Mobile Technologies for Smart Healthcare System Design

Guo, Xiaonan, Wang, Yan, Cheng, Jerry

Mobile Technologies for Smart Healthcare System Design
  • 出版商: Springer
  • 出版日期: 2024-09-12
  • 售價: $6,950
  • 貴賓價: 9.5 折 $6,603
  • 語言: 英文
  • 頁數: 213
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031573447
  • ISBN-13: 9783031573446

    • 海外代購書籍(需單獨結帳)

商品描述

This book is a detailed exploration of mobile technologies in the healthcare sector. The book starts with an overview of WiFi-based systems for activity recognition, utilizing existing infrastructure for health monitoring. It then examines mmWave technology for its precision in health systems and the design of a sophisticated fitness assistant system. The narrative progresses to wearable technologies, emphasizing their role in personal fitness and injury prevention. A significant focus is on eating habit monitoring for comprehensive dietary analysis. The book concludes with an innovative use of PPG sensors in wearables for intricate applications like gesture recognition and user authentication. Looking ahead, the book outlines future research directions, emphasizing the importance of securing deep learning techniques in mobile health technologies, developing adaptive systems for dynamic environments, and creating integrated systems for personalized medicine and comprehensive health monitoring. This forward-looking perspective highlights the need for proactive, preventive, and tailored healthcare solutions.

This book discusses various challenges in designing effective and robust mobile healthcare systems. One of the major challenges is collecting accurate and reliable sensor data, given the complexity and variability of human activities. This book tackled this challenge through innovative sensing modalities like WiFi, millimeter wave signals, and photoplethysmography that can capture fine-grained details about human activities and physiology. Robust algorithms are designed to extract meaningful features from the sensor data to interpret activities, gestures, and biometrics. System robustness across diverse environments is another big challenge in mobile healthcare. The solutions presented by this book could adapt to different settings using advanced learning techniques such as environment-invariant analysis and domain adaptation training. The book also dealt with practical issues like reducing training efforts, handling motion artifacts, and implementing systems using commercially available devices.

Overall, this book provides comprehensive methodologies leveraging cutting-edge mobile technologies to address key challenges in developing real-world healthcare applications for continuous monitoring, personalized assistance, dietary tracking, gesture recognition, and user authentication. The systems are designed to work reliably despite environmental variations, individual differences, and device constraints.

商品描述(中文翻譯)

本書詳細探討了醫療領域中的行動技術。書中首先概述了基於WiFi的活動識別系統，利用現有基礎設施進行健康監測。接著，探討了毫米波技術在健康系統中的精確性以及設計一個複雜的健身助手系統。隨著敘述的進展，重點轉向可穿戴技術，強調其在個人健身和傷害預防中的作用。書中還特別關注飲食習慣監測，以進行全面的飲食分析。最後，本書以可穿戴設備中PPG傳感器的創新應用作結，這些應用涉及手勢識別和用戶身份驗證等複雜功能。展望未來，本書概述了未來的研究方向，強調在行動健康技術中確保深度學習技術的重要性，開發適應動態環境的系統，以及創建個性化醫療和全面健康監測的整合系統。這種前瞻性的視角突顯了主動、預防和量身定制的醫療解決方案的必要性。

本書討論了設計有效且穩健的行動醫療系統所面臨的各種挑戰。其中一個主要挑戰是收集準確且可靠的傳感器數據，因為人類活動的複雜性和變異性。本書通過創新的感測方式，如WiFi、毫米波信號和光電容積描記法，來應對這一挑戰，這些技術能夠捕捉有關人類活動和生理的細微細節。設計了穩健的算法，以從傳感器數據中提取有意義的特徵，以解釋活動、手勢和生物識別。在多樣化環境中系統的穩健性是行動醫療中的另一大挑戰。本書提出的解決方案能夠利用先進的學習技術，如環境不變分析和領域適應訓練，適應不同的環境設置。本書還處理了實際問題，如減少訓練工作量、處理運動伪影，以及使用商業可用設備實施系統。

