Abstract
The ability to walk is typically related to several biomechanical components that are involved in the gait cycle (or stride), including free mobility of joints, particularly in the legs; coordination of muscle activity in terms of timing and intensity; and normal sensory input, such as vision and vestibular system. As people age, they tend to slow their gait speed, and their balance is also affected. Also, the retirement from the working life and the consequent reduction of physical and social activity contribute to the increased incidence of falls in older adults. Moreover, older adults suffer different kinds of cognitive decline, such as dementia or attention problems, which also accentuate gait disorders and its consequences. In this paper we present a methodology for gait identification using the on-board sensors of a smart rollator: the i-Walker. This technique provides the number of steps performed in walking exercises, as well as the time and distance travelled for each stride. It also allows to extract spatio-temporal metrics used in medical gait analysis from the interpretation of the interaction between the individual and the i-Walker. In addition, two metrics to assess users’ driving skills, laterality and directivity, are proposed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adell, E., Wehmhörner, S., Rydwik, E.: The test-retest reliability of 10 meters maximal walking speed in older people living in a residential care unit. J. Geriatr. Phys. Ther. 36(2), 74–77 (2013)
Ballesteros, J., Urdiales, C., Martinez, A.B., Tirado, M.: Automatic assessment of a rollator-user’s condition during rehabilitation using the i-Walker platform. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 2009–2017 (2017)
Cortés, A., et al.: A fall prevention protocol using the i-Walker robotic rollator: the I-DONT-FALL project. In: 14th International Conference on Mobility and Transport for Elderly and Disabled Persons, TRANSED 2015, Lisbon, Portugal, pp. 1366–1380 (2015)
Cortés, A., Martínez, A.B., Béjar, J. (eds.): 4th Workshop on ICTs for Improving Patients Rehabilitation Research Techniques, REHAB 2016. ACM, New York (2016)
Cortés, A., Ojeda, M., Béjar, A.B., Martínez, J.: An approach to gait analysis from human-rollator interaction: the i-Walker. In: 21th International Conference of the Catalan Association for Artificial Intelligence, 8–10 October 2018, Spain (2018)
Cortés, U., Martínez-Velasco, A., Barrué, C., Annicchiarico, R.: AI based fall management services - the role of the i-Walker in I-DONTFALL. In: Advances in Artificial Intelligence - 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, 2012, pp. 395–406 (2012)
Integrated prevention and Detection sOlutioNs Tailored to the population and Risk Factors associated with FALLs. http://www.idontfall.eu/ (2010)
Montero-Odasso, M., et al.: Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J. Gerontol. Ser. A: Biol. Sci. Med. Sci. 60(10), 1304–1309 (2005)
Montero-Odasso, M., Verghese, J., Beauchet, O., Hausdorff, J.M.: Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J. Am. Geriatr. 60(11), 2127–2136 (2012)
Nooijen, C., ter Hoeve, N., Field-Fote, E.: Gait quality is improved by locomotor training in individuals with SCI regardless of training approach. J. NeuroEng. Rehabil. 6, 36 (2009)
Pirker, W., Katzenschlager, R.: Gait disorders in adults and the elderly. Wiener Klinische Wochenschrift 129(3–4), 81–95 (2017)
Prakash, C., Kumar, R., Mittal, N.: Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif. Intell. Rev. 49, 1–40 (2016)
Shimada, H., et al.: Physical factors underlying the association between lower walking performance and falls in older people: a structural equation model. Arch. Gerontol. Geriatr. 53(2), 131–134 (2010)
Tinetti, M.E., Speechley, M., Ginter, S.F.: Risk factors for falls among elderly persons living in the community. New Engl. J. Med. 319(26), 1701–1707 (1988)
Urdiales, C.: Collaborative Assistive Robot for Mobility Enhancement (CARMEN) - The Bare Necessities: Assisted Wheelchair Navigation and Beyond. Vol. 27 of Intelligent Systems Reference Library. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24902-0
Wang, T., et al.: Walking analysis of young and elderly people by using an intelligent walker ANG. Robot. Auton. Syst. (2014)
WHO: European health report 2012: charting the way to well-being. World Health Organization (2013)
Yorozu, A., Moriguchi, T., Takahashi, M.: Improved leg tracking considering gait phase and spline-based interpolation during turning motion in walk tests. Sensors 15(9), 22451 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cortés, A., Martínez, A.B., Béjar, J. (2019). Spatio-Temporal Gait Analysis Based on Human-Smart Rollator Interaction. In: Fardoun, H., Hassan, A., de la Guía, M. (eds) New Technologies to Improve Patient Rehabilitation. REHAB 2016. Communications in Computer and Information Science, vol 1002. Springer, Cham. https://doi.org/10.1007/978-3-030-16785-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-16785-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16784-4
Online ISBN: 978-3-030-16785-1
eBook Packages: Computer ScienceComputer Science (R0)