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Abstract

Gait pattern can be characterized by many measurable parameters like heel strike timing, toe off timing, stride length, stride velocity, motion displacement, pressure distribution under the foot, orientation of the feet with respect to each other. By means of gait analysis, data related to these parameters are collected and utilized in order to diagnose skeletal system and neurologic disorders as well as for the evaluation of the improvement, if exits, before and after orthopedic operations, physical rehabilitations and treatment of the disease. As a result of neurologic diseases such as Cerebral Palsy and Parkinson and orthopedic diseases, gait pattern of a patient differs from that of a healthy person with change in one or more gait parameters. The purpose of a gait analysis is therefore to determine the gait parameters that can be used to differentiate between healthy and unhealthy states and focus on those parameters. Gait analysis is performed in clinics by the experienced clinicians with naked eye; in laboratories by means of video systems, electromyography (EMG) and force platforms and other systems, all of which are limited by time and location. In fact, data related to gait parameters should be collected in the patients’ daily life environment. The aim of this study is to summarize the current literature regarding the gait analysis systems, evaluate the reasons of being not popular in practice so far and provide the discussion of the features that a gait analysis system should have to become more popular in daily life use

Keywords

• Gait analysis, gait pattern, body-worn measurement system, balance and posture failures, force and inertial sensors

HASTALIKLARIN TEŞHİS VE TAKİBİNDE KULLANILAN YÜRÜYÜŞ ANALİZ SİSTEMLERİ

Öz

Yürüyüş, topuk vuruş zamanlaması, ayak başparmağı kalkış zamanlaması, adım uzunluğu, adım hızı, hareket mesafesi, ayağın altındaki basınç dağılımı, ayakların birbirine göre oryantasyonu gibi birçok parametre ile değerlendirilir. Yürüyüş analizinde bu parametrelerle ilgili veriler toplanmakta ve toplanan veriler, günümüzde, kliniklerde ve laboratuar ortamlarında, iskelet-kas sistem bozukluklarının ve nörolojik bozukluklarının değerlendirilmesinde ve tedavisinde, yapılan ortopedik operasyonlar öncesinde ve operasyonlar sonrası hastaların gözlemlenmesinde, hastalığın gelişiminde ve tedavinin etkinliğinin değerlendirilmesinde kullanılmaktadır. Yürüyüş analizi kliniklerde deneyimli klinisyenlerce yalın gözle, laboratuar ortamlarında ise video sistemler, EMG, yük platformları gibi sistemler kullanılarak zamanla ve mekanla sınırlı bir şekilde yapılmaktadır. Gerçekte, yürüyüş analizi verilerinin, hastanın normal hayatına devam ettiği sürede ve mekânlarda elde edilmesi gerekmektedir. Selebral Palsi ve Parkinson gibi nörolojik hastalıklar ile ortopedik hastalıklardan kaynaklanan sorunlarda yürüyüş örüntüsünü oluşturan parametrelerin birinde veya birkaçında sağlıklı bireylere göre değişiklikler görülmektedir. Yürüyüş analizinin amacı hastalıklı ve sağlıklı durumların birbirinden ayırt edilebilmesini sağlayacak parametreleri saptamaktır. 

Anahtar Kelimeler

• Yürüyüş analizi, yürüyüş örüntüsü, giyilebilir ölçüm sistemi, denge ve duruş bozuklukları, kuvvet ve eylemsizlik algılayıcıları

References

1. [1] Berg R., Palaniswami M., “Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques”, Idea Group Publishing, Austrialia, 2006.
2. [2] Davis, R.B., Deluca, P.A., Ounpuu, S. “Analysis of Gait”, The Biomedical Engineering Handbook”, 2. edition, CRR Pres LLC, 2000.
3. [3] Hanson M., Powell H.J., Barth A.T., Lach J., “Neural network gait classification for on body inertial sensors”, Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 3- 5 Haziran, Berkeley, California, ABD, 181-186,2009
4. [4] Luo X., Kline T., Heidi C.F., Stubblefield K.A., Kenyon R.V., Kamper D.G., “Integration of augmented reality and assistive devices for poststroke hand opening rehabilitation”. IEEE Engineering in Medicine and Biology Society, 27th Annual International Conference, 1-4 Eylül, Şangay, Çin, 6855-6858. ,2009
5. [5] Yang C., Chou C.,A Hu J., Hung S., Yang C.,Wu C., Hsu M.,Yang T., “Wireless gait analysis system by digital textile sensors” 31st Annual International Conference of the IEEE EMBS, 3-6 Eylül, Minneapolis, Minnesota, USA, 7256-7260,2009
6. [6] Faivre A., Dahan M., Parratte B., Monnier G., “Instrumented shoes for pathological gait assesment”, Mechanics Research Communications, V.31, Issue 5, p. 627-632, 2004.
7. [7] Lind R.F., Love Lonnie J., Rowe J.C., Pin F.G.,”Multi-axis foot reaction force/torque sensor for biomedical applications”. IEEE/RJS International Conference on Intelligent Robots and Systems, 10-15 Ekim, 2575-2579, 2009.
8. [8] Stefanovic F., Caltenco H., “A portable measurement system for the evaluation of human gait”. Journal of Automatic Control, University of Belgrad, 19, 1-6, 2009.

