Electromyography-based locomotion pattern recognition and personal positioning toward improved context-awareness applications

IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans

Volumn 43, Issue 5, 2013, Pages 1216-1227

  Wang, Qian ^a   Chen, Xiang ^a   Chen, Ruizhi ^b   Chen, Yuwei ^c   Zhang, Xu ^d  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b TEXAS A AND M UNIVERSITY CORPUS CHRISTI   (United States)

^c FINNISH GEODETIC INSTITUTE   (Finland)

^d REHABILITATION INSTITUTE OF CHICAGO   (United States)

Author keywords

Electromyography (EMG); Locomotion pattern recognition; Pedestrian dead reckoning (PDR); Wearable sensors

Indexed keywords

CONTEXT INFORMATION; CONTEXT-AWARENESS; LOCOMOTION PATTERNS; MUSCLE ACTIVITIES; PEDESTRIAN DEAD RECKONING (PDR); POSITIONING ACCURACY; POSITIONING TECHNOLOGIES; WEARABLE SENSORS; CONTEXT- AWARENESS; ELECTROMYOGRAPHY; IN CONTEXTS; LOCOMOTION PATTERN RECOGNITION; PEDESTRIAN DEAD RECKONING; PEDESTRIAN DEAD RECKONINGS; STRIDE LENGTH; SYSTEM SIGNALS;

ELECTROMYOGRAPHY; GLOBAL POSITIONING SYSTEM; NAVIGATION; PATTERN RECOGNITION; SENSORS; SIGNAL DETECTION; WALKING AIDS; WEARABLE SENSORS;

BODY SENSOR NETWORKS; ELECTROMYOGRAPHY;

EID: 84887027673     PISSN: 10834427     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMC.2013.2256857     Document Type: Article

References (41)

