Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications

Sensors - Tập 14 Số 2 - Trang 3362-3394
Alvaro Muro-de-la-Herran1, Begonya García-Zapirain1, Amaia Méndez Zorrilla1
1DeustoTech-Life Unit, DeustoTech Institute of Technology, University of Deusto, Bilbao 48007, Spain.

Tóm tắt

This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis.

Từ khóa


Tài liệu tham khảo

Osman, 2008, Emerging Trends of Body-Mounted Sensors in Sports and Human Gait Analysis, 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, Volume 21, 715, 10.1007/978-3-540-69139-6_178

Logerstedt, 2013, Gait patterns differ between ACL-reconstructed athletes who pass return-to-sport criteria and those who fail, Am. J. Sports Med., 41, 1310, 10.1177/0363546513482718

Lee, 2013, The use of the dual-task paradigm in detecting gait performance deficits following a sports-related concussion: A systematic review and meta-analysis, J. Sci. Med. Sport, 16, 2, 10.1016/j.jsams.2012.03.013

Fathima, S.M.H.S.S., and Banu, R.S.D.W. (2012, January 21–22). Human Gait Recognition Based on Motion Analysis Including Ankle to Foot Angle Measurement. Nagercoil, India.

Wang, 2003, Silhouette analysis-based gait recognition for human identification, IEEE Trans. Pattern Anal. Mach. Intell., 25, 1505, 10.1109/TPAMI.2003.1251144

Han, 2006, Individual recognition using Gait Energy Image, IEEE Trans. Pattern Anal. Mach. Intell., 28, 316, 10.1109/TPAMI.2006.38

Derawi, M.O., Bours, P., and Holien, K. (2010, January 15–17). Improved Cycle Detection for Accelerometer Based Gait Authentication. Darmstadt, Germany.

Sutherland, 2001, The evolution of clinical gait analysis part I: Kinesiological EMG, Gait Posture, 14, 61, 10.1016/S0966-6362(01)00100-X

Sutherland, 2002, The evolution of clinical gait analysis. Part II kinematics, Gait Posture, 16, 159, 10.1016/S0966-6362(02)00004-8

Sutherland, 2005, The evolution of clinical gait analysis part III—kinetics and energy assessment, Gait Posture, 21, 447, 10.1016/j.gaitpost.2004.07.008

Gomatam, A.N.M., and Sasi, S. (2004). Multimodal gait recognition based on stereo vision and 3D template matching. CISST, 405–410.

White, 1992, Predicting muscle forces in gait from EMG signals and musculotendon kinematics, J. Electromyogr. Kinesiol., 2, 217, 10.1016/1050-6411(92)90025-E

Mummolo, 2013, Quantifying dynamic characteristics of human walking for comprehensive gait cycle, J. Biomech. Eng., 135, 091006, 10.1115/1.4024755

Kerrigan, 1998, Biomechanical gait alterations independent of speed in the healthy elderly: Evidence for specific limiting impairments, Arch. Phys. Med. Rehabil., 79, 317, 10.1016/S0003-9993(98)90013-2

Stolze, 2002, Typical features of cerebellar ataxic gait, J. Neurol. Neurosurg. Psychiatry, 73, 310, 10.1136/jnnp.73.3.310

Gehlsen, 1986, Gait characteristics in multiple sclerosis: progressive changes and effects of exercise on parameters, Arch. Phys. Med. Rehabil., 67, 536

Waters, 2010, Osteoporosis and gait and balance disturbances in older sarcopenic obese New Zealanders, Osteoporos. Int., 21, 351, 10.1007/s00198-009-0947-5

2007, Prevalence of certain osteoporosis-determining habits among post menopausal women in the Basque Country, Spain, in 2003 (in Spanish), Rev. Esp. Salud Pública, 81, 647

Afilalo, 2010, Gait speed as an incremental predictor of mortality and major morbidity in elderly patients undergoing cardiac surgery, J. Am. Coll. Cardiol., 56, 1668, 10.1016/j.jacc.2010.06.039

Cutter, 1999, Development of a multiple sclerosis functional composite as a clinical trial outcome measure, Brain, 122, 871, 10.1093/brain/122.5.871