9. [9] Huang B., Chen M., Huang P., Xu Y., “Gait modelling for human identification”, IEEE International Conference on Robotics andAutomation, 10-14 Nisan, Roma, Đtalya, 4833- 4838,2007.
10. [10] Mariani B., Hoskovec C., Rochat S., Büla C, Penders J., Aminian K., “ 3D gait assesment in young and elderly subjects using foot worn inertial sensors”, Journal of Biomechanics, 43 (15), 2999-3006, 2010.
11. [11]Atallah L., Wiik A., Jones G.G., Lo B., Cobb J.P., Amis A., Yang G.Z., “Validation of an ear-worn sensor for gait monitoring using force-plate instrumented treadmill”, Gait & Posture, 35(4), 674- 676, 2012.
12. [12] Chelius G., Braillon C., Pasquier M., Horvais N., Gibollet R.P., Espiau B., Coste C.A., “A wearable sensor network for gait analysis: A six day experiment of running through the desert”, IEEE/ASME Transactions on Mechanics, 16(5), 878- 883,2011.
13. [13] Sabatini A., Martelloni C., Scapellato S., Cavallo F., “Assessment of walking features from foot inertial sensing”, IEEE Transaction on Biomedical Enginering, 52(3), 486-494,2005.
14. [14] Labini F. S., Meli A., Ivanenko Y.P., Tufarelli D., “Recurrence quantification analysis of gait in normal and hypovestibular subjects”, Gait&Posture, 35(1), 48-55, 2012.
15. [15] Kong K., Tomizuka M., “A gait monitoring system based on air pressure sensors embedded in a shoe”, IEEE/ASME TRANSACTIONS ON MECHATRONICS, 14(3), 358-370, 2009.
16. [16] Schepers M.,“Ambulatory assessment of human body kinematics and kinetics”, University of Twente, Doktora Tezi, 136s, Hollanda, 2009.
17. [17] Bamberg S.J.M., Benbasat A.Y., Scarborough D.M., Krebs D.E., Paradiso J.A., “Gait analysis using a shoe-integrated wireless sensor system”, IEEE Transaction on Information Technology in Biomedicine, 12(4), 413-423, 2008.
18. [18] Mariani B., Jimenez M.C. , Vingerhoets F.J.G. , Aminian K., “On-Shoe Wearable Sensors for Gait and Turning Assesment of Patients with Parkinson’s Disease”, IEEE Transactions on Biomedical Engineering Vol. 60(1), 155-158, 2013.
19. [19] Angusri N., Ishkawa K., Yin M., Omi E., Shibata Y., Saito T., Itasaka Y., “Gait instability caused by vestibular disorders - Analysis by tactile sensor”, Auris Nasus Larynx, 38(4), 462-468, 2011.
20. [20] Karakelle F., “Ataksik yürüme bozukluklarında, yürüme ve postürün değerlendirilmesi”, Uzmanlık Tezi,48s., Adana, 2008.
21. [21] Rueterbories J., Spaich E., Larsen B., Andersen O.K., “Methods for gait event detection and analysis in ambulator systems”, Medical Engineering & Physics, 36(2), 545-552, 2010.
22. [22] Klucken J., Barth J., Kugler P., Schlachetzki J., Henze T., Marzzeiter F., Kohl Z., Steidi R., Hornegger J., Eskoifer B., Winkler J., “Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease”, PLOS One, 8(2), 1-9, 2013.
23. [23] Liu T., Inoue Y., Shibata K., “Development of a wearable sensor system for quantative gait analysis”. Measurement, 42(7), 978-988, 2009.
24. [24] Westerdijk M.J.F., Schepers H.M., Veltnick P.H., Asseldonk E.H.F, Buurke J.H., “Use of inertial sensors for ambulatory assesment of center-of-mass displacements during walking”, IEEE Transactions on Biomedical Engineering, 59(7), 2080-2084, 2012.
25. [25] McGrath D., Greene B.R., O’Donovan K.J., Caulfield B., “Gyroscope-based assessment of temporal gait parameters during treadmill walking and running”, Sports Eng., 15(4), 207-213, 2012.
26. [26] Tien I., Glaser S., Aminoff M.J., “Characterization of gait abnormalities in Parkinson’s disease using a wireless inertial sensor system”, 32nd Annual International Conference of the IEEE EMBS, 31 Ağustos-4 Eylül, Buenos Aires, 3353-3356, 2010.
27. [27] Salarian A., Burkhard P.R., Vingerhoets F.J.G., Jolles B.M., Aminian K., “ A novel approach to reducing number of sensing units for wearable gait analysis systems” IEEE Transactions on Biomedical Engineering, 60(1), 72-77, 2013.
28. [28] Moore S.T., MacDougall H.G., Gracies J-M, Cohen H., Ondo W.G., “Long-term monitoring of gait in Parkinson disease”, Gait&Poisture, 26(2), 200-207, 2007.
29. [29] Jagos H., Oberzaucher J., Reichel M., Zagler W.L., Hlauschek W., “A multimodal approach for insole motion measurement and analysis”, Procedia Engineering, 2(2), 3103-3108, 2010.