AtHoCare: An Intelligent Elder Care at Home System

详细信息    查看全文

• 关键词：Healthcare ; Fall detection ; Intelligent system
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9745
• 期：1
• 页码：298-305
• 全文大小：1,289 KB
• 参考文献：1.Abbate, S., Avvenuti, M., Corsini, P., Light, J., Vecchio, A.: Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: a survey. In: Tan, Y.K. (ed.) Wireless Sensor Networks: Application-Centric Design. InTech, Rijeka (2010)
  2.Mubashir, M., Shao, L., Seed, L.: A survey on fall detection: principles and approaches. Neurocomput. 100, 144–152 (2013)CrossRef
  3.Bevilacqua, V., Nuzzolese, N., Barone, D., Pantaleo, M., Suma, M., D’Ambruoso, D., Volpe, A., Loconsole, C., Stroppa, F.: Fall detection in indoor environment with kinect sensor. In: Proceedings of 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 319–324 (2014)
  4.Stone, E.E., Skubic, M.: Fall detection in homes of older adults using the Microsoft Kinect. IEEE J. Biomed. Health Inform. 19, 290–301 (2015)CrossRef
  5.Miaou, S.-G., Sung, P.-H., Huang, C.-Y.: A Customized human fall detection system using omni-camera images and personal information. In: 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, D2H2, pp. 39–42 (2006)
  6.Wu, G.: Distinguishing fall activities from normal activities by velocity characteristics. J. Biomech. 33, 1497–1500 (2000)CrossRef
  7.Williams, A., Ganesan, D., Hanson, A.: Aging in place: fall detection and localization in a distributed smart camera network. In: Proceedings of the 15th International Conference on Multimedia (2007)
  8.Igual, R., Medrano, C., Plaza, I.: Challenges, issues and trends in fall detection systems. Biomed. Eng. OnLine. 12, 1–24 (2013)CrossRef
  9.Kinect (2016). https://​en.​wikipedia.​org/​w/​index.​php?​title=​Kinect&​oldid=​702574374
  10.de Leva, P.: Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters. J. Biomech. 29, 1223–1230 (1996)CrossRef
  11.Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, Hoboken (2009)CrossRef
  12.Li, N., Hou, Y., Huang, Z.: Implementation of a real-time fall detection algorithm based on body’s acceleration. J. Chin. Comput. Syst. 33(11), 2410–2413 (2012)
  
• 作者单位：Tao Xu (14) (15)
  Yun Zhou (16)
  Zhe Ma (14)
  
  14. School of Software and Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, 710072, Shaanxi, People’s Republic of China
  15. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 99 Yan Cheung Road, Xi’an, 710072, Shaanxi, People’s Republic of China
  16. School of Education, Shaanxi Normal University, 199 South Chang’an Road, Xi’an, 710062, Shaanxi, People’s Republic of China
  
• 丛书名：Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management
• ISBN：978-3-319-40247-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9745

文摘

In recent years, the shortage of nursing home and the demand from elders have made the balance inclined. Additionally, the increased numbers of elders per year have not deemed fit to wait for growth rate of nursing home. Therefore, more and more elders have to stay at home and live alone, which easily leads them to be in danger, especially when unexpected emergency occurring like falling. To investigate this issue, we have designed AtHoCare, an intelligent elder care at home system, which employs Microsoft depth camera sensor Kinect to detect fall and an intelligent sever to send alarms to nurses’ smart phones. In this way, medical staffs could easily monitor several elders at the same time, which greatly increases work efficiency. It is worth stressing that AtHoCare also proposes an algorithm of fall detection based on skeleton data of elders only. It protects elders’ privacy much more than other vision based algorithm of fall detection. Results from our preliminary lab-environment test showed that AtHoCare has a well-done performance on detection.