Hobart, 2003, Measuring the impact of MS on walking ability: The 12-Item MS Walking Scale (MSWS-12), Neurology, 60, 31, 10.1212/WNL.60.1.31

Holland, 2006, Talking the talk on walking the walk: A 12-item generic walking scale suitable for neurological conditions, J. Neurol., 253, 1594, 10.1007/s00415-006-0272-2

Tinetti, 1986, Performance-oriented assessment of mobility problems in elderly patients, J. Am. Geriatr. Soc., 34, 119, 10.1111/j.1532-5415.1986.tb05480.x

Mathias, 1986, Balance in elderly patients: The “get-up and go” test, Arch. Phys. Med. Rehabil., 67, 387

Wolfson, 1990, Gait assessment in the elderly: A gait abnormality rating scale and its relation to falls, J. Gerontol., 45, M12, 10.1093/geronj/45.1.M12

Fried, 1990, ELGAM-extra-laboratory gait assessment method: Identification of risk factors for falls among the elderly at home, Int. Disabil. Stud., 12, 161, 10.3109/03790799009166609

Pratheepan, Y., Condell, J.V., and Prasad, G. (2009, January 2–4). The Use of Dynamic and Static Characteristics of Gait for Individual Identification. Dublin, Ireland.

Kusakunniran, W., Wu, Q., Zhang, J., and Li, H. (2010, January 13–18). Support Vector Regression for Multi-View Gait Recognition Based on Local Motion Feature Selection. San Francisco, CA, USA.

Chang, P.C., Tien, M.C., Wu, J.L., and Hu, C.S. (2009, January 14–16). Real-Time Gender Classification from Human Gait for Arbitrary View Angles. San Diego, CA, USA.

Arias-Enriquez, O., Chacon-Murguia, M.I., and Sandoval-Rodriguez, R. (2012, January 6–8). Kinematic Analysis of Gait Cycle Using a Fuzzy System for Medical Diagnosis. Berkeley, CA, USA.

Iwashita, Y., Kurazume, R., and Ogawara, K. (2013, January 15–17). Expanding Gait Identification Methods from Straight to Curved Trajectories. Tampa, FL, USA.

Muramatsu, D., Shiraishi, A., Makihara, Y., and Yagi, Y. (2012, January 23–27). Arbitrary View Transformation Model for Gait Person Authentication. Arlington, VA, USA.

Jain, R.C., Kasturi, R., and Schunck, B.G. (1995). Machine Vision, McGraw-Hill.

Phan Ba, R., Pierard, S., Moonen, G., van Droogenbroeck, M., and Belachew, S. Detection and Quantification of Efficiency and Quality of Gait Impairment in Multiple Sclerosis through Foot Path Analysis. Available online: http://orbi.ulg.ac.be/handle/2268/132779.

Salberg, A.B., Hardeberg, J.Y., and Jenssen, R. (2009). Image Analysis, Springer.

Gabel, M., Gilad-Bachrach, R., Renshaw, E., and Schuster, A. (September, January 28). Full Body Gait Analysis with Kinect. San Diego, CA, USA.

Clark, 2013, Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining, Gait Posture, 38, 1064, 10.1016/j.gaitpost.2013.03.029

Xue, 2010, Infrared gait recognition based on wavelet transform and support vector machine, Pattern Recognit., 43, 2904, 10.1016/j.patcog.2010.03.011

Liu, H., Cao, Y., and Wang, Z. (2010, January 26–28). Automatic Gait Recognition from a Distance. Xuzhou, China.

Kolb, A., Barth, E., Koch, R., and Larsen, R. (2009). Time-of-Flight Sensors in Computer Graphics; EUROGRAPHICS STAR Report.

Derawi, M.O., Ali, H., and Cheikh, F.A. Gait Recognition Using Time-of-Flight Sensor. Available online: http://subs.emis.de/LNI/Proceedings/Proceedings191/187.pdf.

Samson, 2012, Dynamic footprint analysis by time-of-flight camera, Comput. Methods Biomech. Biomed. Engin., 15, 180, 10.1080/10255842.2012.713629

Geng, 2011, Structured-light 3D surface imaging: A tutorial, Adv. Opt. Photon., 3, 128, 10.1364/AOP.3.000128

Young, A. (1994). Handbook of Pattern Recognition and Image Processing, Academic Press.

