 patients were in the TLG group.Basic patient information,clinicopathological characteristics,operation information and QOL data were collected to compare efficacy.Compared with those in the LAG group,intraoperative blood loss in the TLG group was significantly lower(P<0.001),and the time to first flatus and postoperative hospitalization time were significantly shorter(both P<0.001).The overall incidence of postoperative complications in the TLG group was significantly lower than that in the LAG group(P=0.01).Binary logistic regression results indicated that LAG and an operation time>220 min were independent risk factors for postoperative complications in elderly patients with GC(P<0.05).In terms of QOL,no statistically significant differences in various preoperative indicators were found between the LAG group and the LTG group(P>0.05).Compared with the laparoscopic-assisted total gastrectomy group,patients who received totally laparoscopic total gastrectomy had lower nausea and vomiting scores and higher satisfaction with their body image(P<0.05).Patients who underwent laparoscopic-assisted distal gastrectomy were more satisfied with their body image than patients in the totally laparoscopic distal gastrectomy group(P<0.05).CONCLUSION TLG is safe and feasible for elderly patients with GC and has outstanding advantages such as reducing intracorporeal blood loss,promoting postoperative recovery and improving QOL.

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

A Study of Visual Event-Related Potential and Reaction Time in Elderly People: Comparative Analysis of the Scores of Intelligence Test in 30 Subjects

The results of visual event-related potential(ERP)examinations and reactiontime(RT)tests were reported in 30 elders and compared with their performanceintellegence quotient(PIQ)scores.The subjects consisted of 18 males and 12 femalesaged 50-71(mean 61.4)years old.No history of central nervous system disease wasfound.The visual stimuli were randomly presented to the subject,including three sym-bols:E as target stimulus with 0.15 probability,and H and E as nontarget stimuliwith 0.15 and 0.70 probability respectively.The recording electrodes were placed on Fzand Pz.The duration from the subject seeing the target to touching a button immediatelywas considered as reaction time(RT).It was shown that the P3 latency at Pz was longer than that at Fz and the P3amplitude at Pz was larger than that at Fz,and that the RT was longer than P3 latencywith obvious effect of distribution(P【0.05 at Fz and P】0.05 at Pz)as well .The higherthe PIQ scores,the longer the RT and the P3 latency.It is suggested that the ERPmight reflect the differences of PIQ scores,and the P3 is an objective index.We considerthat the research of ERP is of great interest in the neuropsychological and neurological sci-ences.

Improving the Accuracy of Under-Fog Driving Assistance System

Driving in fog condition is dangerous. Fog causes poor visibility on roads leading to road traffic accident (RTA). RTA in Albaha is common because of its rough terrain, in addition to the climate that is mainly rainy and foggy. The rain season in Albaha region begins in October to February characterized by rainfall and fog. Many studies have reported the adverse effects of the rain on RTA which results in an increased rate of crashes. On the other hand, Albaha region is not supported by a proper intelligent transportation system and infrastructure. Thus, a Driver Assistance System (DAS) that requires minimum infrastructure is needed. A DAS under fog called No_Collision has been developed by a researcher in Albaha University. This paper discusses an implementation of adaptive Kalman Filter by utilizing Fuzzy logic system with the aim to improve the accuracy of position and velocity prediction of the No_Collision system. The experiment results show a promising adaptive system that reduces the error percentage of the prediction up to 56.58%.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

Improving Clinical Support through Retrieval-Augmented Generation Powered Virtual Health Assistants

This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery.

基于《国际功能、残疾和健康分类》评价中国智能化辅盲设备的补偿功能

背景:辅盲设备及技术的应用是当今视力障碍人群常用的干预手段,可提高日常生活活动的参与及工作学习能力,帮助其回归家庭、回归社会。数字信息和智能化时代下辅盲设备的形式、技术和功能各有不同,其分类目前暂未能得到有效统一的讨论和评价。目的:基于《国际功能、残疾和健康分类》评价国内视力障碍人群智能化辅盲设备的补偿功能。方法:通过计算机检索中国知网、维普和万方数据库2013-01-01/2023-12-31收录的相关文献。基于《国际功能、残疾和健康分类》理论模式和框架结构,应用术语结构和编码程序归纳相关视力障碍评估类目,整理与分析国内视力障碍的智能化辅盲设备补偿功能研究与分类。结果与结论:①共纳入197篇文献:其中身体功能1篇,包含b2(b210);身体结构1篇,包含s2(s220);活动与参与共119篇,包含d1(10篇,d110、d115、d120、d140、d166),d3(4篇,d315、d325、d345、d360),d4(102篇,d465、d470),d8(3篇,d820、d825);环境因素共76篇,包含e1(72篇,e115、e120、e125、e130、e140、e150、e155、e160),e2(4篇,e210、e240);②基于《国际功能、残疾和健康分类》的视力障碍智能化辅盲设备补偿功能研究分类包含4个部分、8个分类、25个类目,领域涉及身体补偿类、生活用品类、教育学习类、出行导盲类、布局规划类。

