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International Conference on Applied System Innovation (ICASI)

作者： Yu-Chen Tu Hsuan-Chih Wang Chien-Pin Liu Chia-Yeh Hsieh Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

Fall incidents among the elderly represent a significant global concern, often resulting in physical injuries and psychological distress. It is crucial to develop reliable fall detection systems which are capable of identifying fall events immediately and triggering alerts for assistance. However, real-world fall occurrences are infrequent, leading to a highly imbalanced class situation. Training a model with imbalanced datasets may result in biased models with poor performance in fall detection. To address this challenge, various techniques such as data transformation and Synthetic Minority Oversampling Technique (SMOTE) have been proposed. However, these methods are constrained by issues such as limitations in input data size or sensitivity to outliers. Compared to other methods, variational autoencoder (VAE) can generate data with a similar probability distribution to the original input data while constraining the latent representation in a Gaussian distribution. This study proposes a VAE-based data augmentation method for wearable-based fall detection system. The proposed method is validated on the FallAllD public dataset, achieving a F-score of 99.46%. The performance has been increased by 2.21%. The results demonstrate the effectiveness of VAE-based data augmentation technique in enhancing fall detection systems and its superior performance compared with other traditional data augmentation methods.

关键词： Training Technological innovation Sensitivity Psychology Data augmentation Probability distribution Reliability

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PREFALLKD: PRE-IMPACT FALL DETECTION VIA CNN-VIT KNOWLEDGE DISTILLATION

arXiv

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arXiv 2023年

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems" using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). Copyright © 2023, The Authors. All rights reserved.

关键词： Fall detection

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tin-Han Chi Kai-Chun Liu Chia-Yeh Hsieh Yu Tsao Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

关键词： Computational modeling System performance Aging Transformers Reliability Fall detection Usability

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Facilitating decision making in authentic contexts: an SVVR-based experiential flipped learning approach for professional training

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Interactive Learning Environments 2023年第8期31卷 5219-5235页

作者： Hsin Huang Gwo-Jen Hwang Shao-Chen Chang a Department of Nursing Tri-Service General Hospital National Defense Medical Center Taipei Taiwan b Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan c International Bachelor Program in Informatics and the Department of Information Communication Yuan Ze University Chung-Li Taiwan

ABSTRACTFacilitating decision making in authentic contexts is an important educational objective of professional training. To enable learners to have more opportunities to practice in authentic contexts under the guidance of instructors, the flipped learning mode of shifting the lectures to be the pre-class activity with the presentation of digital media has gradually been receiving attention. However, the conventional flipped learning mostly uses videos to present teaching content. In such a learning environment with one-way information and lack of experience, it is not easy for most learners to experience the actual situation encountered in the nursing procedure, which affects their judgment and performance when dealing with actual scenarios. To solve this problem, the present study adopted spherical video-based virtual reality (SVVR) to provide an experiential learning mode in flipped learning. To verify the effects of this teaching mode, this study conducted an experiment in a blood transfusion safety training course. The experimental group used the SVVR-based experiential flipped learning (SVVR-EFL) mode, while the control group used the conventional flipped learning mode. The results showed that using the SVVR-EFL mode not only enhanced new nursing staff’s learning achievement, but also increased their decision-making performance, meta-cognition tendency, problem-solving tendency and classroom engagement.

关键词： Flipped learning virtual reality experiential learning decision making professional training

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Application of HL7 FHIR in a Microservice Architecture for Patient Navigation on Registration and Appointments 3

Application of HL7 FHIR in a Microservice Architecture for P...