1. H. W. Gellersen, A. Schmidt, and M. Beigl, Multisensor contextawareness in mobile devices and smart artifacts, Mobile Netw. Appl., vol. 7, pp. 341-351, Oct. 2002.
2. A. Salmeri, C. A. Licciardi, L. Lamorte, M. Valla, R. Giannantonio, and M. Sgroi, An architecture to combine context awareness and body sensor networks for health care applications, in Proc. Ambient Assistive Health Wellness Manage. Heart City, vol. 5597. 2009, pp. 90-97.
3. N. Bricon-Souf and C. R. Newman, Context awareness in health care: A review, Int. J. Med. Informatics, vol. 76, no. 1, pp. 2-12, Jan. 2007.
4. S. Lee, J. Chang, and S. G. Lee, Survey and trend analysis of contextaware systems, Inform. Int. Interdisciplinary J., vol. 14, no. 2, pp. 527-547, Feb. 2011.
5. Y. Ren, R. W. N. Pazzi, and A. Boukerche, Monitoring patients via a secure and mobile healthcare system, Wireless Commun. IEEE, vol. 17, no. 2, pp. 59-65, Feb. 2010.
6. S. Mazuelas, A. Bahillo, R. M. Lorenzo, P. Fernandez, F. A. Lago, E. Garcia, J. Blas, and E. J. Abril, Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks, IEEE J. Select. Topics Signal Process., vol. 3, no. 5, pp. 821-831, Oct. 2009.
7. A. D. Koutsou, F. Seco, A. R. Jimenez, J. O. Roa, J. L. Ealo, C. Prieto, and J. Guevara, Preliminary localization results with an RFID based indoor guiding system, in Proc. IEEE Int. Symp. Intell. Signal Process. Conf. Proc. Book, 2007, pp. 917-922.
8. T. Ishikawa, M. Kourogi, T. Okuma, and T. Kurata, Economic and synergistic pedestrian tracking system for indoor environments, in Proc. Int. Conf. Soft Comput. Pattern Recognit., 2009, pp. 522-527.
9. D. Grejner-Brzezinska, C. Toth, S. Moafipoor, and J. Kwon, Design and calibration of a neural network-based adaptive knowledge system for multi-sensor personal navigation, in Proc. 5th Int. Symp. Mobil Mapping Technol. MMT?07, Padua.
10. C. Toth, D. A. Grejner-Brzezinska, and S. Moafipoor, Pedestrian tracking and navigation using neural networks and fuzzy logic, in Proc. IEEE Int. Symp. Intell. Signal Process. Conf. Proc. Book, 2007, pp. 657-662.
11. Q. Ladetto, On foot navigation: Continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering, in Proc. 13th Int. Tech. Meeting Satellite Division Institute Navigat. (ION GPS), Salt Lake City, UT, USA, Sep. 2000, pp. 1735-1740.
12. M. Kourogi, T. Ishikawa, and T. Kurata, A method of pedestrian dead reckoning using action recognition, in Proc. IEEE-Ion Position Location Navigat. Symp. Plans, 2010, pp. 642-646.
13. Z. Sun, X. Mao, W. Tian, and X. Zhang, Activity classification and dead reckoning for pedestrian navigation with wearable sensors, Meas. Sci. Technol., vol. 20, no. 1, pp. 1-10, 2009.
14. T. B. Moeslund, A. Hilton, and V. Kruger, A survey of advances in vision-based human motion capture and analysis, Comput. Vis. Image Understanding, vol. 104, no. 2, pp. 90-126, 2006. (Pubitemid 44573873)
15. R. Poppe, Vision-based human motion analysis: An overview, Comput. Vis. Image Understanding, vol. 108, no. 1, pp. 4-18, 2007. (Pubitemid 47333391)
16. S. Park, I. Locher, A. Savvides, M. B. Srivastava, A. Chen, R. Muntz, and S. Yuen, Design of a wearable sensor badge for Smart Kindergarten, in Proc. 6th Int. Symp. Wearable Comput., 2002, pp. 231-238.
17. A. Bulling, D. Roggen, and G. Troster, Wearable EOG goggles: Seamless sensing and context-awareness in everyday environments, J. Ambient Intell. Smart Environ., vol. 1, no. 2, pp. 157-171, 2009.
18. L. Wang, T. Gu, X. P. Tao, and J. Lu, A hierarchical approach to realtime activity recognition in body sensor networks, Pervasive Mobile Comput., vol. 8, no. 1, pp. 115-130, Feb. 2012.
19. L. Atallah, B. Lo, R. King, and G. Z. Yang, Sensor positioning for activity recognition using wearable accelerometers, IEEE Trans. Biomed. Circuits Syst., vol. 5, no. 4, pp. 320-329, Aug. 2011.
20. C. Zhu and W. H. Sheng, Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living, IEEE Trans. Syst., Man, Cybern., A Syst. Humans, vol. 41, no. 3, pp. 569-573, May 2011.
21. K. Altun and B. Barshan, Human activity recognition using inertial/magnetic sensor units, Human Behavior Understanding, vol. 6219, pp. 38-51, 2010.
