Browse > Article
http://dx.doi.org/10.6109/jkiice.2017.21.1.8

Detection of Rotations in Jump Rope using Complementary Filter  

Yoo, Byeong-Hyeon (Department of Electronic Engineering, Dong-eui University)
Heo, Gyeongyong (Department of Electronic Engineering, Dong-eui University)
Abstract
There are various methods to count the number of repetitive motions such as jump rope. Most of the methods use features extracted from the time-varying waves of acceleration or angular velocity, which is the main feature in the count of rotations in jump rope. However, there exist several variables and it is not easy to find the count with a single sensor. For example, accelerometer is susceptible to noise and vibration, and the angular velocity may cause a drift phenomenon, which is the main cause of the inaccurate count of jump rope rotation. In this paper, complementary filter is used to consider two sensors simultaneously and complement each other, which results in more accurate count in jump rope rotation. The proposed method can count the exact number of jump rope rotation compared to other existing methods only using one sensor value, which is confirmed through experimental results.
Keywords
Acceleration; Angular velocity; Complementary filter; Rotation motion;
Citations & Related Records
연도 인용수 순위
  • Reference
1 E. S. Choi, W. C. Bang, S. J. Cho, J. Yang, D. Y. Kim, and S. R. Kim, "Beatbox music phone: gesture-based interactive mobile phone using a tri-axis accelerometer," in Proceedings of the IEEE International Conference on Industrial Technology, pp. 97-102, 2005.
2 T. Iso, and K. Yamazaki, "Gait analyzer based on a cell phone with a single three-axis accelerometer," in Proceedings of the 8th conference on Human-computer interaction with mobile devices and services, pp. 141-144, 2006.
3 B. J. Mortazavi, M. Pourhomayoun, G. Alsheikh, N.Alshurafa, S. I. Lee, and M. Sarrafzadeh, "Determining the single best axis for exercise repetition recognition and counting on smartwatches," in Proceedings of the 11th International Conference on Wearable and Implantable Body Sensor Networks, pp. 33-38, 2014.
4 J. Masino, B. Daubner, M. Frey, and F. Gauterin, "Development of a tire cavity sound measurement system for the application of field operational tests," in Proceedings of the Systems Conference (SysCon), 2016 Annual IEEE, pp. 1-5, 2016.
5 H. J. Luinge, and P. H. Veltink, "Measuring orientation of human body segments using miniature gyroscope and accelerometers," Medical and Biological Engineering & Computing, vol. 43, no. 2, pp. 273-282, Apr. 2005.   DOI
6 J. Parkka, M. Ermes, K. Antila, M. van Gils, A. Manttari, and H. Nieminen, "Estimating intensity of physical activity: a comparison of wearable accelerometer and gyro sensors and 3 sensor locations," in Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1511-1514, 2007.
7 M. Vetterli, and C. Herley, "Wavelets and filter banks: Theory and design," IEEE transactions on signal processing, vol. 40, no. 9, pp. 2207-2232, Sep. 1992.   DOI
8 C. Zhu, and W. Sheng, "Wearable sensor-based hand gesture anddaily activity recognition for robot-assisted living," IEEE Transactions on Systems, vol. 41, no. 3, pp. 569-573, May 2011.
9 C. W. Kang, Y. M. Yoo, and C. G. Park, "Performance improvement of attitude estimation using modified Euler angle based Kalman filter," Journal of Institute of control, Robotics and Systems, vol. 14, no. 9, pp. 881-885, 2008.   DOI
10 R. G. Brown, and P. Y. Hwang, "Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions," Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions, New York: Wiley, 1997.