Dziuban, E. Human Body Temperature Measurement—Class Program. Available online: http://www.imeko.org/publications/tc1-2002/IMEKO-TC1-2002-005.pdf.

Robertson, G., Kamen, G., Caldwell, G., Hamill, J., and Whittlesey, S. Available online: http://www.humankinetics.com/products/all-products/research-methods-in-biomechanics-2nd-edition.

Middleton, L., Buss, A.A., Bazin, A., and Nixon, M.S. (2005, January 17–18). A Floor Sensor System for Gait Recognition. Buffalo, NY, USA.

Leusmann, P., Mollering, C., Klack, L., Kasugai, K., Ziefle, M., and Rumpe, B. (2011, January 6–9). Your Floor Knows Where You Are: Sensing and Acquisition of Movement Data. Luleå, Sweden.

Mason, 2013, Comparative analysis and fusion of spatiotemporal information for footstep recognition, IEEE Trans. Pattern Anal. Mach. Intell., 35, 823, 10.1109/TPAMI.2012.164

Tao, 2012, Gait analysis using wearable sensors, Sensors, 12, 2255, 10.3390/s120202255

Zayegh, 2012, Foot plantar pressure measurement system: A review, Sensors, 12, 9884, 10.3390/s120709884

Bae, 2013, A tele-monitoring system for gait rehabilitation with an inertial measurement unit and a shoe-type ground reaction force sensor, Mechatronics, 23, 646, 10.1016/j.mechatronics.2013.06.007

Savelberg, 1999, Assessment of the horizontal, fore-aft component of the ground reaction force from insole pressure patterns by using artificial neural networks, Clin. Biomech., 14, 585, 10.1016/S0268-0033(99)00036-4

Koopman, 2004, Use of pressure insoles to calculate the complete ground reaction forces, J. Biomech., 37, 1427, 10.1016/j.jbiomech.2003.12.016

Howell, 2013, Kinetic gait analysis using a low-cost insole, IEEE Trans. Biomed. Eng., 60, 3284, 10.1109/TBME.2013.2250972

Lincoln, L.S., Bamberg, S.J.M., Parsons, E., Salisbury, C., and Wheeler, J. (2012, January 24–27). An Elastomeric Insole for 3-Axis Ground Reaction Force Measurement. Rome, Italy.

Gabriel, J., Schier, J., and Huffel, S.V. (2013). Biomedical Engineering Systems and Technologies, Springer.

Pons, J.L., Torricelli, D., and Pajaro, M. (2013). Converging Clinical and Engineering Research on Neurorehabilitation, Springer.

Salarian, 2004, Gait assessment in Parkinson's disease: Toward an ambulatory system for long-term monitoring, IEEE Trans. Biomed. Eng., 51, 1434, 10.1109/TBME.2004.827933

Tay, A., Yen, S.C., Li, J.Z., Lee, W.W., Yogaprakash, K., Chung, C., Liew, S., David, B., and Au, W.L. (2013, January 12–14). Real-Time Gait Monitoring for Parkinson Disease. Hangzhou, China.

Dominguez, G., Cardiel, E., Arias, S., and Rogeli, P. (2013, January 12–14). A Digital Goniometer Based on Encoders for Measuring Knee-Joint Position in an Orthosis. Fargo, ND, USA.

Bamberg, 2008, Gait analysis using a shoe-integrated wireless sensor system, Trans. Inf. Tech. Biomed., 12, 413, 10.1109/TITB.2007.899493

Wahab, Y., and Bakar, N.A. (2011, January 14–17). Gait Analysis Measurement for Sport Application Based on Ultrasonic System. Singapore.

Maki, 2012, A new ultrasonic stride length measuring system, Biomed. Sci. Instrum., 48, 282

Frigo, 2009, Multichannel SEMG in clinical gait analysis: A review and state-of-the-art, Clin. Biomech., 24, 236, 10.1016/j.clinbiomech.2008.07.012

Wentink, 2014, Detection of the onset of gait initiation using kinematic sensors and EMG in transfemoral amputees, Gait Posture, 39, 391, 10.1016/j.gaitpost.2013.08.008

Templo Clinical Gait Analysis. Available online: http://www.contemplas.com/clinical_gait_analysis_walkway.aspx.

