The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist.
References
[1]
Kavanagh, J.J.; Menz, H.B. Accelerometry: A technique for quantifying movement patterns during walking. Gait Posture 2008, 28, 1–15.
[2]
Tao, W.; Liu, T.; Zheng, R.; Feng, H. Gait analysis using wearable sensors. Sensors 2012, 12, 2255–2283.
[3]
Aminian, K.; Najafi, B. Capturing human motion using body fixed sensors: Outdoor measurement and clinical applications. Comput. Anim. Virtual Worlds 2004, 15, 79–94.
[4]
LeMoyne, R.; Coroian, C.; Mastroianni, T.; Grundfest, W. Accelerometers for quantification of gait and movement disorders: A perspective review. J. Mech. Med. Biol. 2008, 8, 137–152.
[5]
Bonato, P. Wearable sensors and systems. Eng. Med. Biol. Mag. IEEE 2010, 29, 25–36.
[6]
Patel, S.; Park, H.; Bonato, P.; Chan, L.; Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 2012, 9, 1–17.
[7]
Corke, P.; Lobo, J.; Dias, J. An introduction to inertial and visual sensing. Int. J. Robot. Res. 2007, 26, 519–535.
[8]
Tao, Y.; Hu, H.; Zhou, H. Integration of vision and inertial sensors for 3D arm motion tracking in home-based rehabilitation. Int. J. Robot. Res. 2007, 26, 607–624.
[9]
Mizuike, C.; Ohgi, S.; Morita, S. Analysis of stroke patient walking dynamics using a tri-axial accelerometer. Gait Posture 2009, 30, 60–67.
[10]
Salarian, A.; Russmann, H.; Vingerhoets, F.J.G.; Dehollain, C.; Blanc, Y.; Burkhard, P.R.; Aminian, K. Gait assessment in Parkinson's disease: Toward an ambulatory system for long-term monitoring. IEEE Trans. Biomed. Eng. 2004, 51, 1434–1443.
[11]
Brennan, A.; Zhang, J.; Deluzio, K.; Li, Q. Quantification of inertial sensor-based 3D joint angle measurement accuracy using an instrumented gimbal. Gait Posture 2011, 34, 320–323.
[12]
Favre, J.; Aissaoui, R.; Jolles, B.M.; de Guise, J.A.; Aminian, K. Functional calibration procedure for 3D knee joint angle description using inertial sensors. J. Biomech. 2009, 42, 2330–2335.
[13]
Tadano, S.; Takeda, R.; Miyagawa., H. Three dimensional gait analysis using wearable acceleration and gyro sensors based on quaternion calculations. Sensors 2013, 13, 9321–9343.
[14]
Bergmann, J.H.M.; McGregor, A. Body-worn sensor design: What do patients and clinicians want? Ann. Biomed. Eng. 2011, 39, 2299–2312.
[15]
Andriacchi, T.P.; Briant, P.L.; Bevill, S.L.; Koo, S. Rotational changes at the knee after ACL injury cause cartilage thinning. Clin. Orthop. Relat. Res. 2006, 442, 39–44.
[16]
Gao, B.; Zheng, N.N. Alterations in three-dimensional joint kinematics of anterior cruciate ligament-deficient and-reconstructed knees during walking. Clin. Biomech. 2010, 25, 222–229.
[17]
Lohmander, L.S.; Englund, P.M.; Dahl, L.L.; Roos, E.M. The long-term consequence of anterior cruciate ligament and meniscus injuries. Am. J. Sports Med. 2007, 35, 1756–1769.
[18]
Butler, R.J.; Minick, K.I.; Ferber, R.; Underwood, F. Gait mechanics after ACL reconstruction: Implications for the early onset of knee osteoarthritis. Br. J. Sports Med. 2009, 43, 366–370.
[19]
Knoll, Z.; Kocsis, L.; Kiss, R.M. Gait patterns before and after anterior cruciate ligament reconstruction. Knee Surg. Sports Traumatol. Arthrosc. 2004, 12, 7–14.
[20]
Webster, K.E.; Wittwer, J.E.; O'Brien, J.; Feller, J.A. Gait patterns after anterior cruciate ligament reconstruction are related to graft type. Am. J. Sports Med. 2005, 33, 247–254.
[21]
Monaghan, K.; Delahunt, E.; Caulfield, B. Increasing the number of gait trial recordings maximises intra-rater reliability of the CODA motion analysis system. Gait Posture 2007, 25, 303–315.
[22]
Greene, B.R.; McGrath, D.; O'Neill, R.; O'Donovan, K.J.; Burns, A.; Caulfield, B. An adaptive gyroscope-based algorithm for temporal gait analysis. Med. Biol. Eng. Comput. 2010, 48, 1251–1260.
[23]
Strohrmann, C.; Rossi, M.; Arnrich, B.; Troster, G. A Data-Driven Approach to Kinematic Analysis in Running Using Wearable Technology. Proceedings of 9th International Conference on Wearable and Implantable Body Sensor Networks, London, UK, 9–12 May 2012; pp. 118–123.
[24]
Hreljac, A.; Marshall, R.N. Algorithms to determine event timing during normal walking using kinematic data. J. Biomech. 2000, 33, 783–786.
[25]
Delahunt, E.; Monaghan, K.; Caulfield, B. Altered neuromuscular control and ankle joint kinematics during walking in subjects with functional instability of the ankle joint. Am. J. Sports Med. 2006, 34, 1970–1976.
[26]
Monaghan, K.; Delahunt, E.; Caulfield, B. Ankle function during gait in patients with chronic ankle instability compared to controls. Clin. Biomech. 2006, 21, 168–174.
[27]
Moisio, K.C.; Sumner, D.R.; Shott, S.; Hurwitz, D.E. Normalization of joint moments during gait: A comparison of two techniques. J. Biomech. 2003, 36, 599–603.
[28]
Pallant, J. SPSS Survival Manual: A Step-By-Step Guide to Data Analysis Using SPSS, 4th ed. ed.; Open University Press: London, UK, 2010; pp. 209–210.
[29]
Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. ed.; Routledge: New York, NY, USA, 1988.
[30]
Mariani, B.; Jiménez, M.C.; Vingerhoets, F.J.; Aminian, K. On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease. IEEE Trans. Biomed. Eng. 2013, 60, 155–158.
[31]
Felson, D.T.; Zhang, Y.; Hannan, M.T.; Naimark, A.; Weissman, B.; Aliabadi, P.; Levy, D. Risk factors for incident radiographic knee osteoarthritis in the elderly. The Framingham Study. Arthritis Rheum. 1997, 40, 728–733.
[32]
Aminian, K.; Trevisan, C.; Najafi, B.; Dejnabadi, H.; Frigo, C.; Pavan, E.; Telonio, A.; Cerati, F.; Marinoni, E.C.; Robert, P.H.; et al. Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement. Gait Posture 2004, 20, 102–107.
[33]
Gelber, A.C.; Hochberg, M.C.; Mead, L.A.; Wang, N.Y.; Wigley, F.M.; Klag, M.J. Joint injury in young adults and risk for subsequent knee and hip osteoarthritis. Ann. Intern. Med. 2000, 133, 321–328.
