Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Detection of Rotations in Jump Rope using Complementary Filter

상보필터를 이용한 줄넘기 회전운동 검출

  • Received : 2016.10.07
  • Accepted : 2016.10.18
  • Published : 2017.01.31
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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.

줄넘기와 같은 반복적인 운동들의 횟수를 측정하는 방법은 다양하다. 그 중 대표적으로 가속도 센서의 가속도 값 또는 자이로스코프 센서의 각속도 값을 이용하여 파형과 데이터의 특징을 추출하고 선택한 후 선택한 특징을 알고리즘에 적용하여 측정하는 방법이 있다. 하지만 고정되지 않고 유동적인 운동들은 다양한 변수가 존재한다. 이러한 경우의 수를 하나의 센서만으로 찾기 쉽지 않으며, 잡음과 진동에 취약한 가속도계와 드리프트 현상이 발생하는 각속도의 문제점으로 인하여 정확한 줄넘기 개수를 세는데 다소 정확도가 떨어지는 현상이 발생한다. 본 논문에서는 기존의 방식인 단일 센서만의 값으로 회전운동을 검출하는 방법의 문제점을 개선하기 위해 가속도와 각속도의 데이터값에 상보 필터를 적용하고, 가속도와 각속도 값이 상호보완 하여 서로의 문제점을 최소화하여 보다 정확한 개수를 측정할 수 있는 방법을 제안한다. 제안하는 방법은 센서 값의 특징만을 보고 판단하는 방법과 비교하여 정확하게 줄넘기 개수를 측정하는 것을 실험 결과를 통해 확인할 수 있다.

Keywords

References

  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. https://doi.org/10.1007/BF02345966
  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. https://doi.org/10.1109/78.157221
  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. https://doi.org/10.5302/J.ICROS.2008.14.9.881
  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.

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