Enhance Gait Analysis with Pressure Mapping. Available online: http://www.tekscan.com/medical/gait-analysis.html?utm_source=google&utm_medium=cpc&utm_term=gait+analysis&utm_content=ad1&utm_campaign=medical&gclid=CPvH8uWjgrsCFevjwgodqFIAsQ.

Grail—Gait Real-time Analysis Interactive Lab. Available online: http://www.motekmedical.com/products/grail-gait-real-time-analysis-interactive-lab/.

BTS Bioengineering. Available online: http://www.btsbioengineering.com/products/integrated-solutions/bts-gaitlab/.

Zhang, 2013, Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics, Physiolog. Meas., 34, N63, 10.1088/0967-3334/34/8/N63

Tec Gihan Co., Ltd. Available online: http://www.tecgihan.co.jp/english/p7.htm.

Intelligent Sensor and Control System Co., Ltd. Available online: http://www.insenco-j.com/_d275212500.htm.

Benedetti, 2013, Inter-laboratory consistency of gait analysis measurements, Gait Posture, 38, 934, 10.1016/j.gaitpost.2013.04.022

Lee, J., Lee, M.C., Liu, H., and Ryu, J.H. (2013). Intelligent Robotics and Applications, Springer.

Howcroft, 2013, Review of fall risk assessment in geriatric populations using inertial sensors, J. Neuroeng. Rehabil., 10, 1, 10.1186/1743-0003-10-91

Adachi, W., Tsujiuchi, N., Koizumi, T., Shiojima, K., Tsuchiya, Y., and Inoue, Y. (September, January 28). Calculation of Joint Reaction Force and Joint Moments Using by Wearable Walking Analysis System. San Diego, CA, USA.

Novak, 2013, Automated detection of gait initiation and termination using wearable sensors, Med. Eng. Phys., 35, 1713, 10.1016/j.medengphy.2013.07.003

Yang, 2012, Inertial sensor-based methods in walking speed estimation: A systematic review, Sensors, 12, 6102, 10.3390/s120506102

Salarian, 2013, Novel approach to reducing number of sensing units for wearable gait analysis systems, IEEE Trans. Biomed. Eng., 60, 72, 10.1109/TBME.2012.2223465

McGuire, M.L. (2012, January 19–21). An Overview of Gait Analysis and Step Detection in Mobile Computing Devices. Bucharest, Romania.

Kashihara, H., Shimizu, H., Houchi, H., Yoshimi, M., Yoshinaga, T., and Irie, H.A. (2013, January 7–8). Real-Time Gait Improvement Tool Using a Smartphone. Stuttgart, Germany.

Susi, 2013, Motion mode recognition and step detection algorithms for mobile phone users, Sensors, 13, 1539, 10.3390/s130201539

Chen, 2013, Locomotion mode classification using a wearable capacitive sensing system, IEEE Trans. Neural Syst. Rehabil. Eng., 21, 744, 10.1109/TNSRE.2013.2262952

Qi, Y., Soh, C.B., Gunawan, E., Low, K.S., and Maskooki, A. (2013, January 3–7). Using Wearable UWB Radios to Measure Foot Clearance During Walking. Osaka, Japan.

Horak, 2013, Objective biomarkers of balance and gait for parkinson's disease using body-worn sensors, Mov. Disord., 28, 1544, 10.1002/mds.25684

New Product Friday: The Keypad to my Heart. Available online: https://www.sparkfun.com/.

Shimmer. Available online: http://www.shimmersensing.com/.

Devantech. Available online: http://www.robot-electronics.co.uk/.

Maillet, 2012, Imaging gait disorders in Parkinsonism: A review, J. Neurol. Neurosurg. Psychiatry, 83, 986, 10.1136/jnnp-2012-302461

Dodson, 2012, Slow gait among older adults post-ami and risk for hospital readmission, J. Am. Coll. Cardiol., 59, E1914, 10.1016/S0735-1097(12)61915-9

Simon, 1996, Gait pattern in the early recovery period after stroke, J. Bone Joint Surg. Am., 78, 1506, 10.2106/00004623-199610000-00008

Jahn, 2010, Gait disturbances in old age (in German), Dtsch. Ärztebl. Int., 107, 306