Abstract
A fast and precise automatic gait diagnostic system is an urgent need for real-time clinical gait assessment. Existing machine intelligence-based systems to detect cerebral palsy gait have often ignored the crucial issue of performance and computation speed trade-off. This study, in a low-cost experimental setup, proposes an ensemble model by combining fast and deep neural networks. The proposed system demonstrates a competing result with an overall \({\approx }82\%\) of detection accuracy (sensitivity: \({\approx }78\%\), specificity: \({\approx }84\%\), and F1-score: \({\approx }83\%\)). Although the improvement in detection performance is marginal, the computation speed increased remarkably from state of the art. From the perspective of computation time and performance trade-off, the proposed model demonstrated to be competing.
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References
Alharbi A (2020) A genetic-elm neural network computational method for diagnosis of the Parkinson disease gait dataset. Int J Comput Math 97(5):1087–1099
Bei S, Zhen Z, Xing Z, Taocheng L, Qin L (2018) Movement disorder detection via adaptively fused gait analysis based on Kinect sensors. IEEE Sens J 18(17):7305–7314
Camps J, Sama A, Martin M, Rodriguez-Martin D, Perez-Lopez C, Arostegui JMM, Cabestany J, Catala A, Alcaine S, Mestre B et al (2018) Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit. Knowl-Based Syst 139:119–131
Chakraborty S, Thomas N, Nandy A (2020) Gait abnormality detection in people with cerebral palsy using an uncertainty-based state-space model. In: International conference on computational science. Springer, pp 536–549
Cui C, Bian G-B, Hou Z-G, Zhao J, Su G, Zhou H, Peng L, Wang W (2018) Simultaneous recognition and assessment of post-stroke hemiparetic gait by fusing kinematic, kinetic, and electrophysiological data. IEEE Trans Neural Syst Rehabil Eng 26(4):856–864
De Laet T, Papageorgiou E, Nieuwenhuys A, Desloovere K (2017) Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy? PloS one 12(6):e0178378
Dobson F, Morris ME, Baker R, Graham HK (2007) Gait classification in children with cerebral palsy: a systematic review. Gait Posture 25(1):140–152
Dolatabadi E, Taati B, Mihailidis A (2017) An automated classification of pathological gait using unobtrusive sensing technology. IEEE Trans Neural Syst Rehabil Eng 25(12):2336–2346
Ferrari A, Bergamini L, Guerzoni G, Calderara S, Bicocchi N, Vitetta G, Borghi C, Neviani R, Ferrari A (2019) Gait-based diplegia classification using lsmt networks. J Healthcare Eng
Gautam R, Sharma M (2020) Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J Med Syst 44(2):49
Geerse DJ, Coolen BH, Roerdink M (2015) Kinematic validation of a multi-Kinect v2 instrumented 10-meter walkway for quantitative gait assessments. PloS one 10(10):e0139913
Halilaj E, Rajagopal A, Fiterau M, Hicks JL, Hastie TJ, Delp SL (2018) Machine learning in human movement biomechanics: best practices, common pitfalls, and new opportunities. J Biomech 81:1–11
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48
Kamruzzaman J, Begg RK (2006) Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait. IEEE Trans Biomed Eng 53(12):2479–2490
Khokhlova M, Migniot C, Morozov A, Sushkova O, Dipanda A (2019) Normal and pathological gait classification lstm model. Artif Intell Med 94:54–66
Müller B, Ilg W, Giese MA, Ludolph N (2017) Validation of enhanced Kinect sensor based motion capturing for gait assessment. PloS one 12(4):e0175813
Nieuwenhuys A, Õunpuu S, Van Campenhout A, Theologis T, De Cat J, Stout J, Molenaers G, De Laet T, Desloovere K (2016) Identification of joint patterns during gait in children with cerebral palsy: a Delphi consensus study. Dev Med Child Neurol 58(3):306–313
Papageorgiou E, Nieuwenhuys A, Vandekerckhove I, Van Campenhout A, Ortibus E, Desloovere K (2019) Systematic review on gait classifications in children with cerebral palsy: an update. Gait Posture 69:209–223
Quisel T, Foschini L, Signorini A, Kale DC (2017) Collecting and analyzing millions of mhealth data streams. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1971–1980
Richards CL, Malouin F (2013) Cerebral palsy: definition, assessment and rehabilitation. In: Handbook of clinical neurology, vol 111. Elsevier, pp 183–195
Stavsky M, Mor O, Mastrolia SA, Greenbaum S, Than NG, Erez O (2017) Cerebral palsy-trends in epidemiology and recent development in prenatal mechanisms of disease, treatment, and prevention. Frontiers in pediatrics 5:21
Van Gestel L, De Laet T, Di Lello E, Bruyninckx H, Molenaers G, Van Campenhout A, Aertbeliën E, Schwartz M, Wambacq H, De Cock P et al (2011) Probabilistic gait classification in children with cerebral palsy: a Bayesian approach. Res Dev Disabil 32(6):2542–2552
You Z-H, Lei Y-K, Zhu L, Xia J, Wang B (2013) Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. In: BMC Bioinformatics, vol 14. Springer, p S10
Yu W, Li X, Gonzalez J (2019) Fast training of deep lstm networks with guaranteed stability for nonlinear system modeling. In: International symposium on neural networks, vol 422. Springer, pp 3–10
Zeni J Jr, Richards J, Higginson J (2008) Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait and posture 27(4):710–714
Zhang Y, Ma Y (2019) Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia. Comput Biol Med 106:33–39
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Chakraborty, S., Sambhavi, S., Panda, P., Nandy, A. (2023). An Ensemble Model for Gait Classification in Children and Adolescent with Cerebral Palsy: A Low-Cost Approach. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_7
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