Abstract
To follow-up Parkinson’s disease (PD) progress, clinical gait analysis is performed with the precise measuring equipments (e.g. IMU, electric walkway, etc.). However, the existing gait analysis methods have a limitation such that patients must visit a certain space in hospital for the checkup. For clinical gait analysis in and out of hospital, we propose a baseline model of ‘deepvision’ system, which can estimate 15 clinical gait parameters measured from electric walkway named GAITRite. We constructed 3D convolution layers which have skip connections to grasp spatio-temporal characteristics of the walking behavior with an effective manner. Afterwards, we validated the method with scripted walking videos, and achieved the following results: error range of temporal and spatial parameters as 32–71 ms, 1.6–6.7 cm respectively, and error for cadence, velocity and functional ambulation profile as 7.0 steps/min, 4.1 cm/min, and 4.9 points respectively.
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References
Benson, L.C., Clermont, C.A., Bošnjak, E., Ferber, R.: The use of wearable devices for walking and running gait analysis outside of the lab: a systematic review. Gait Posture 63, 124–138 (2018)
Cloete, T., Scheffer, C.: Benchmarking of a full-body inertial motion capture system for clinical gait analysis. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4579–4582. IEEE (2008)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
Pfister, A., West, A.M., Bronner, S., Noah, J.A.: Comparative abilities of microsoft kinect and vicon 3D motion capture for gait analysis. J. Med. Eng. Technol. 38(5), 274–280 (2014)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint. arXiv:1804.02767 (2018)
Salarian, A., et al.: Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans. Biomed. Eng. 51(8), 1434–1443 (2004)
Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472. IEEE (2017)
Storm, F.A., Buckley, C.J., Mazzà, C.: Gait event detection in laboratory and real life settings: accuracy of ankle and waist sensor based methods. Gait Posture 50, 42–46 (2016)
Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: C3D: generic features for video analysis. CoRR 2(7), 8 (2014). arXiv:1412.0767
Trojaniello, D., et al.: Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait. J. Neuroeng. Rehabil. 11(1), 152 (2014)
Trojaniello, D., Ravaschio, A., Hausdorff, J.M., Cereatti, A.: Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson’s disease and Huntington’s disease subjects. Gait Posture 42(3), 310–316 (2015)
Veit, A., Wilber, M.J., Belongie, S.: Residual networks behave like ensembles of relatively shallow networks. In: Advances in Neural Information Processing Systems, pp. 550–558 (2016)
Zijlstra, W., Hof, A.L.: Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 18(2), 1–10 (2003)
Acknowledgement
This work was supported by Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MS-IT) (2016-0-00564, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding) and supported by the Korea government (MOTIE) (P0004794, Creating innovate ecosystem for Convergence medical industry of Daegu innovation city).
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Yu, H., Kang, K., Jeong, S., Park, J. (2019). Deep Vision System for Clinical Gait Analysis in and Out of Hospital. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_69
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DOI: https://doi.org/10.1007/978-3-030-36808-1_69
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