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Development of Wireless Integrated Early Warning System (EWS) for Hospital Patient 1

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Steady Oey, Endra Astuti, Winda Andriatin, Yuli Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Nursery Department Cilacap Regional General Hospital Gatot Subroto No. 28 Central Java Cilacap53223 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

The implementation of the Early Warning System (EWS) in hospitalized patients aims to identify worsening of the patient’s condition based on the assessment of vital signs. Currently, the EWS is still applied manually by nurses by filling out observation sheets to assess the risk of patient deterioration, so that it can be used. There was an error filling in by the human error factor. In addition, the lack of nurses in Indonesia makes the implementation of EWS not optimal. Therefore, a system for measuring vital signs and risk assessment of patient deterioration was created automatically which nurses can monitor through the website. The results of this study are a system design prototype consisting of five data retrieval devices and a Raspberry Pi as a web server. Each device sends data to the server wireless using the MQ Telemetry Transport (MQTT) protocol. The test results show that the system can retrieve vital signs data automatically every hour and displays a risk that is one level lower than manual EWS implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deterioration

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The Michigan robotics Undergraduate Curriculum: Defining the Discipline of robotics for Equity and Excellence

arXiv

引用

arXiv 2023年

作者： Jenkins, Odest Chadwicke Grizzle, Jessy Atkins, Ella Stirling, Leia Rouse, Elliott Guzdial, Mark Provost, Damen Mann, Kimberly Millunchick, Joanna Robotics and Electrical Engineering & Computer Science United States Undergraduate Education Robotics Institute United States Robotics Institute United States Graduate Programs Robotics Institute United States Robotics and Industrial Engineering United States Robotics and Mechanical Engineering United States Electrical Engineering & Computer Science United States Engineering Education Research Program United States Undergraduate Education College of Engineering United States University of Michigan United States

The robotics Major at the University of Michigan was successfully launched inthe 2022-23 academic year as an innovative step forward to better servestudents, our communities, and our society. Building on our guiding principleof "robotics with Respect" and our larger robotics Pathways model, the Michiganrobotics Major was designed to define robotics as a true academic disciplinewith both equity and excellence as our highest priorities. Understanding thattalent is equally distributed but opportunity is not, the Michigan roboticsMajor has embraced an adaptable curriculum that is accessible through adiversity of student pathways and enables successful and sustained career-longparticipation in robotics, AI, and automation professions. The results afterour planning efforts (2019-22) and first academic year (2022-23) have beenhighly encouraging: more than 100 students declared robotics as their major,completion of the robotics major by our first two graduates, soaringenrollments in our robotics classes, thriving partnerships with HistoricallyBlack Colleges and Universities. This document provides our original curricularproposal for the robotics Undergraduate program at the University of Michigan,submitted to the Michigan Association of State Universities in April 2022 andapproved in June 2022. The dissemination of our program design is in the spiritof continued growth for higher education towards realizing equity andexcellence. The most recent version of this document is also available on Google Docsthrough this link: https://***/robotics_majorMSC Codes 97 © 2023, CC BY.

关键词： Curricula

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Automatic wall slant angle map generation using 3D point clouds

Automatic wall slant angle map generation using 3D point clo...

引用

作者： Kim, Jeongyun Yun, Seungsang Jung, Minwoo Kim, Ayoung Cho, Younggun Department of Civil and Environmental Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of Department of Robotics Engineering Yeungnam University Gyeongsan Korea Republic of

Recently, quantitative and repetitive inspections of the old urban area were conducted because many structures exceed their designed lifetime. The health of a building can be validated from the condition of the outer wall, while the slant angle of the wall widely serves as an indicator of urban regeneration projects. Mostly, the inspector directly measures the inclination of the wall or partially uses 3D point measurements using a static light detection and ranging (LiDAR). These approaches are costly, time-consuming, and only limited space can be measured. Therefore, we propose a mobile mapping system and automatic slant map generation algorithm, configured to capture urban environments online. Additionally, we use the LiDAR-inertial mapping algorithm to construct raw point clouds with gravity information. The proposed method extracts walls from raw point clouds and measures the slant angle of walls accurately. The generated slant angle map is evaluated in indoor and outdoor environments, and the accuracy is compared with real tiltmeter measurements. 1225-6463/$ © 2021 ETRI

关键词： Conformal mapping

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A Resilient Navigation and Path Planning System for High-speed Autonomous Race Car

arXiv

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arXiv 2022年

作者： Lee, Daegyu Jung, Chanyoung Finazzi, Andrea Seong, Hyunki Shim, D. Hyunchul Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