22. C. Frigo and P. Crenna, Multichannel SEMG in clinical gait analysis: A review and state-of-the-art, Clin. Biomech., vol. 24, no. 3, pp. 236-245, 2009.
23. P. Zhou, M. M. Lowery, J. P. A. Dewald, and T. A. Kuiken, Toward improved myoelectric prosthesis control: High density surface EMG recording after targeted muscle reinnervation, in Proc. 27th Annu. Int. Conf. IEEE-EMBS, 2005, pp. 4064-4067. (Pubitemid 46229829)
24. X. Zhang and P. Zhou, High density myoelectric pattern recognition toward improved stroke rehabilitation, IEEE Trans. Biomed. Eng., vol. 59, no. 6, pp. 1649-1657, Jun. 2012.
25. Z. O. Khokhar, Z. G. Xiao, and C. Menon, Surface EMG pattern recognition for real-time control of a wrist exoskeleton, Biomed. Eng. Online, vol. 9, no. 1, Aug. 2010.
26. H. Tamura, T. Gotoh, D. Okumura, H. Tanaka, and K. Tanno, A study on the S-Emg pattern recognition using neural network, Int. J. Innovative Comput. Inform. Control, vol. 5, no. 12, pp. 4877-4884, Dec. 2009.
27. Y. H. Liu and H. P. Huang, Toward a high-stability EMG recognition system for prosthesis control: A one-class classification based non-target EMG pattern filtering scheme, in Proc. IEEE Int. Conf. SMC, vols. 1-9. 2009, pp. 4752-4757.
28. X. Zhang, X. Chen, Y. Li, V. Lantz, K. Q. Wang, and J. H. Yang, A framework for hand gesture recognition based on accelerometer and EMG sensors, IEEE Trans. Syst., Man, Cybern., A Syst. Humans, vol. 41, no. 6, pp. 1064-1076, Nov. 2011.
29. M. Asghari Oskoei and H. Hu, Myoelectric control systems: A survey, Biomed. Signal Process. Control, vol. 2, no. 4, pp. 275-294, 2007.
30. Q. Wang, X. Zhang, X. Chen, R. Z. Chen, W. Chen, and Y. W. Chen, A novel pedestrian dead reckoning algorithm using wearable EMG sensors to measure walking strides, in Proc. UPINLBS, Helsinki, Finland, Oct. 2010.
31. W. Chen, R. Z. Chen, X. A. Chen, X. Zhang, Y.W. Chen, J. Y.Wang, and Z. Q. Fu, Comparison of EMG-based and accelerometer-based speed estimation methods in pedestrian dead reckoning, J. Navigat., vol. 64, no. 2, pp. 265-280, 2011.
32. W. Chen and X. Zhang, A novel EMG-based stride length estimation method for pedestrian dead reckoning, in Proc. 23rd Int. Tech. Meeting Satell. Div. Inst. Navigat., Portland, OR, USA, Sep. 2010, pp. 569-576.
33. R. Z. Chen, W. Chen, X. A. Chen, X. Zhang, and Y. W. Chen, Sensing strides using EMG signal for pedestrian navigation, GPS Solutions, vol. 15, no. 2, pp. 161-170, 2011.
34. Y. Chen, R. Chen, X. Chen, W. Chen, and Q. Wang, Wearable electromyography sensor based outdoor-indoor seamless pedestrian navigation using motion recognition method, in Proc. Int. Conf. Indoor Positioning Indoor Navigat., Guimarães, Portugal, 2011, pp. 1-9.
35. K. Abdulrahim, C. Hide, T. Moore, and C. Hill, Integrating low cost IMU with building heading in indoor pedestrian navigation, J. Global Positioning Syst., vol. 10, no. 1, pp. 30-38, 2011.
36. J. W. Kim, H. J. Jang, D.-H. Hwang, and C. Park, A step, stride and heading determination for the pedestrian navigation system, J. Global Positioning Syst., vol. 3, nos. 1-2, pp. 273-279, 2004.
37. P. Xu, G. N. Brock, and R. S. Parrish, Modified linear discriminant analysis approaches for classification of high-dimensional microarray data, Comput. Statist. Data Anal., vol. 53, no. 5, pp. 1674-1687, 2009.
38. M. Kourogi, T. Ishikawa, and T. Kurata, Method of Pedestrian Dead Reckoning Using Action Recognition, in Proc. IEEE/ION, PLANS, 2010, pp. 85-89.
39. H. Huang, T. A. Kuiken, and R. D. Lipschutz, A strategy for identifying locomotion modes using surface electromyography, IEEE Trans. Biomed. Eng., vol. 56, no. 1, pp. 65-73, Jan. 2009.
40. M. Kourogi, T. Ishikawa, Y. Kameda, J. Ishikawa, K. Aoki, and T. Kurata, Pedestrian dead reckoning and its applications, in Proc. ISMAR Workshop: Let's Go Out: Res. Outdoor Mixed Augmented Reality (ISMAR), Orlando, USA, vol. 9, 2009.
41. Y. Chen, R. Chen, L. Pei, T. Kroger, H. Kuusniemi, J. Liu, and W. Chen, Knowledge-based error detection and correction method of a multi-sensor multi-network positioning platform for pedestrian indoor navigation, in Proc. IEEE/ION Position Location Navigat. Symp., 2010, pp. 873-879.