Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Gravity Removal and Vector Rotation Algorithm for Step counting using a 3-axis MEMS accelerometer

3축 MEMS 가속도 센서를 이용한 걸음 수 측정을 위한 중력 제거 및 백터 전환 알고리즘

  • Kim, Seung-Young (Dept. of Computer Information and Science, Inha University) ;
  • Kwon, Gu-In (Dept. of Computer Information and Science, Inha University)
  • 김승영 (인하대학교 컴퓨터정보공학과) ;
  • 권구인 (인하대학교 컴퓨터정보공학과)
  • Received : 2014.01.09
  • Accepted : 2014.04.15
  • Published : 2014.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose Gravity Removal and Vector Rotation algorithm for counting steps of wearable device, and we evaluated the proposed GRVR algorithm with Micro-Electro-Mechanical (MEMS) 3-axis accelerometer equipped in low-power wearable device while the device is mounted on various positions of a walking or running person. By applying low-pass filter, the gravity elements are canceled from acceleration on each axis of yaw, pitch and roll. In addition to DC-bias removal and the low-pass filtering, the proposed GRVR calculates acceleration only on the yaw-axis while a person is walking or running thus we count the step even if the wearable device's axis are rotated during walking or running. The experimental result shows 99.4% accuracies for the cases where the wearable device is mounted in the middle and on the right of the belt, and 91.1% accuracy which is more accurate than 83% of commercial 3-axis pedometer when worn on wrist for the case of axis-rotation.

최근, 다양한 형태의 웨어러블 컴퓨팅 디바이스와 이에 따른 응용 프로그램이 개발되고 있으며, 이들 중에서 헬스 케어의 한 영역으로 웨어러블 컴퓨팅 디바이스를 이용하여 개인의 운동량은 측정하는 다양한 연구가 진행되고 있다. 본 논문에서는 가장 기초적인 운동인 걸음걸이 측정 알고리즘으로 중력 제거 및 백터 회전(Gravity Removal and Vector Rotation) 알고리즘을 제안하고, 이를 위한 실험으로 보행 또는 주행 중인 개인의 다양한 위치에 부착한 웨어러블 디바이스에 장착된 3축 MEMS 가속도 센서로부터 획득된 가속도 값을GRVR 알고리즘을 이용하여 걸음 수를 측정한다. GRVR은 가속도 센서로 획득된 3축 가속도 값으로 부터 중력에 의한 가속도 분은 저대역 필터를 이용하여 제거 하고, 이 후 각각의 가속도 값에서 직류 성분 제거 및 센서 축 회전 보상인 GRVR 알고리즘으로 보행 혹은 주행 중에 순수하게 걸음걸이에 의하여 발생하는 가속도 변화분 만을 추출한다. 실험 결과로 웨어러블 디바이스를 개인의 허리 중앙 혹은 우측에 부착한 경우 GRVR 알고리즘을 이용한 걸음 수 측정은 99.4%의 정확도, 또한 손목에 부착한 경우 상용 3축 가속도 만보계의 83%보다 정확한 91.1%의 정확도를 확인하였다.