This paper describes a resilient navigation and planning system used in the Indy Autonomous Challenge (IAC) competition. The IAC is a competition where full-scale race cars run autonomously on Indianapolis Motor Speedway(IMS) up to 290 km/h (180 mph). Race cars will experience severe vibrations. Especially at high speeds. These vibrations can degrade standard localization algorithms based on precision GPS-aided inertial measurement units. Degraded localization can lead to serious problems, including collisions. Therefore, we propose a resilient navigation system that enables a race car to stay within the track in the event of localization failures. Our navigation system uses a multi-sensor fusion-based Kalman filter. We detect degradation of the navigation solution using probabilistic approaches to computing optimal measurement values for the correction step of our Kalman filter. In addition, an optimal path planning algorithm for obstacle avoidance is proposed. In this challenge, the track has static obstacles on the track. The vehicle is required to avoid them with minimal time loss. By taking the original optimal racing line, obstacles, and vehicle dynamics into account, we propose a road-graph-based path planning algorithm to ensure that our race car can perform efficient obstacle avoidance. The proposed localization system was successfully validated to show its capability to prevent localization failures in the event of faulty GPS measurements during the historic world’s first autonomous racing at IMS. Owing to our robust navigation and planning algorithm, we were able to finish the race as one of the top four teams while the remaining five teams failed to finish due to collisions or out-of-track violations. © 2022, CC BY-NC-SA.

关键词： Kalman filters

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Emotion Recognition based on Heart Rate Detection using Logistic Regression for Automation Food Healing Application 8

Emotion Recognition based on Heart Rate Detection using Logi...

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8th International Conference on Education and Technology, ICET 2022

作者： Antonio, Nicholas Jason Nobel, Yosse Ricardo Ho, Hwi-Chie Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Bina Nusantara University Industrial Engineering Department Jakarta Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

Daily pressure activity especially in work place or any society condition may causing stress or pressure for the people deal with that condition. This stress may causing of decreasing of work performance and achievement in the daily activity. Furthermore in worse condition, people may went to psychiatrist consulting regarding the condition in order to heal the stress or pressure feeling. In this work, food recommendation healing in order to improve the mood based on mood recognition using heartbeat rate detection system is developed. Mood detection results will be used to recommend foods that can improve the mood. the mood detection system work based on automatic intelligent heartbeat rate identification. This system installed on an Android cellphone, so its make the system more flexible and portable. the system has also connected to the web, in order used by doctors to monitor the patient's condition if the patient is being monitoring due to health mental diseases treatment. This system has worked properly with mood detection based on heartbeat rate with 96% and 97%, training and testing accuracy, respectively. Furthermore, this system is connected to the website for monitoring purposes for the psychiatrist to monitor their patient intern of mood changing fluctuation © 2022 IEEE.

关键词： Data handling

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Design Of A ‘ Drive Wave ‘ System based on Communications System Remote Real-time Running Text and Speech to Text Converter using Light Emitting Diode (LED) Display

Design Of A ‘ Drive Wave ‘ System based on Communications ...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Juan Felix Christopher Sandy Setiawan Elioenai Sitepu Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Drive Wave is a device that can help drivers communicate with other road users by displaying familiar words and icons as well as a speech to text feature that can make it easier for drivers to communicate with other road users. in this work, a system design prototype consisting of two devices, namely Remote and LED message board. The most spoken words used and icons displayed on the LED message board are controlled wireless using a Remote using the ESP-NOW protocol and Bluetooth Low Energy (Bluetooth LE). The two ways of he LED message board controlled by the remote are manual and automatic speech to text converter system controlled the LED message board displayed. the manual way, the user pressed the button to choice the word to be displayed. in the automatic voiced control, the LED displayed the word uttered by the user. the automatic voiced controlled applied Mel-frequency cepstral coefficients (MFCC) as feature extraction techniques and Long Short-Term Memory (LSTM) method for word identification methods. The results showed that the automatic voice controlled 99.78% and 100% for training and validating accuracy, respectively. the real testing result showed that 24 times testing which have 100% accuracy.

关键词： Meters Pediatrics Roads Drives Light emitting diodes Mel frequency cepstral coefficient Long short term memory Speech to text Vehicles Testing

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Analyze and Predict Car Accidents Using Different Machine Learning Algorithms

Analyze and Predict Car Accidents Using Different Machine Le...

