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http://dx.doi.org/10.9708/jksci.2022.27.11.029

Gait Type Classification Using Multi-modal Ensemble Deep Learning Network  

Park, Hee-Chan (Dept. of Computer Science and Engineering, Dankook University, AlcheraInc)
Choi, Young-Chan (Dept. of AI-based Convergence, Dankook University)
Choi, Sang-Il (Dept. of Computer Science and Engineering, Dankook University, Dept. of AI-based Convergence, Dankook University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.11, 2022 , pp. 29-38 More about this Journal
Abstract
This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.
Keywords
Gait type; Deep learning; Ensemble network; Smart insole; Multi-modal sensor;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Murray, M.P.; Drought, A.B.; Kory, R.C. Walking patterns of normal men. J. Bone Jt. Surg. 1964, 46, 335-360.   DOI
2 Schoenberg, Isaac J. (1946). Contributions to the Problem of Approximation of Equidistant Data by Analytic Functions: Part A.-On the Problem of Smoothing or Graduation. A First Class of Analytic Approximation Formulae. Quarterly of Applied Mathematics. 4 (2): 45-99.   DOI
3 Bridle, John. "Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters." Advances in neural information processing systems 2 (1989).
4 Charalambous, Charalambos P. "Walking patterns of normal men." Classic Papers in Orthopaedics. Springer, London, 2014. 393-395.
5 E. Alpaydin, "Multiple networks for function learning," IEEE International Conference on Neural Networks, 1993, pp. 9-14 vol.1, doi: 10.1109/ICNN.1993.298539.   DOI
6 Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS'12). Curran Associates Inc., Red Hook, NY, USA, 1097-1105.
7 Hochreiter, Sepp. (1998). The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 6. 107-116. 10.1142/S0218488598000094.   DOI
8 Kelley, H. J. (1960). Gradient theory of optimal flight paths. Ars Journal, 30(10), 947-954   DOI
9 Q. Deng, Y. Luo, and J. Ge, "Dual threshold based unsupervised face image clustering," Proceedings of the 2nd International Conference on Industrial Mechatronics and Automation, pp. 436-439, 2010.
10 SIMGRID Project, http://simgrid.gforge.inria.fr
11 Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998, doi: 10.1109/5.726791.   DOI
12 Jucheol Moon, Nelson Hebert Minaya, Nhat Anh Le, Hee-Chan Park, Sang-Il Choi(Corresp. Author), "Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?", MDPI Sensors, (1424-8220, 2.457 ), 2020.
13 Sung-Sin Lee, Sang Tae Choi, Sang-Il Choi(Corresp. Author), "Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole", MDPI Sensor, ( 1424-8220, 2.457 ), 2019.
14 Seo, Woo-Duk, et al. "Gait type classification using pressure sensor of smart insole." Journal of The Korea Society of Computer and Information 23.2 (2018): 17-26.   DOI
15 Kingma, Diederik & Ba, Jimmy. (2014). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations.
16 Bonnet, Stephane, and Pierre Jallon. "Hidden markov models applied onto gait classification." 2010 18th European Signal Processing Conference. IEEE, 2010.
17 Davis III, Roy B., et al. "A gait analysis data collection and reduction technique." Humanmovement science 10.5 (1991): 575-587.
18 Williams, Ronald J.; Hinton, Geoffrey E.; Rumelhart, David E. (October 1986). "Learning representations by back-propagating errors". Nature. 323 (6088): 533-536.   DOI
19 Yan, Lei, et al. "Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network." Complexity 2020 (2020).
20 Wu, Jianning, Jue Wang, and Li Liu. "Feature extraction via KPCA for classification of gait patterns." Human movement science 26.3 (2007): 393-411.   DOI
21 Hochreiter, S., & Schmidhuber, J"urgen. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.   DOI
22 Karl Pearson F.R.S. . (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.   DOI
23 B. Sch6lkopf, A. Smola, and K-R. Miiller. "Nonlinear component analysis as a kernel eigenvalue problem." Neural Computation, 10:1299-1319, 1998. Technical Report No. 44, 1996, Max Planck Institut fiir biologische Kybernetik, Tiibingen.   DOI
24 Hee Chan Park, "Classification of Gait Pattern Based on Ensemble Learning Using Smart Insole," M.S. Thesis, Dankook University, Korea, Feb. 2021.
25 Cevikalp, Hakan, et al. "Discriminative common vectors for face recognition." IEEE Transactions on pattern analysis and machine intelligence 27.1 (2005): 4-13.   DOI
26 Agarap, A. F. (2018). Deep learning using rectified linear units (relu). ArXiv Preprint ArXiv:1803.08375.
27 Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (January 2014), 1929-1958.
28 E. Smirni, and G. Ciardo, "Workload-Aware Load Balancing for Cluster Web Servers," IEEE Trans. on Parallel and Distributed Systems, Vol. 16, No. 3, pp. 219-232, March 2005. DOI: 10.1109/TPDS.2005.38   DOI
29 Taborri, Juri, et al. "A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network." Sensors 14.9 (2014): 16212-16234.   DOI
30 Ioffe, S. & Szegedy, C.. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning in Proceedings of Machine Learning Research
31 Kdhong, "An Efficient Dynamic Workload Balancing Strategy," Journal of The Korea Society of Computer and Information, Vol. 15, No. 1, pp. 1-10, Nov. 2010.   DOI
32 Kdhong,, "C Programming Language" Korea-Press, pp.100-120, 1991.
33 Q. Deng, Y. Luo, and J. Ge, "Dual threshold based unsupervised face image clustering," Proceedings of the 2nd International Conference on Industrial Mechatronics and Automation, pp. 436-439, 2010.
34 Q. Deng, Y. Luo, and J. Ge, "Dual threshold based unsupervised face image clustering," Proceedings of the 2nd International Conference on Industrial Mechatronics and Automation, pp. 436-439, 2010.
35 L. Rabiner and B. Juang, "An introduction to hidden Markov models," in IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, Jan 1986, doi: 10.1109/MASSP.1986.1165342.   DOI
36 Choi, Sang-Il, et al. "User identification from gait analysis using multi-modal sensors in smart insole." Sensors 19.17 (2019): 3785
37 Shetty, Sachin, and Y. S. Rao. "SVM based machine learning approach to identify Parkinson's disease using gait analysis." 2016 International Conference on Inventive Computation Technologies (ICICT). Vol. 2. IEEE, 2016