Abstract
Aiming at the problems of low accuracy and high time cost of existing motion function rehabilitation evaluation methods, this paper proposes a rehabilitation evaluation method and system of lower limb motion function based on gait parameter analysis. Firstly, according to the calculation method of gait information related parameters, compare the differences of age and gender in healthy people and the differences of gait information between healthy and disabled group, and preliminarily determine the characteristic parameters of lower limb motion function evaluation. Then, the evaluation indexes are determined through multicollinearity and significance analysis, and the multiple linear regression model of Fugl-Meyer assessment (FMA) score is established. The goodness-of-fit test method is used to prove that the proposed evaluation method can replace the lower limb score of FMA as the evaluation method of lower limb motor function in stroke patients. Finally, the lower limb motor function evaluation system is built in the upper computer, so that the model can be used concretely.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Makihara, T., et al.: Shoulder motion assistance using a single-joint Hybrid Assistive limb® robot: evaluation of its safety and validity in healthy adults. J. Orthop. Surg. 25(3), 1–6 (2017)
Pérez-nombela, S., et al.: Physiological Evaluation of Different Control Modes of Lower Limb Robotic Exoskeleton H2 in Patients with Incomplete Spinal Cord Injury, vol. 15, pp. 343–348 (2017)
Rodgers, H., et al.: Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial. Lancet 394(10192), 51–62 (2019)
Nam, H.S., Hong, N., Cho, M., Lee, C., Seo, H.G., Kim, S.: Vision-assisted interactive human-in-the-loop distal upper limb rehabilitation robot and its clinical usability test. Appl. Sci. 9(15), 1–12 (2019)
Wu, C.H., Mao, H.F., Hu, J.S., Wang, T.Y., Tsai, Y.J., Hsu, W.L.: The effects of gait training using powered lower limb exoskeleton robot on individuals with complete spinal cord injury. J. Neuroeng. Rehabil. 15(1), 1–10 (2018)
Blackwood, J.: Reliability, validity and minimal detectable change in the Timed Up and Go and five times sit to stand tests in older adults with early cognitive loss. J. Physiother. Rehabil 5, 58–65 (2017)
Olafsdottir, S.A., et al.: Developing ActivABLES for community-dwelling stroke survivors using the Medical Research Council framework for complex interventions. BMC Health Serv. Res. 20(1), 1–14 (2020)
Huang, C.Y., et al.: Improving the utility of the Brunnstrom recovery stages in patients with stroke: validation and quantification. Med. (United States) 95(31), 1–8 (2016)
Lim, J.Y., An, S.H., Park, D.S.: Walking velocity and modified rivermead mobility index as discriminatory measures for functional ambulation classification of chronic stroke patients. Hong Kong Physiother. J. 39(2), 125–132 (2019)
Freire, B., Bochehin do Valle, M., Lanferdini, F.J., Foschi, C.V.S., Abou, L., Pietta-Dias, C.: Cut-off score of the modified Ashworth scale corresponding to walking ability and functional mobility in individuals with chronic stroke. Disabil. Rehabil. 1–5 (2022)
Rech, K.D., Salazar, A.P., Marchese, R.R., Schifino, G., Cimolin, V., Pagnussat, A.S.: Fugl-Meyer assessment scores are related with kinematic measures in people with chronic hemiparesis after stroke. J. Stroke Cerebrovasc. Dis. 29(1), 1–8 (2020)
Liao, W.L., Chang, C.W., Sung, P.Y., Hsu, W.N., Lai, M.W., Tsai, S.W.: The berg balance scale at admission can predict community ambulation at discharge in patients with stroke. Med. 57(6), 1–8 (2021)
Liu, X., et al.: Wearable devices for gait analysis in intelligent healthcare. Front. Comput. Sci. 3(May), 1–8 (2021)
Wu, J., et al.: An intelligent in-shoe system for gait monitoring and analysis with optimized sampling and real-time visualization capabilities. Sensors 21(8), 1–19 (2021)
Serrao, M., et al.: Prediction of responsiveness of gait variables to rehabilitation training in Parkinson’s disease. Front. Neurol. 10(JUL), 1–12 (2019)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant NSFC U1813212, in part by the Science and Technology Planning Project of Guangdong Province, China under Grant 2020B121201012.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, YP. et al. (2022). Gait Time Parameter Analysis-Based Rehabilitation Evaluation System of Lower Limb Motion Function. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-13835-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13834-8
Online ISBN: 978-3-031-13835-5
eBook Packages: Computer ScienceComputer Science (R0)