Abstract
This paper proposes two approaches to characterize gait taking into account only quantitative measurements of dynamic nature. A pair of wireless sensorized insoles are used to obtain gait phases based on the involved forces, and a computer vision system externally estimates measurements through movement analysis. The wearable approach is composed of a pair of insoles, consisting of an assembly of FSRs and an inertial measurement unit. A micro-controller provides the captured data to a Bluetooth module that transmits it to be processed. The vision system obtains gait features using a single RGB camera. We have developed an algorithm to extract the silhouette using background subtraction, and locating heel and toe of each foot using the shape of the silhouettes. Detection of Heel-strike and Toe-off is based on gradient. Gait phases and other spatio-temporal parameters are derived from them.
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References
Perry, J.: Gait analysis systems. In: Gait Analysis: Normal and Pathological Function, ch. 19–24. SLACK Incorporated: Thorofare, New Jersey (1992)
Theologis, T., Stebbins, J.: The use of gait analysis in the treatment of pediatric foot and ankle disorders. Foot Ankle Clin. 15(2), 365–382 (2010)
Brach, J.S., Perera, S., Studenski, S., Newman, A.B.: The reliability and validity of measures of gait variability in community-dwelling older adults. Arch. Phys. Med. Rehabil. 89, 2293–2296 (2008)
Hausdorff, J.M.: Gait variability: methods, modeling and meaning. J. NeuroEngineering Rehabil. 2, 19 (2005)
Lopez-Meyer, P., Fulk, G.D., Sazonov, E.: Automatic detection of temporal gait parameters in poststroke individuals. IEEE Trans. Inf. Technol. Biomed. 15(4), 594–601 (2011)
Bamberg, S.J.M., Benbasat, A.Y., Scarborough, D.M., Krebs, D.E., Paradiso, J.A.: Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed. 12(4), 413–423 (2008)
Crea, S., Donati, M., De Rossi, S.M.M., Oddo, C.M., Vitiello, N.: A wireless flexible sensorized insole for gait analysis. Sens. 14, 1073–1093 (2014)
Pappas, I.P.I., Popovic, M.R., Kelly, T., Dietz, V., Morari, M.: A reliable gait phase detection system. IEEE Trans. Neural Syst. Rehabil. Eng. 9(2), 113–125 (2001)
Senanayake, C., Senanayake, S.M.N.A.: A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation. Comput. Methods Biomech. Biomed. Eng. 14(10), 863–874 (2011)
Roy, A., Sural, S., Mukherjee, J.: A hierarchical method combining gait and phase of motion with spatiotemporal model for person re-identification. Pattern Recogn. Lett. 33(14), 1891–1901 (2012)
Iwashita, Y., Stoica, A., Kurazume, R.: Gait identification using shadow biometrics. Pattern Recogn. Lett. 33(16), 2148–2155 (2012)
Choudhury, S.D., Tjahjadi, T.: Gait recognition based on shape and motion analysis of silhoutte contours. Comput. Vis. Image Underst. 117(12), 1770–1785 (2013)
Khan, T., Westin, J., Dougherty, M.: Motion cue analysis for Parkinsonian gait recognition. Open Biomed. Eng. J. 7, 1–8 (2013)
Choudhury, S.D., Tjahjadi, T.: Silhoutte-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors. Pattern Recogn. 45(9), 3414–3426 (2012)
Zeng, W., Wang, C., Yang, F.: Silhouette-based gait recognition via deterministic learning. Pattern Recogn. 47(11), 3568–3584 (2014)
Xu, X., McGorry, R.W., Chou, L., Lin, J., Chang, C.: Accuracy of the Microsoft Kinect for measuring gait parameters during treadmill walking. Gait & Posture 42(2), 145–151 (2015)
González, I., Fontecha, J., Hervás, R., Bravo, J.: An ambulatory system for gait monitoring based on wireless sensorized insoles. Sens. 15(7), 16589–16613 (2015)
Acknowledgment
This work is conducted in the context of the FRASE MINECO project (TIN2013-47152-C3-1-R). Also, we appreciate the support of UBIHEALTH project under International Research Staff Exchange Schema (MC-IRSES 316337).
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González, I., Nieto-Hidalgo, M., Mora, J., García-Chamizo, J.M., Bravo, J. (2015). A Dual Approach for Quantitative Gait Analysis Based on Vision and Wearable Pressure Systems. In: Cleland, I., Guerrero, L., Bravo, J. (eds) Ambient Assisted Living. ICT-based Solutions in Real Life Situations. IWAAL 2015. Lecture Notes in Computer Science(), vol 9455. Springer, Cham. https://doi.org/10.1007/978-3-319-26410-3_20
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DOI: https://doi.org/10.1007/978-3-319-26410-3_20
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