Abstract
Individualization of rehabilitation therapies for gait recovery is usually guided by subjective assessments based on the perception of the patient or the therapist. The use of sensorized devices can provide more objective data to adapt the therapy to each patient. In this work, an approach to recognise different daily activities is proposed based on the data captured by an instrumented tip for crutches. The developed approach is based on two steps: a pre-processing based on obtaining a set of statistical indicators, and an artificial neural network that process them. The proposed methodology has been tested in a group of 13 volunteers, allowing to recognize three critical daily activities (walking, standing still and going up/down stairs) with a success rate of 88%.
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Aminian, K., Robert, P., Jequier, E., Schutz, Y.: Estimation of speed and incline of walking using neural network. In: Conference Proceedings, 10th Anniversary, IMTC/94, Advanced Technologies in I & M, 1994 IEEE Instrumentation and Measurement Technology Conference (Cat. No. 94CH3424-9). IEEE (1994)
Bartlett, H.L., Goldfarb, M.: A phase variable approach for IMU-based locomotion activity recognition. IEEE Trans. Biomed. Eng. 65(6), 1330–1338 (2018)
Chamorro-Moriana, G., Sevillano, J., Ridao-Fernández, C.: A compact forearm crutch based on force sensors for aided gait: reliability and validity. Sensors 16(6), 925 (2016)
Cohen, J.A., Reingold, S.C., Polman, C.H., Wolinsky, J.S.: Disability outcome measures in multiple sclerosis clinical trials: current status and future prospects. Lancet Neurol. 11(5), 467–476 (2012). http://www.sciencedirect.com/science/article/pii/S1474442212700595
Federation, M.S.I.: The atlas of multiple sclerosis. Technical report (2013)
Gadaleta, M., Merelli, L., Rossi, M.: Human authentication from ankle motion data using convolutional neural networks. In: 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, June 2016
Godfrey, A., Conway, R., Meagher, D., ÓLaighin, G.: Direct measurement of human movement by accelerometry. Med. Eng. Phys. 30(10), 1364–1386 (2008)
Gyllensten, I.C., Bonomi, A.G.: Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life. IEEE Trans. Biomed. Eng. 58(9), 2656–2663 (2011)
Khan, A.M., Lee, Y.K., Kim, T.S.: Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, August 2008
Latimer-Cheung, A.E., Pilutti, L.A., Hicks, A.L., Ginis, K.A.M., Fenuta, A.M., MacKibbon, K.A., Motl, R.W.: Effects of exercise training on fitness, mobility, fatigue, and health-related quality of life among adults with multiple sclerosis: a systematic review to inform guideline development. Arch. Phys. Med. Rehabil. 94(9), 1800–1828.e3 (2013)
Lei, L., Peng, Y., Zuojun, L., Yanli, G., Jun, Z.: Leg amputees motion pattern recognition based on principal component analysis and BP network. In: 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, May 2013
World Health Organization: Neurological disorders: public health challenges. Technical report (2006)
Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors—a review of classification techniques. Physiol. Measur. 30(4), R1–R33 (2009)
Sardini, E., Serpelloni, M., Lancini, M., Pasinetti, S.: Wireless instrumented crutches for force and tilt monitoring in lower limb rehabilitation. Procedia Eng. 87, 348–351 (2014)
Shull, P.B., Jirattigalachote, W., Hunt, M.A., Cutkosky, M.R., Delp, S.L.: Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 40(1), 11–19 (2014)
Spain, R., George, R.S., Salarian, A., Mancini, M., Wagner, J., Horak, F., Bourdette, D.: Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. Gait Posture 35(4), 573–578 (2012)
Wang, N., Ambikairajah, E., Lovell, N.H., Celler, B.G.: Accelerometry based classification of walking patterns using time-frequency analysis. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, August 2007
Watanabe, T., Yamagishi, S., Murakami, H., Furuse, N., Hoshimiya, N., Handa, Y.: Recognition of lower limb movements by artificial neural network for restoring gait of hemiplegic patients by functional electrical stimulation. In: 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2011)
Zeng, W., Wang, C.: Classification of neurodegenerative diseases using gait dynamics via deterministic learning. Inf. Sci. 317, 246–258 (2015)
Acknowledgments
This research was supported by the University of the Basque Country under grant number PIF18/067, by the University of the Basque Country UPV/EHU under project number PPGA19/48 (GV/EJ IT1381-19), by the European Commission under grant number PN/TG1/UNSW/PhD/18/2017 and by the Ministerio de Ciencia, Innovación y Universidades (MCIU) under grant number DPI2017-82694-R (AEI/FEDER, UE).
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Brull, A., Gorrotxategi, A., Zubizarreta, A., Cabanes, I., Rodriguez-Larrad, A. (2020). Classification of Daily Activities Using an Intelligent Tip for Crutches. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_33
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DOI: https://doi.org/10.1007/978-3-030-36150-1_33
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