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International Conference on Information Management and Technology (ICIMTech)

作者： Farrell Putra Harimanto Chris Andrew Henry William Muhammad Zacky Asy'ari Samuel Albert Artanto Muhammad Nurul Puji Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

Traffic accidents can occur from various factors, ranging from the driver themselves to problems with the vehicle. Road and traffic accidents are the most significant factor of fatalities in the world. Most of the causes of accidents are the drivers themselves. Poor physical and mental conditions can significantly impact how to drive. The driving experience is also important; the longer the driver has been driving, the more professional he will be, and vice versa. Smart City solutions can predict the severity of accidents and give information to the authorities in order to reduce the number of fatalities. This paper investigates the factors that may lead to an accident on the road. By looking at data collected from traffic-related accidents and then visualizing it to make it easy to analyze; then, from this data, we will predict the causes of traffic accidents so they can be avoided. The best algorithm based on accuracy, MAE and RMSE is Random Forest Classifier with 0.8461, 0.1663 and 0.4376, respectively.

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Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling

arXiv

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arXiv 2022年

作者： Wijaya, Kevin Tirta Paek, Dong-Hee Kong, Seung-Hyun Robotics Program CCS Graduate School of Mobility KAIST Korea Republic of

Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud. Unfortunately, such a process may result in a substantial loss of granular information due to the sampling operation. Moreover, the widely-used max-pooling feature aggregation may exacerbate the loss since it completely neglects information from non-maximum point features. Due to the compounded loss of information concerning granularity and non-maximum point features, the resulting high-semantic point features from existing networks could be insufficient to represent the local context of a point cloud, which in turn may hinder the network in distinguishing fine shapes. To cope with this problem, we propose a novel point cloud feature learning network, PointStack, using multi-resolution feature learning and learnable pooling (LP). The multi-resolution feature learning is realized by aggregating point features of various resolutions in the multiple layers, so that the final point features contain both high-semantic and high-resolution information. On the other hand, the LP is used as a generalized pooling function that calculates the weighted sum of multi-resolution point features through the attention mechanism with learnable queries, in order to extract all possible information from all available point features. Consequently, PointStack is capable of extracting high-semantic point features with minimal loss of information concerning granularity and non-maximum point features. Therefore, the final aggregated point features can effectively represent both global and local contexts of a point cloud. In addition, both the global structure and the local shape details of a point cloud can be well comprehended by the network head, which enables PointStack to advance the state-of-the-art of feature learning on point cl

关键词： Semantics

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Automation Lamp Healing Application based on Face Expression Identification System using Convolution Neural Network (CNN)

Automation Lamp Healing Application based on Face Expression...

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International Conference on Electrical Engineering and Informatics, ICEEI

作者： Devin Onil Saduki Simangunsong Mahesh Muhammad Nurul Puji Winda Astuti Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

The disruption of routines, education, recreation, as well as concerns about family income and health, has left many young people feeling fearful, angry, and worried about their future. In this system, a smart system will be developed that can capture, process, and interpret facial expressions so that a person's emotions can be easily and accurately identified, which is one of the best solutions. The system will use artificial intelligence that has been trained beforehand using suitable datasets. In the processed of developing the system, Face image with the size of 48 x 48 x 1 became the input of the system, processes by Convolution layer using Relu activation function. The activation function used in the output layer is softmax because it is suitable for more than 2 classifications. by applying CNN based on Adam optimization which have result on 98.96% and 70%, for training and testing accuracy, respectively. Furthermore, the system accurately turn-on the healing lamp based on the emotion identified.

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Walk With Me: Socially Acceptable Speed and Distance Control for Mobile Wayfinding Robots

Walk With Me: Socially Acceptable Speed and Distance Control...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Karl D. Hansen Anders Clement Hans Christian Østgård Larsen Jonas Andersen Oliver Beyer Lauritsen Katrine Møller-Rahbek Department of Materials and Production Aalborg University Aalborg Denmark Students of the Robotics MSc Program at Aalborg University Aalborg Denmark

Robots can help people find their way around in complex buildings and environments. Robots are embodied, which makes it more intuitive for users to follow them around compared to, e.g., colored lines on the floor. However, they need to be socially intelligent for people to accept them. Like people, robots should keep a comfortable distance to its users when *** investigate the optimization of a guiding robot’s speed and distance when guiding to improve the user experience. Our findings indicate a correlation between the user’s speed and the distance they keep from the robot, which can be utilized to control the robot’s speed.A person tracking system is implemented on a Spot quadruped mobile robot platform from Boston Dynamics, using the on-board depth and 2D cameras plus an Intel NUC and a Jetson Nano for processing. The system uses the Mobilenet-SSDv2 CNN for user detection and Kalman filtering for *** from human-robot interaction tests on footpaths at Aalborg University is analyzed to create a linear model of the speed-distance relationship. Based on this, a control law is proposed and tested, demonstrating the ability to build a controller that allows the person following the robot to set the desired velocity of the robot.

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