DOI QR코드

DOI QR Code

Pedestrian Walking Velocity Estimation based on Wearable Inertial Sensors and Lower-limb Kinematics

착용형 관성센서 및 인체 하지부 기구학 기반의 보행자 속도추정에 관한 연구

  • Received : 2016.03.17
  • Accepted : 2017.05.26
  • Published : 2017.09.01

Abstract

In this paper, a new method is proposed for estimating pedestrians' walking velocity based on lower-limb kinematics and wearable inertial measurement unit (IMU) sensors. While the soles and ground are not in contact during the walking cycle, the walking velocity can be estimated by integrating the acceleration output of the inertial sensor mounted on the pelvis. To minimize the effects of acceleration measurement errors caused by the tilt of the pelvis while walking, the estimated walking velocity based on lower-limb kinematics is imposed as the initial value in the acceleration signal integration process of the pelvis inertial sensor. In the experiment involving outdoor walking for six minutes, sensor drift due to error accumulation was not observed, and the RMS error in the walking velocity estimation was less than 0.08 m/s.

본 논문은 하지부의 각 분절에 부착된 착용형 관성센서의 자세 및 각속도 정보와 하지부 기구학을 기반으로 착용자의 보행속도를 추정하는 방법에 관한 연구를 다룬다. 보행 주기 중 발바닥과 지면이 완전히 접촉되지 않는 구간에서는 골반부에 장착된 관성센서의 가속도 출력을 적분하여 보행속도를 추정할 수 있다. 이 때, 보행 시 골반부의 기울어짐으로 인하여 발생되는 가속도의 측정오차의 누적 영향을 최소화하기 위하여, 하지부 기구학을 기반으로 추정된 보행속도를 매 보행 주기마다 골반 관성센서의 가속도 출력신호 적분 초기값으로 갱신한다. 그 결과 6분 가량의 야외 보행 실험을 수행한 결과, 오차 누적에 의한 영향은 관찰되지 않았으며, 보행속도 추정 오차의 RMS는 0.08m/s 이하인 것으로 확인되었다.

Keywords

References

  1. Hong, I. Y., 2009, "New Hope for Location-based Services, WPS: From Car Navigation to Pedestrian Navigation," SW Insight Policy Report, 46, pp. 36-52.
  2. Nam, Y., Choi, Y.-J. and Cho, W.-D., 2010, "Human Activity Recognition using an Image Sensor and a 3-axis Accelerometer Sensor," korean society for internet information, Vol. 11, No. 1, pp. 129-141.
  3. Craig, J. J., 1986, Introduction to Robotics: Mechanics and Control Addison-Wesley. Reading, Mass.
  4. Britting, K. R., 2010, Inertial navigation systems analysis.
  5. Cho, J. B. and Lee, J. S., 2001, A Study on the Stand-alone Inertial Navigation System with low-cost Inertial Sensors.
  6. Yuan, Q., Chen, I. M. and Lee, S. P., 2011, May, SLAC: 3D Localization of Human based on Kinetic Human Movement Capture. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pp. 848-853, IEEE.
  7. Lee, J. and Kim, H., 2012, "A Study of High Precision Position Estimator Using GPS/INS Sensor Fusion," Journal of the Institute of Electronics Engineers of Korea, Vol. 49, No. 11, pp. 159-166.
  8. Yeu, B. M. and Joo, H. S., 2000, "A Study on the Development of Accuracy using a Cheap GPS Receiver in Car Navigation System," Journal of the Korean Society of Civil Engineers D, Vol. 20 No. 2D, pp. 227-240.
  9. Angerer, C. and Rupp, M., 2009, Advanced Synchronisation and Decoding in RFID Reader Receivers. In 2009 IEEE Radio and Wireless Symposium, pp. 59-62, IEEE.
  10. Kim, S. P. and Kim, N., 2012, Point-based Location Estimation using WiFi Signal. Journal of KIISE, 10(1).
  11. Kaplan, E. and Hegarty, C., 2005, Understanding GPS: principles and applications. Artech House.
  12. http://www.lockheedmartin.com/
  13. Hollerer, T. and Feiner, S., 2004, Mobile Augmented Reality. Telegeoinformatics: Location-Based Computing and Services. Taylor and Francis Books Ltd., London, UK, 21.
  14. Kim, M. and Lee, D., 2016, Development of an IMU based Foot-ground Contact Detection (FGCD) Algorithm. Ergonomics, (just-accepted), pp. 1-23.
  15. Chen, I. M., Yang, G., Tan, C. T. and Yeo, S. H., 2001, Local POE Model for Robot Kinematic Calibration. Mechanism and Machine Theory, 36(11), pp. 1215-1239. https://doi.org/10.1016/S0094-114X(01)00048-9
  16. Yuan, Q. and Chen, I. M., 2012, Human Velocity and Dynamic Behavior Tracking Method for Inertial Capture System. Sensors and Actuators A: Physical, 183, pp. 123-131. https://doi.org/10.1016/j.sna.2012.06.003
  17. Lee, M. S., Ju, H., Song, J. W. and Park, C. G., 2015, Kinematic Model-based Pedestrian Dead Reckoning for Heading Correction and Lower Body Motion Tracking. Sensors, Vol. 15, No. 11, pp. 28129-28153. https://doi.org/10.3390/s151128129
  18. Kim, M. and Lee, D., 2016, Analysis of Lower-Limb Motion during Walking on Various Types of Terrain in Daily Life. Journal of the Ergonomics Society of Korea, 35(5).