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3rd IEEE/ACM International Workshop on Software Engineering for Healthcare, SEH 2021

作者： Bettoni, Giovani Nicolas Lobo, Thafarel Camargo Flores, Cecilia Dias Dos Santos, Bruno Gomes Tavares Da Silva, Filipe Santana Pontifical Catholic University of Rio Grande Do sul Bachelor of Biomedical Informatics School of Technology Porto Alegre Brazil Federal University of Health Sciences of Porto Alegre Health Information Technology and Management Graduation Program Porto Alegre90050-170 Brazil Federal University of Rio Grande Do sul Bachelor of Biomedical Informatics Psychiatry and Behavioral Sciences Graduation Program Porto Alegre Brazil Federal University of Health Sciences of Porto Alegre Exact and Applied Sciences Department Porto Alegre90050-170 Brazil

ISBN: (纸本)9781665444583

Electronic Health Record Systems (EHR-S) are commonly developed in monolithic architectures. This architectural style presents greater complexity and demands more effort when we think of interoperability. A solution proposal is the creation of Microservices that use HL7 FHIR as an interoperability strategy. In this sense, it is presented the development of a prototype, based on a microservices architecture, to act in a real scenario of Patient Navigation (PN). The problem was subdivided into 3 steps: definition of architecture, development and construction of an interface to simulate the role of the navigator. The Patient and Appointment microservices are capable of synchronous communication to query and record information. In general, the implemented architectural style not only isolates information domains but can receive data from multiple sources while maintaining essential functionality. This type of approach plays a crucial role in a hospital environment, specifically in PN, highlighting the importance of the standard and expanding the possibilities for further research to be conducted. © 2021 IEEE.

关键词： Interoperability

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Methodology for the design of a nanosatellite telecommunication system in S-Band based on Software for Digital Mission Engineering

Methodology for the design of a nanosatellite telecommunicat...

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International Conference on AeroSpace Science and Technology 2023, ICASST 2023

作者： García-Guillén, L.A. Sanchéz-Sanchéz, J.L. Ramírez-Aguilar, J.A. Ferrer-Pérez, J.A. Romo-Fuentes, J.C. Farah-Simón, A. Chávez-Moreno, R.G. Sánchez Hernández, D.C. Sánchez-Guzmán, J.A. Valdés-Galicia, J.F. Master's and Doctorate Programm in Engineering Engineering Faculty Universidad Nacional Autónoma de México CDMX Mexico Bachelor's in Aero Systems Electronics Engineering Universidad Aeronáutica en Querétaro Queretaro Mexico Advanced Technology Unit School of Engineering UNAM Queretaro Juriquilla Mexico Astronomy Institute Universidad Nacional Autónoma de México CDMX Mexico Faculty of Informatics Autonomous University of Queretaro Queretaro Juriquilla Mexico Space Program University Universidad Nacional Autónoma de México CDMX Mexico

A trend in the development of nanosatellite platforms for technology demonstration has increased in both private industry and academic projects. Communication link design plays a crucial role in the success of space missions, enabling efficient and reliable data transmission between satellites and ground stations. In this context, the use of advanced tools such as System Tool Kit (STK) has gained significant importance by enabling a comprehensive analysis of the technical and operational aspects of the communication link. In an effort to make the future design of the communications subsystem payload on a nanosatellite more efficient and accessible, this paper shows a methodology for the efficient use of STK to perform link analysis. © Published under licence by IOP Publishing Ltd.

关键词： Nanosatellites

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Inappropriate Benefits and Identification of ChatGPT Misuse in programming Tests: A Controlled Experiment

arXiv

引用

arXiv 2023年

作者： Toba, Hapnes Karnalim, Oscar Johan, Meliana Christianti Tada, Terutoshi Djajalaksana, Yenni Merlin Vivaldy, Tristan Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Media Culture Department Faculty of Information Sciences and Arts Toyo University Kawagoe Japan EC-Council TampaFL United States