Keywords

References

  1. D. M. Bravata, C. S. Spangler, Using pedometers to increase physical activity and improve health, Journal of the American Medical Association, Vol. 298. No. 19, Nov. 2007.
  2. ACSM metabolic equations, http://blue.utb.edu/mbailey/handouts/pdf/MetCalnew.pdf
  3. Y. Kawahara, N. Ryu, T. Asami, Monitoring daily energy expenditure using 3-axis accelerometer with a low-power microprocessor, e-minds, Vol. 1, No. 5,2009(www.eminds.hci-rg.com).
  4. B. E. Ainsworth, W. L. Haskell, M. C. Whitt, M. L. Irwin, A. M. Swartz, S. J. Strath, W. L. O'Brien, D. R. Bassett, Jr, K. H. Schmitz, P. O. Emplaincourt, D. R. Jacobs, Jr, and A. S. Leon, Compendium of physical activities: an update of activity codes and MET intensities, Medicine and Science in Sports and Exercise Vol. 32 S498-S516, 2000. https://doi.org/10.1097/00005768-200009001-00009
  5. E. L. Melanson, J. R. Troll, M. L. Bell, W. T. Donahoo, J. O. Hill, L. J. Ynjje, L. Lennigham-Foster, J. C. Beters, and J. S. Levinre, Commercially available pedometers: considerations for accurate step counting, PREVENTIVE MEDICINE Vol. 39, 2004.
  6. T.M. Ahola, Pedometer for running activity using accelerometer sensors on the wrist, MEDICAL EQUIPMENT INSIGHTS Vol. 3, 2010.
  7. A. L. G Meijer, K. R. Westerterp, F. M. H. Verhoeven, H. B. M. Koper, and F. ten Hoor, Methods to assess physical activity with special reference to motion sensors and accelerometers, IEEE TRACTIONS OF ON BIOMEDICAL ENGINEERING, Vol. 38, No. 3, Mar. 1991.
  8. J. W. Kim, H. J. Jang, D. H. Hwang, and C. S. Park, A step, stride and heading determination for the pedestrian navigation system, JOURNAL OF GLOBAL POSITIONING SYSTEMS, Vol. 3, No. 1-2, pp273-279, 2004. https://doi.org/10.5081/jgps.3.1.273
  9. N. Zhao, Full-featured pedometer design realized with 3-axis digital accelerometer, ANALOG DIALOGUE 44-06, June 2010.
  10. C. G. Ryan, P. M. Grant, W. W. Tigbe, and M. H. Granat. Br J, The validity and reliability of a novel activity monitor as a measure of walking, British Journal of Sports Medicine, Vol. 40, pp. 779-784, 2006. https://doi.org/10.1136/bjsm.2006.027276
  11. Texas Instruments, eZ430-ChronosTM Development Tool User's Guide, Dec. 2010.
  12. A. R. Jimenez, F. Seco, C. Prieto, and J. Guevara., A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU, 6TH IEEE INTERNATIONAL SYMPOSIUM OF INTELLIGENT SIGNAL PROCESSING, WSIP 2009 26-28 August, 2009.
  13. A. Milenkovic, C. Otto, and E. Jovanov, Wireless sensor networks for personal health monitoring: issues and an Implementation.
  14. A. AKAHORI, Y. KISHIMOTO and K. Oguri, Estimate activity for M-health using one three-axis accelerometer, 3RD IEEE-EMBS, INTERNATIONAL SUMMER SCHOOL. IEEE-EMBS, MIT, Sept. 4-6, 2006.
  15. P. L. Schneider, S. E. Crouter, O. Lukajic and D. R. Bassett, Jr, Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk, MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, Vol. 3, No. 10, pp. 1779-84, Oct. 2003.
  16. S. Y. Cho, C. G. Park, and J. G. Lee, A personal navigation system using low-cost MEMS/GPS/Fluxgate, ION 59TH ANNUAL MEETING /CIGTF 22ND GUIDENCE TEST SYMPOSIUM, Albuquerque, NM, 23-25 June 2003.
  17. P. L. Schneider, S. E. Crouter, and D. R. Basset, Jr, Pedometer measures of free-living physical activity: comparison of 13 Models, MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, Vol. 36, No. 2, pp.331-5, 2004. https://doi.org/10.1249/01.MSS.0000113486.60548.E9
  18. CMA3000-D01 3-axis ultra low power accelerometer with digital SPI and I2C interface, VTI Technologies.

Cited by

  1. User Identification from Gait Analysis Using Multi-Modal Sensors in Smart Insole vol.19, pp.17, 2014, https://doi.org/10.3390/s19173785
  2. Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns vol.20, pp.21, 2014, https://doi.org/10.3390/s20216253
  3. Comparison of Step Counting Methods according to the Internal Material Molding Methods for the Module of a Smart Shoe vol.13, pp.1, 2014, https://doi.org/10.7236/ijibc.2021.13.1.90