Gyroscope-based assessment of temporal gait parameters during treadmill walking and running

被引:53
作者
McGrath, Denise [1 ]
Greene, Barry R. [3 ,4 ]
O'Donovan, Karol J. [3 ,4 ]
Caulfield, Brian [1 ,2 ]
机构
[1] The TRIL Centre, University College Dublin, Dublin 4, Belfield
[2] School of Public Health, Physiotherapy and Population Science, Health Sciences, University College Dublin, Dublin 4, Belfield
[3] The TRIL Centre, Dublin
[4] Health Research and Innovation, Intel Labs, Leixlip, Co. Kildare
关键词
Adaptive algorithm; Gait events; Heel-strike; Inertial sensor; Stance time; Stride time; Swing time; Toe-off;
D O I
10.1007/s12283-012-0093-8
中图分类号
学科分类号
摘要
Wireless sensing solutions that provide accurate long-term monitoring of walking and running gait characteristics in a real-world environment would be an excellent tool for sport scientist researchers and practitioners. The purpose of this study was to compare the performance of a body-worn wireless gyroscope-based gait analysis application to a marker-based motion capture system for the detection of heel-strike and toe-off and subsequent calculation of gait parameters during walking and running. The gait application consists of a set of wireless inertial sensors and an adaptive algorithm for the calculation of temporal gait parameters. Five healthy subjects were asked to walk and run on a treadmill at two different walking speeds (2 and 4 kph) and at a jogging (8 kph) and running (12 kph) speed. Data were simultaneously acquired from both systems. True error, percentage error and ICC scores indicate that the adaptive algorithm successfully calculated strides times across all speeds. However, results showed poor to moderate agreement for stance and swing times. We conclude that this gait analysis platform is valid for determining stride times in both walking and running. This is a useful application, particularly in the sporting arena, where long-term monitoring of running gait characteristics outside of the laboratory is of interest. © 2012 International Sports Engineering Association.
引用
收藏
页码:207 / 213
页数:6
相关论文
共 27 条
[1]  
Aminian K., Trevisan C., Najafi B., Et al., Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement, Gait Posture, 20, 1, pp. 102-107, (2004)
[2]  
Burns A., Greene B.R., McGrath M.J., Et al., SHIMMER™-a wireless sensor platform for non-invasive biomedical research, IEEE Sens J, 10, pp. 1527-1534, (2010)
[3]  
Burns A., McGrath M.J., Delaney J., Et al., Open shareable research platform for developing interoperable personal health systems, 1st AMA-IEEE medical technology conference on individualized medicine, Washington, DC, (2010)
[4]  
Channells J., Purcell B., Barrett R., Et al., Determination of rotational kinematics of the lower leg during sprint running using accelerometers, BioMEMS and Nanotechnology II, Brisbane, (2005)
[5]  
Chung W.Y., Bhardwaj S., Purwar A., Et al., A fusion health monitoring using ECG and accelerometer sensors for elderly persons at home, International Conference of the IEEE EMBS, Lyon, (2007)
[6]  
Ferraris F., Grimaldi U., Parvis M., Procedure for effortless in-field calibration of three-axis rate gyros and accelerometers, Sens Mater, 7, 5, pp. 311-330, (1995)
[7]  
Franz J.R., Paylo K.W., Dicharry J., Et al., Changes in the coordination of hip and pelvis kinematics with mode of locomotion, Gait Posture, 29, 3, pp. 494-498, (2009)
[8]  
Greene B., McGrath D., O'Neill R., Et al., An adaptive gyroscope-based algorithm for temporal gait analysis, Med Biol Eng Comput, 48, 12, pp. 1251-1260, (2010)
[9]  
Greene B.R., O'Donovan A., Romero-Ortuno R., Et al., Quantitative falls risk assessment using the timed up and go test, IEEE Trans Biomed Eng, 57, pp. 2918-2926, (2010)
[10]  
Hay J.G., Reid J.G., Anatomy, Mechanics, and Human Motion, (1993)