Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, Bridgett L, Williams S, Guillemin F, Hill CL, et al. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1323–30.
Zhang Y, Jordan JM. Epidemiology of osteoarthritis. Clin Geriatr Med. 2010;26(3):355–69.
Murphy L, Helmick CG. The impact of osteoarthritis in the united states: a population-health perspective: a population-based review of the fourth most common cause of hospitalization in us adults. Orthop Nurs. 2012;31(2):85–91.
Constantinou M, Barrett R, Brown M, Mills P. Spatial-temporal gait characteristics in individuals with hip osteoarthritis: a systematic literature review and meta-analysis. J Orthop Sports Phys Therapy. 2014;44(4):291-B7.
Cichy B, Wilk-Frańczuk M. Gait analysis in osteoarthritis of the hip. Med Sci Monit. 2006;12(12):CR507-513.
Choursiya P, Veqar Z, Khan Z, Tanwar T, Iram I, Aldabbas M. Gait analysis technologies for measurement of biomechanical parameters of knee osteoarthritis. SN Compr Clin Med. 2024;6(1):6.
Teufl W, Taetz B, Miezal M, Lorenz M, Pietschmann J, Jöllenbeck T, Fröhlich M, Bleser G. Towards an inertial sensor-based wearable feedback system for patients after total hip arthroplasty: validity and applicability for gait classification with gait kinematics-based features. Sensors. 2019;19(22):5006.
Ornetti P, Maillefert J-F, Laroche D, Morisset C, Dougados M, Gossec L. Gait analysis as a quantifiable outcome measure in hip or knee osteoarthritis: a systematic review. Jt Bone Spine. 2010;77(5):421–5.
Laroche D, Tolambiya A, Morisset C, Maillefert J-F, French RM, Ornetti P, Thomas E. A classification study of kinematic gait trajectories in hip osteoarthritis. Comput Biol Med. 2014;55:42–8.
Kubota K, Hanawa H, Yokoyama M, Kita S, Hirata K, Fujino T, Kokubun T, Ishibashi T, Kanemura N. Usefulness of muscle synergy analysis in individuals with knee osteoarthritis during gait. IEEE Trans Neural Syst Rehabil Eng. 2020;29:239–48.
Salvino L, Chiu WK, Lynch J, Loh KJ. Special issue of biomedical engineering letters on advances in intelligent prostheses. Biomed Eng Lett. 2020;10:1–3.
Tosserams A, Keijsers N, Kapelle W, Kessels RP, Weerdesteyn V, Bloem BR, Nonnekes J. Evaluation of compensation strategies for gait impairment in patients with Parkinson disease. Neurology. 2022;99(20):e2253–63.
Chen C-L, Chen H-C, Tang SF-T, Wu C-Y, Cheng P-T, Hong W-H. Gait performance with compensatory adaptations in stroke patients with different degrees of motor recovery. Am J Phys Med Rehabilitat. 2003;82(12):925–35.
Dhanalakshmi S, Maanasaa RS, Maalikaa RS, Senthil R. A review of emergent intelligent systems for the detection of Parkinson’s disease. Biomed Eng Lett. 2023;13(4):591–612.
Fregly BJ, Reinbolt JA, Rooney KL, Mitchell KH, Chmielewski TL. Design of patient-specific gait modifications for knee osteoarthritis rehabilitation. IEEE Trans Biomed Eng. 2007;54(9):1687–95.
Diamond LE, Devaprakash D, Cornish B, Plinsinga ML, Hams A, Hall M, Hinman RS, Pizzolato C, Saxby DJ. Feasibility of personalised hip load modification using real-time biofeedback in hip osteoarthritis: a pilot study. Osteoarthr Cartil Open. 2022;4(1):100230.
Astephen Wilson JL, Lamontagne M, Wilson DR, Beaulé PE, Mwale F, Yee A. Patient-specific functional analysis: The key to the next revolution towards the treatment of hip and knee osteoarthritis. J Orthop Res ®. 2019;37(8):1754–9.
Kaczmarczyk K, Wit A, Krawczyk M, Zaborski J. Gait classification in post-stroke patients using artificial neural networks. Gait Post. 2009;30(2):207–10.
Dolatabadi E, Mansfield A, Patterson KK, Taati B, Mihailidis A. Mixture-model clustering of pathological gait patterns. IEEE J Biomed Health Inform. 2016;21(5):1297–305.
Halim HNA, Azaman A. Clustering-based support vector machine (SVM) for symptomatic knee osteoarthritis severity classification. In: Proceedings of the 2022 9th international conference on biomedical and bioinformatics engineering; 2022, pp. 140–146.
Prajapati N, Kaur A, Sethi D, A review on clinical gait analysis. In: 2021 5th international conference on trends in electronics and informatics (ICOEI), IEEE; 2021, pp. 967–974.
Parween R, Shriram D, Mohan RE, Lee YHD, Subburaj K. Methods for evaluating effects of unloader knee braces on joint health: a review. Biomed Eng Lett. 2019;9:153–68.
Lee M, Lee H, Chung H, Lee J-H, Kim D, Cho S, Kim T-J, Kim HS. Micro-current stimulation could inhibit il-1\(\beta\)-induced inflammatory responses in chondrocytes and protect knee bone cartilage from osteoarthritis’’. Biomed Eng Lett. 2024. https://doi.org/10.1007/s13534-024-00376-1.
Lee S-S, Choi ST, Choi S-I. Classification of gait type based on deep learning using various sensors with smart insole. Sensors. 2019;19(8):1757.
