Williamson, R. and Andrews, B.J. (2001) Detecting Absolute Human Knee Angle and Angular Velocity Using Accelerometers and Rate Gyroscopes. Medical Biological Engineering Computing, 39, 1-9. - References

Browse Menu >>

Browse Subjects >>

Computer Science & Communications

Earth & Environmental Sciences

Social Sciences & Humanities

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 >>

customer@scirp.org

+86 18163351462(WhatsApp)

1655362766

Paper Publishing WeChat

Book Publishing WeChat

(or Email:book@scirp.org)

Article citations
More>>

Williamson, R. and Andrews, B.J. (2001) Detecting Absolute Human Knee Angle and Angular Velocity Using Accelerometers and Rate Gyroscopes. Medical Biological Engineering Computing, 39, 1-9.
http://dx.doi.org/10.1007/BF02345283

has been cited by the following article:

TITLE: Human Physical Activity Measurement Method Based on Electrostatic Induction

AUTHORS: Koichi Kurita

KEYWORDS: Human Walking, Electrostatic Induction, Human Physical Activity, Walking with a Limp

JOURNAL NAME: Journal of Sensor Technology, Vol.4 No.3, September 1, 2014

ABSTRACT: In this study, an effective noncontact and nonattached technique that is based on electrostatic induction current generated during walking motion is proposed for the detection and assessment of human physical activity. In addition, a theoretical model is proposed for the electrostatic induction current generated owing to variation in the electric potential of the human body. The proposed electrostatic induction current model is compared with the theoretical model, and the proposed model is shown to effectively explain the behavior of the electrostatic induction current waveform. The normal walking motions of daily living are recorded with a portable sensor located in a regular house. The obtained results show that detailed information of physical activity such as a gait cycle can be estimated using our proposed technique. Additionally, the walking signal was measured when the subject walked with the ankle and knee fastened to a splint with bandages to simulate a limp. Therefore, the proposed technique, which is based on the detection of signal generated during walking, can be successfully employed to assess human physical activity.