總體而言，本書提供了全面的方法論，利用尖端的行動技術來解決開發現實世界醫療應用中的關鍵挑戰，這些應用包括持續監測、個性化協助、飲食追蹤、手勢識別和用戶身份驗證。這些系統旨在儘管面對環境變化、個體差異和設備限制，仍能可靠運作。

作者簡介

Xiaonan Guo received the Ph.D. degree in computer science and engineering from The Hong Kong University of Science and Technology, Hong Kong, in 2013. He is currently an Assistant Professor with the department of information science and technology at George Mason University. Prior that he was an Assistant Professor at Indiana University-Purdue University, Indianapolis. He was a Research Associate with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, His research interests include pervasive computing, mobile computing, and cybersecurity and privacy. He has received the Best Paper Award from the ACM Conference on Information, Computer, and Communications Security (ASIACCS) in 2016 and the EAI International Conference on IoT Technologies for HealthCare (EAI Healthy IoT) in 2019.

Yan Wang is an Associate Professor in Computer & Information Sciences Department at Temple University. Before that, he was with the Department of Computer Science at SUNY, Binghamton. He received his Ph.D. degree in Electrical Engineering from Stevens Institute of Technology. His research interests include Cyber Security and Privacy, Internet of Things, Mobile and Pervasive Computing, and Smart Healthcare. His research is supported by the National Science Foundation (NSF). He is the recipient of the NSF CAREER Award. He is the recipient of the Best Paper Award from IEEE CNS 2018, IEEE SECON 2017, and ACM AsiaCCS 2016. He is serving and has served on the organizing committee of ACM MobiCom, IEEE INFOCOM, ACM WiSec, IEEE MASS, IEEE DYSPAN, and IEEE CNS. He is the Associate Editor of IEEE Transactions on Information Forensics and Security and the guest editor of the special issue of the Journal of Surveillance, Security and Safety. He regularly serves on the technical program committees of Top-ranked ACM and IEEE conferences, including ACM MobiCom, ACM MobiSys, IEEE INFOCOM, IEEE ICDCS, IEEE CNS, IEEE ICC. He also serves as the reviewer for prestigious journals, including IEEE/ACM Transactions on Networking (IEEE/ACM ToN), IEEE Transactions on Mobile Computing (IEEE TMC), IEEE Transactions on Wireless Communications (IEEE TWireless), and EURASIP Journal on Information Security.

Jerry Cheng was an Assistant Professor with the Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA. He was formerly a Postdoctoral Researcher with the Department of Statistics, Columbia University, New York, NY, USA. He had extensive industrial experiences as a Member of Technical Staff at AT&T Labs, Murray Hill, NJ, USA. He is currently an Assistant Professor of computer science with the New York Institute of Technology, New York. His background is a combination of computer science, statistics, and physics. His work has been reported by many new media, including MIT Technology Review, Yahoo News, Digital World, FierceHealthcare, and WTOP Radio. His research interests include big data analytics, statistical learning, Bayesian statistics, and their applications in computer systems and smart healthcare.

Yingying (Jennifer) Chen is a Professor and Department Chair of Electrical and Computer Engineering (ECE) and Peter Cherasia Endowed Faculty Scholar at Rutgers University. She is the Associate Director of Wireless Information Network Laboratory (WINLAB). She also leads the Data Analysis and Information Security (DAISY) Lab. She is a Fellow of Association for Computing Machinery (ACM), a Fellow of Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of National Academy of Inventors (NAI). Her research interests include Applied Machine Learning in Mobile Computing and Sensing, Internet of Things (IoT), Security in AI/ML Systems, Smart Healthcare, and Deep Learning on Mobile Systems. She is a pioneer in RF/WiFi sensing, location systems, and mobile security. Before joining Rutgers, she was a tenured professor at Stevens Institute of Technology and had extensive industry experiences at Nokia (previously Lucent Technologies). She has published 3 books, 4 book chapters and 300+ journal articles and refereed conference papers. She is the recipient of seven Best Paper Awards in top ACM and IEEE conferences. She is the recipient of NSF CAREER Award and Google Faculty Research Award. She received NJ Inventors Hall of Fame Innovator Award and is also the recipient of IEEE Region 1 Technological Innovation in Academic Award. Her research has been supported by many funding agencies including NSF, NIH, ARO, DoD and AFRL and reported in numerous media outlets including MIT Technology Review, CNN, Wall Street Journal, National Public Radio and IEEE Spectrum. She has been serving/served on the editorial boards of IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Wireless Communications (TWireless), IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Privacy and Security.