骨科机器人在临床应用中的分类及优缺点

背景:步入智能时代以来,人们对应用于医疗方面的人工智能技术的研究热情越发高涨。在骨科领域,机器人辅助技术是最具吸引力的人工智能技术之一。经过近40年的发展,机器人辅助技术逐渐成熟,可参与的骨科手术种类逐渐增多,且起到至关重要的作用。目的:总结到目前为止骨科机器人在临床的应用情况,并分析其优缺点。方法:通过对PubMed数据库和中国知网已收录的相关文献进行检索,英文检索词为“robot-assisted technology,surgical robots,orthopaedic robots,robotic arm,orthopaedic,orthopaedic surgery,joint,spine,trauma,bone tumor”,中文检索词为“机器人辅助技术,手术机器人,骨科机器人,机械臂,骨科,骨科手术,关节,脊柱,创伤,骨肿瘤”。按照入选标准进行筛选共得到82篇相关性高的文献。结果与结论:目前机器人辅助技术在临床中的应用比较成熟,可用于大多数骨科手术中,有着不错的临床表现;骨科机器人主要优点包括缓解临床医生的工作压力、减少术中副损伤、缩短辐射暴露时间等,但同时也可能存在手术操作时间延长和术后并发症增加的情况。未来骨科机器人的发展将沿着高智能、低成本、更快捷等方向发展。

ChatGPT在产品设计中效率和个性化设计的影响研究

随着人工智能(AI)技术的不断发展,AI已成为产品设计领域的重要工具。首先,分析了产品设计面临的诸多局限性,如设计周期长、市场需求变化快以及资源的严重浪费等。接着,探讨了ChatGPT如何提升产品设计的质量和效率,具体表现在培养创造力、优化设计解决方案以及为用户提供个性化指导。通过具体案例,ChatGPT在设计任务中的高效性与迅速性缩短了整体设计时间,凸显了其在改善产品设计流程中的价值,包括促进团队间的协作、减少设计迭代以及降低整体设计成本等。最后,本文展望了人工智能在智能产品设计和个性化设计中的应用前景,指出其将可能引领行业未来的发展方向。

智慧教育中智能交互助教学伴体系构建探究

随着人工智能时代的到来与发展,促使传统课堂教学模式的转变,同时,彻底转变了师生之间的关系,借助先进智能技术与语言处理方式,拉近教师与学生之间的距离,从而发挥出教学辅导作用与价值,降低教师教学负担和辅导压力,全面激发学生自主学习意识与能力。基于此,文章主要对智能交互助教学伴体系构建的重要性进行分析,并从课前、课堂以及课后三个层面入手,提出智慧教育中智能交互助教学伴体系构建的策略,仅供相关人员参考借鉴。

数字化背景下脑脊液细胞形态学教学模式的探索

探讨在教育数字化背景下利用人工智能辅助诊断系统开展脑脊液细胞形态学教学的效果。以2019年1月至2022年8月在陆军军医大学第二附属医院血液病医学中心进修的32名学员为研究对象,随机分为试验组与对照组,试验组采用人工智能辅助诊断系统教学模式,对照组采用传统教学模式,比较两组学员学习效果。实践表明,基于人工智能辅助诊断系统的教学模式,有助于提高学员对脑脊液细胞形态学的学习兴趣,具备良好的细胞识别能力,教学效果较好。

Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy

Assisted diagnosis using artificial intelligence has been a holy grail in medical research for many years, and recent developments in computer hardware have enabled the narrower area of machine learning to equip clinicians with potentially useful tools for computer assisted diagnosis(CAD) systems. However, training and assessing a computer's ability to diagnose like a human are complex tasks, and successful outcomes depend on various factors. We have focused our work on gastrointestinal(GI) endoscopy because it is a cornerstone for diagnosis and treatment of diseases of the GI tract. About 2.8 million luminal GI(esophageal, stomach, colorectal) cancers are detected globally every year, and although substantial technical improvements in endoscopes have been made over the last 10-15 years, a major limitation of endoscopic examinations remains operator variation. This translates into a substantial inter-observer variation in the detection and assessment of mucosal lesions, causing among other things an average polyp miss-rate of 20% in the colon and thus the subsequent development of a number of post-colonoscopy colorectal cancers. CAD systems might eliminate this variation and lead to more accurate diagnoses. In this editorial, we point out some of the current challenges in the development of efficient computer-based digital assistants. We give examples of proposed tools using various techniques, identify current challenges, and give suggestions for the development and assessment of future CAD systems.

Left ventricular assist devices as destination therapy in stage D heart failure

1 Introduction? Mechanical circulatory support (MCS) has increasingly become an important management opportunity for patients with stage D heart failure (HF) with remarkable impact on patient survival and quality of life. Early clinical trials have demonstrated improved outcomes of durable left ventricular assist device (LVAD) support compared with optimal medical management.[1] As technology advanced, continuous flow LVADs outperformed pulsatile flow devices in clinical trials and the field migrated to HeartMate (Abbott Laboratories, Abbott Park, IL) and HeartWare (Medtronic, Minneapolis, MN) devices due to their clinical superiority. Among the continuous flow devices.

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

The driver’s cognitive and physiological states affect his/her ability to control the vehicle.Thus,these driver states are essential to the safety of automobiles.The design of advanced driver assistance systems(ADAS)or autonomous vehicles will depend on their ability to interact effectively with the driver.A deeper understanding of the driver state is,therefore,paramount.Electroencephalography(EEG)is proven to be one of the most effective methods for driver state monitoring and human error detection.This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades.First,the commonly used EEG system setup for driver state studies is introduced.Then,the EEG signal preprocessing,feature extraction,and classification algorithms for driver state detection are reviewed.Finally,EEG-based driver state monitoring research is reviewed in-depth,and its future development is discussed.It is concluded that the current EEGbased driver state monitoring algorithms are promising for safety applications.However,many improvements are still required in EEG artifact reduction,real-time processing,and between-subject classification accuracy.