Howell AM, Kobayashi T, Hayes HA, Foreman KB, Bamberg SJM. Kinetic gait analysis using a low-cost insole. IEEE Trans Biomed Eng. 2013;60(12):3284–90.
Xu W, Huang M-C, Amini N, Liu JJ, He L, Sarrafzadeh M. Smart insole: a wearable system for gait analysis. In: Proceedings of the 5th international conference on pervasive technologies related to assistive environments; 2012, pp. 1–4.
Manap HH, Tahir NM, Yassin AIM, Abdullah R. Anomaly gait classification of Parkinson disease based on ANN. In: 2011 IEEE international conference on system engineering and technology. IEEE; 2011, pp. 5–9.
Pradhan A, Oladi S, Kuruganti U, Chester V. Classification of elderly fallers and non-fallers using force plate parameters from gait and balance tasks. In: Methods Computer, editor. Imaging and visualization in biomechanics and biomedical engineering: Selected papers from the 16th international symposium CMBBE and 4th conference on imaging and visualization, August 14–16, 2019. New York City: Springer; 2020. p. 339–53.
Rodrigues TB, Salgado DP, Catháin CÓ, O’Connor N, Murray N. Human gait assessment using a 3d marker-less multimodal motion capture system. Multimed Tools Appl. 2020;79:2629–51.
Alaqtash M, Sarkodie-Gyan T, Yu H, Fuentes O, Brower R, Abdelgawad A. Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms. In: Annual international conference of the IEEE engineering in medicine and biology society. IEEE. 2011;2011, pp. 453–7.
LeMoyne R, Kerr W, Mastroianni T, Hessel A. Implementation of machine learning for classifying hemiplegic gait disparity through use of a force plate. In: 2014 13th international conference on machine learning and applications. IEEE; 2014, pp. 379–382.
Mezghani N, Boivin K, Turcot K, Aissaoui R, Hagmeister N, De Guise JA. Hierarchical analysis and classification of asymptomatic and knee osteoarthritis gait patterns using a wavelet representation of kinetic data and the nearest neighbor classifier. J Mech Med Biol. 2008;8(01):45–54.
Kim JY, Illigens BM, McCormick MP, Wang N, Gibbons CH, Choi J, Park J, Lee B-I, ShinKj Yoo S, et al. The correlation between cognition screening scores and gait status from three-dimensional gait analysis. J Clin Neurol. 2019;15(2):152–8.
Wren TA, Do KP, Rethlefsen SA, Healy B. Cross-correlation as a method for comparing dynamic electromyography signals during gait. J Biomech. 2006;39(14):2714–8.
Salarian A, Russmann H, Vingerhoets FJ, Dehollain C, Blanc Y, Burkhard PR, Aminian K. Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng. 2004;51(8):1434–43.
Wahid F, Begg RK, Hass CJ, Halgamuge S, Ackland DC. Classification of Parkinson’s disease gait using spatial-temporal gait features. IEEE J Biomed Health Inform. 2015;19(6):1794–802.
Ceseracciu E, Sawacha Z, Cobelli C. Comparison of markerless and marker-based motion capture technologies through simultaneous data collection during gait: proof of concept. PLoS ONE. 2014;9(3):e87640.
Moro M, Marchesi G, Hesse F, Odone F, Casadio M. Markerless versus marker-based gait analysis: a proof of concept study. Sensors. 2022;22(5):2011.
Zell P, Rosenhahn B. A physics-based statistical model for human gait analysis. In: Pattern recognition: 37th German conference, GCPR,. Aachen, Germany, October 7–10, 2015, Proceedings 37. Springer. 2015, 2015; pp. 169–80.
Osateerakun P. Estimation of lower extremity joint moments in clinical gait analysis by using artificial neural networks. Liverpool John Moores University (United Kingdom), 2021.
Chicco D, Warrens MJ, Jurman G. The coefficient of determination r-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci. 2021;7:e623.
Prayudani S, Hizriadi A, Lase Y, Fatmi Y, et al. Analysis accuracy of forecasting measurement technique on random k-nearest neighbor (RKNN) using MAPE and MSE. J Phys: Conf Ser. 2019;1361(1): 012089.
Reynolds DA, et al. Gaussian mixture models. Encycl Biom. 2009;741:659–63.
Rasmussen C. The infinite gaussian mixture model. Adv Neural Inf Process syst. 1999;12.
Xuan G, Zhang W, Chai P. Em algorithms of gaussian mixture model and hidden markov model. In: Proceedings international conference on image processing (Cat. No. 01CH37205), vol. 1. IEEE. 2001; 2001, pp. 145–8.
Papavasileiou, I, Zhang W, Han S. Real-time data-driven gait phase detection using infinite gaussian mixture model and parallel particle filter. In: 2016 IEEE First international conference on connected health: applications, systems and engineering technologies (CHASE). IEEE; 2016, pp. 302–311.
Kellgren J, Lawrence J. Radiological assessment of rheumatoid arthritis. Ann Rheum Dis. 1957;16(4):485.
Xue Y, Zhang R, Deng Y, Chen K, Jiang T. A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS ONE. 2017;12(6):e0178992.
Nair SP, Gibbs S, Arnold G, Abboud R, Wang W. A method to calculate the centre of the ankle joint: A comparison with the vicon® plug-in-gait model. Clin Biomech. 2010;25(6):582–7.
Duffell LD, Hope N, McGregor AH. Comparison of kinematic and kinetic parameters calculated using a cluster-based model and vicon’s plug-in gait. Proc Inst Mech Eng [H]. 2014;228(2):206–10.
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