Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

D. Taylor and C. Stretton, “The Otago Exercise Program, An Evidence-Based Approach to Falls Prevention for Older Adults Living in the Community,” Journal of Primary Health Care, Vol. 31, No. 6, 2004.

has been cited by the following article:

  • TITLE: Combination of the Kinect with Virtual Reality in Balance Training for the Elderly

    AUTHORS: Wei-Min Hsieh, Chih-Chen Chen, Shih-Chuan Wang, Yu-Luen Chen, Yuh-Shyan Hwang, Jin-Shin Lai

    KEYWORDS: Balance; Kinect; Fall; Virtual Reality

    JOURNAL NAME: Engineering, Vol.5 No.10B, October 30, 2013

    ABSTRACT: Daily life movements such as standing, walking, and jumping need balance ability to achieve. Good balance control is closely related to the body stability and its development. According to medical research, people showing dizziness after taking drugs may have their balance ability substantially affected, and are more likely to fall, especially true to the elderly. This study proposes the combination of Kinect with virtual reality to build an information platform of interactive scenarios, for practices and evaluation on balance ability. Based on the indicators of balance ability, this platform sets out various training activities to improve the balance ability, making the supposedly boring process fun and vivid for a much better training purposes. Also, according to literatures, training with gaming patterns results in 30% reduction of falls. What’s more, this type of training can have the data, like time of use, scores, and joint postures, recorded and sent through network to databases on remote computer servers. The data collected from this platform can be sorted and analyzed, and the results can then be used to evaluate the performance of the balance training, and referenced for follow-up planning in the future.