Abstract
Gait Analysis involves analyzing human walking, particularly its temporal parameters. Gait parameters (cadence, step length, trajectory of center of mass, etc) require measurements oflower-limb joint angles. The most commonly used techniques for gait analysis are optical systems. However, these systems have several drawbacks which include: markers need to be attached to the human body; it is required a considerable amount of time for setting up the experimental session; High cost; Bulk and space requirements; finally, they are restricted to a limited place or laboratory. Inertial measurement units (IMU) can be used in body-worn biomechanical monitoring devices for movement data collection in daily life.This paper presents a system based on 2 IMUs for knee joint angle monitoring to be used for long-time periods in out-of-lab daily life. For validation, the system was compared to a vision-based motion capture system, in a set of experiments involving walking on a treadmill under three different velocities (slow-speed, normal-speed, high-speed). Obtained results showed high correlation (>0.94) between measurements from the developed device and the vision-based motion capture system, according to the obtained concordance correlation coefficients.
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References
Tamura T (2014) Chapter: Wearable Inertial Sensors and Their Applications. In the book Wearable Sensors: Fundamentals, Implementation and Applications, edited by Edward Sazonov, Michael R Neuman, Elsevier
Bonato P (2005) Advances in wearable technology and applications in physical medicine and rehabilitation. Journal of NeuroEngineering and Rehabilitation 2(2):1-4
Tik-Pui Fong D,Chan Y (2010) The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review. Sensors 10(12): 11556–11565
Troje NF (2002) Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. Journal of Vision 2(5):371-387
Mündermann L, Corazza S, Andriacchi TP (2006) The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. Journal of NeuroEngineering and Rehabilitation 3(6):1-11
Kirk AG, O’Brien JF, Forsyth DA (2005) Skeletal parameter estimation from optical motion capture data. Proc. vol. 2, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 782-788
Kurihara K, Hoshino SI, Yamane K, Nakamura Y (2002) Optical motion capture system with pan-tilt camera tracking and realtime data processing. Proc. IEEE Int. Conference on Robotics and Automation, Washington, USA, 2002, pp. 1241-1248
Pribani T, Peharec S, Medved V (2009) A comparison between 2d plate calibration and wand calibration for 3d kinematic systems. Kinesiology 41(2):147-155
Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45: 255-268
McBride GB (2005) A proposal for strength-of-agreement criteria for Lin’s Concordance Correlation Coefficient. NIWA Client Report: HAM2005-062
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Castañeda, J.J., Ruiz-Olaya, A.F., Lara-Herrera, C.N., Roldán, F.Z. (2017). Knee Joint Angle Monitoring System Based on Inertial Measurement Units for Human Gait Analysis. In: Torres, I., Bustamante, J., Sierra, D. (eds) VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016. IFMBE Proceedings, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-10-4086-3_173
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DOI: https://doi.org/10.1007/978-981-10-4086-3_173
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