While ChatGPT may help students to learn to program, it can be misused to do plagiarism, a breach of academic integrity. Students can ask ChatGPT to complete a programming task, generating a solution from other people’s work without proper acknowledgment of the source(s). To help address this new kind of plagiarism, we performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance. We also reported how to manually identify programs aided with ChatGPT (via student behavior while using ChatGPT) and student perspective of ChatGPT (via a survey). Seventeen students participated in the experiment. They were asked to complete two programming tests. They were divided into two groups per the test: one group should complete the test without help while the other group should complete it with ChatGPT. Our study shows that students with ChatGPT complete programming tests two times faster than those without ChatGPT, though their programming performance is comparable. The generated code is highly efficient and uses complex data structures like lists and dictionaries. Based on the survey results, ChatGPT is recommended to be used as an assistant to complete programming tasks and other general assignments. ChatGPT will be beneficial as a reference as other search engines do. Logical and critical thinking are needed to validate the result presented by ChatGPT. © 2023, CC BY.

关键词： Students

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AI in Industry: Real-World Applications and Case Studies

TechRxiv

引用

TechRxiv 2023年

作者： Rahmaniar, Wahyu Maarif, Alfian ul Haq, Qazi Mazhar Iskandar, Muchammad Edo Institute of Innovative Research Tokyo Institute of Technology Kanagawa226-8503 Japan Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55166 Indonesia International Bachelor Program in Informatics and Computer Science Yuan Ze University Taoyuan320315 Taiwan Ezy Industries Jakarta Indonesia

Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. This paper examines the real-world application of AI in multiple sectors, including healthcare, finance, agriculture, retail, energy, and automotive. Several case studies are described to understand better the practical applications, results, and challenges of implementing AI. While many industries have reaped enormous benefits from AI, inherent challenges include data privacy, the potential for bias, and the continuing demand for skilled labor. This comprehensive review aims to provide AI application insights to professionals and researchers. Thus, as AI grows, there may be challenges and avenues for future research. © 2023, CC BY.

关键词： Agriculture

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Incorporating teacher intelligence into digital games: An expert system-guided self-regulated learning approach to promoting EFL students' performance in digital gaming contexts

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British Journal of Educational Technology 2023年第2期54卷 534-553页

作者： Chu, Shih-Ting Hwang, Gwo-Jen Chien, Shu-Yun Chang, Shao-Chen Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan International Bachelor Program in Informatics Department of Information Communication Yuan Ze University Taoyuan Taiwan

In recent years, several studies have reported the potential of employing digital games in EFL (English as Foreign Language) courses to promote students' learning motivation. However, scholars have pointed out that students generally lack self-learning ability, which is the key to the success of learning a foreign language. Therefore, it is crucial to foster students' self-learning ability during the game-based learning process. In this study, an expert system was developed to facilitate self-regulated learning in digital game-based learning contexts. To evaluate the effectiveness of the proposed approach, a quasi-experimental design was employed in a university English course. The experimental group students learned with the self-regulated English vocabulary game (SR-EVG) approach, while the control group students learned with the conventional English vocabulary game (C-EVG) approach. The experimental results indicated that the use of the SR-EVG approach could improve learners' English vocabulary achievement and self-regulation compared with the C-EVG approach without increasing students' English learning anxiety. Moreover, through qualitative interviews, it was found that students who used the SR-EVG approach would focus on their learning due to goal-setting in the game and would pay more attention to the learning strategies they used. Practitioner notes: What is already known about this topic Digital game-based learning situates students in a realistic situational environment, enabling students to learn by experiencing and interacting with the situations. Without appropriate scaffolding during digital game-based learning, students may become overly focused on the game, or perform many non-learning behaviors Self-regulated learning refers to students’ ability to learn on their own via goal setting, strategic planning, self-monitoring, and self-adjustment. What this paper adds An expert system-guided self-regulated learning approach was proposed to facilitate EFL s

关键词： Students

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Deep Learning-based Fall Detection Algorithm Using Ensemble ...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Chien-Pin Liu Ju-Hsuan Li En-Ping Chu Chia-Yeh Hsieh Kai-Chun Liu Chia-Tai Chan Yu Tsao Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a “long-lie.” Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.

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