作者簡介(中文翻譯)

Xiaonan Guo於2013年獲得香港科技大學計算機科學與工程博士學位。目前，他是喬治梅森大學資訊科學與技術系的助理教授。在此之前，他曾擔任印第安納大學-普渡大學印第安納波利斯分校的助理教授。他曾是史蒂文斯理工學院電氣與計算機工程系的研究助理。他的研究興趣包括普遍計算、移動計算以及網絡安全與隱私。他於2016年獲得ACM信息、計算機與通信安全會議（ASIACCS）的最佳論文獎，以及2019年EAI國際會議IoT健康技術（EAI Healthy IoT）的最佳論文獎。

Yan Wang是天普大學計算機與信息科學系的副教授。在此之前，他曾在紐約州立大學賓漢頓分校的計算機科學系任職。他在史蒂文斯理工學院獲得電氣工程博士學位。他的研究興趣包括網絡安全與隱私、物聯網、移動與普遍計算以及智能醫療。他的研究得到了國家科學基金會（NSF）的支持。他是NSF CAREER獎的獲得者，並且曾獲得IEEE CNS 2018、IEEE SECON 2017和ACM AsiaCCS 2016的最佳論文獎。他目前及曾經擔任ACM MobiCom、IEEE INFOCOM、ACM WiSec、IEEE MASS、IEEE DYSPAN和IEEE CNS的組織委員會成員。他是IEEE信息取證與安全期刊的副編輯，並擔任《監控、安全與安全性期刊》特刊的客座編輯。他定期擔任頂尖ACM和IEEE會議的技術程序委員會成員，包括ACM MobiCom、ACM MobiSys、IEEE INFOCOM、IEEE ICDCS、IEEE CNS和IEEE ICC。他也擔任多個知名期刊的審稿人，包括IEEE/ACM網絡期刊（IEEE/ACM ToN）、IEEE移動計算期刊（IEEE TMC）、IEEE無線通信期刊（IEEE TWireless）和EURASIP信息安全期刊。

Jerry Cheng曾是羅伯特·伍德·約翰遜醫學院的助理教授，任職於新澤西州的羅格斯大學。他曾是哥倫比亞大學統計系的博士後研究員，位於紐約州。他在AT&T實驗室擔任技術人員，擁有豐富的產業經驗。目前，他是紐約理工學院計算機科學的助理教授。他的背景結合了計算機科學、統計學和物理學。他的研究工作曾被多家新媒體報導，包括MIT技術評論、雅虎新聞、數位世界、FierceHealthcare和WTOP廣播電台。他的研究興趣包括大數據分析、統計學習、貝葉斯統計及其在計算機系統和智能醫療中的應用。

Yingying (Jennifer) Chen是羅格斯大學電氣與計算機工程系的教授及系主任，也是彼得·切拉西亞捐贈教授。她是無線信息網絡實驗室（WINLAB）的副主任，並領導數據分析與信息安全（DAISY）實驗室。她是計算機協會（ACM）院士、電氣與電子工程師學會（IEEE）院士以及國家發明家學院（NAI）院士。她的研究興趣包括移動計算與感測中的應用機器學習、物聯網（IoT）、AI/ML系統的安全性、智能醫療以及移動系統上的深度學習。她在RF/WiFi感測、定位系統和移動安全方面是先驅。在加入羅格斯大學之前，她是史蒂文斯理工學院的終身教授，並在多個行業擁有豐富的經驗。

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