Abstract
The purpose of this study is to implement a walking program for the elderly by applying fashion model walking using Kinect. The skeleton through the avatar of the model walking that appears on the screen is given as feedback to the participant. The given information is implemented using Kinect, and the game proceeds with accurate gait balance control and the angle of the spine, which is the center of the body, in the walking program according to the body balance. Joint Value Using Unity 3D’s human pose library, Kinect modeling where 21 values out of a total of 95 joint values are presented. It is a game-type program in which the score of the spine angle and joint value is also lowered if the center of gravity of the body is not accurately displayed in the gait motion. Walking (right, left), turn (half, full turn, right, left respectively), pose (right center pose, left center pose) motion animations were filmed in real time. The walking motion appeared at normal speed only up to the step divided into two steps. When working with turns, full turns tended to be much more difficult than half turns. However, as the number of exercises increased, the degree of agreement between the angle of the spinal axis and the angle of the joints improved. In other words, the higher score that appears through the game format improves the control of walking balance and body balance, and the lower the accuracy of the body balance, the lower the score. This research program can be used as rehabilitation exercise for people with developmental disabilities, elderly people with dementia, and patients with Alzheimer’s disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kang, J.K., Lee, J.Y.: Status and Tasks of ICT-based Welfare services for the elderly living alone. J. Digit. Convergence 13(1), 67–76 (2015). https://doi.org/10.14400/JDC.2015.13.1.67
Lee, G.W., Son, H.W.: Geospatial Information Systems Thesaurus. Seoul: Gamebook (2016)
Gros, A., et al.: Recommendations for the use of ICT in elderly populations with affective disorders. Front. Aging Neurosci. 8, 269 (2016). https://doi.org/10.3389/fnagi.2016.00269
Amichai-Hamburger, Y., McKenna, K.Y., Tal, S.: E-empowerment: empowerment by the internet. Comput. Hum. Behave. 24(5), 1776–1789 (2008). https://doi.org/10.1016/j.chb.2008.02.002
Cho, S.Y., Byun, H.R., Lee, H.K., Cha, J.H.: Hand gesture recognition from kinect sensor data. Korean Soc. Broad Eng. 17(3), 447–458 (2012). https://doi.org/10.5909/JBE.2012.17.3.447
Ma, S.R.: The effects of exertainment task oriented upper limb motor task on muscle tone and upper extremity function in stroke patients over 65 years. J. Korea Entertainment Ind. Assoc. 11(7), 335–345 (2017). https://doi.org/10.21184/jkeia.2017.10.11.7.335
Moon, M.S., Jung, M.Y.: A systematic review on the association between cognitive function and upper extremity function in the elderly. Ther. Sci. Neurorehabilitation 5, 23–33 (2016)
Rikli, R.E., Jones, C.J.: Development and validation of criterion-referenced clinically relevant fitness standards for maintaining physical independence in later years. Gerontologist 53(2), 255–267 (2013)
Daubney, M.E., Culham, E.G.: Lower-extremity muscle force and balance performance in adults aged 65 years and older. Phys. Ther. 79(12), 1177–1185 (1999)
Gauchard, G.C., Gangloff, P., Jeandel, C., Perrin, P.P.: Physical activity improves gaze and posture control in the elderly. Neurosci. Res. 45(4), 409–417 (2003)
Lach, H.W., Reed, A.T., Arfken, Cl.: Falls in the elderly: reliability of a classification system. J. Am. Geriatr. Soc. 39, 197–202 (1991)
Kollegger, H., Baumgartner, C., Wober, C., Oder, W., Deecke, L.: Spontaneous body sway as a function of sex, age, and vision: posturographic study in 30 healthy adults. Eur. Neurol. 32, 253–259 (1992)
Tinetti, M.E., Speechley, M., Ginter, S.F.: Risk factor falls among elderly persons living in the community. N. Engl. J. Med. 319, 1701–1707 (1988)
Tinetti, M., Willams, T.F., Mauewski, K.: Fall risk index for elderly patients based on number of chronic disabilities. Am. J. Med. 80, 429–434 (1986)
Maki, B.E.: Gait changes in older adult: Predictors of falls or indicators of fear? J. Am. Geriatr. Soc. 45(3), 313–319 (1997)
Judge, J.O., Lindsey, C., Underwood, M., Winsemius, D.: Balance improvement in older women: effect of exercise training. Phys. Ther. 73(4), 253–262 (1993)
Brown, M., Sinacore, D.R., Host, H.: The relationship of power to function in the older adult. J. Gerontd. 50, 55–59 (1995)
Campbell, A.J., Barrie, M.J., Spears, G.F.: Risk factors for fall in a community based prospective study of people 70 years and older. J. Gerontol. 44, 112–117 (1989)
Lupu, R.G., Ungureanu, F., Botezatu, N., Ignat, D., Moldoveanu, A.: Virtual reality-based stroke recovery for upper limbs using leap motion. In: 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, pp. 295-299 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hong, S., Park, S., Jung, S. (2023). The Effect of Fashion Model Walking Program Using Kinect on the Movement Activity of the Elderly. In: Gao, Q., Zhou, J., Duffy, V.G., Antona, M., Stephanidis, C. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14055. Springer, Cham. https://doi.org/10.1007/978-3-031-48041-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-48041-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48040-9
Online ISBN: 978-3-031-48041-6
eBook Packages: Computer ScienceComputer Science (R0)