Abstract
Non-wearable sensors utilizing advanced image-processing and sensing technologies for capturing an individual’s behavior in real-world settings are now available. Wearable sensors are difficult to use, especially for long-term recordings, because the batteries for these sensors must be exchanged or recharged. Behavioral data collected using non-wearable sensors can be used for precision nursing, which indicates individualized risk management and intervention plan based on the understanding of individual risk and other health status. In this study, we applied the developed non-wearable technologies to evaluate a person’s gait ability, which is an important factor for creating a fall prevention program.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
World Health Organization. http://www.who.int/mediacentre/factsheets/fs344/en/
Tokyo Fire Department. http://www.tfd.metro.tokyo.jp/lfe/topics/201509/kkhansoudeta.html
Hayashi, Y.: The influence of falls on medical economy. Bone 23(2), 181–184 (2009). (available only in Japanese)
Rubenstein, L.Z., Josephson, K.R., Robbins, A.S.: Falls in the nursing home. Ann. Intern. Med. 121(6), 442–451 (1994)
Murata, E., Kitamura, K., Nishida, Y.: Fall’s and non-fall’s feature comparison analysis for the fall prediction system development of the elderly. In: The 17th SICE System Integration Lecture, pp. 2858–2860 (2016)
Soyano, A.: Fall prevention of elderly people with dementia. J. Jpn. Prev. Soc. 1, 17–21 (2014). (available only in Japanese)
Stalenhoef, P.A., Diederiks, J.P.M., Knottnerus, J.A., Kester, A.D.M., Crebolder, H.F.J.M.: A risk model for the prediction of recurrent falls in community-dwelling elderly, a prospective cohort study. J. Clin. Epidemiol. 55, 1088–1094 (2002)
Maki, B.E.: Gait changes in older adults predictors of falls or indicators of fear. J. Am. Geriatr. Soc. 45, 313–320 (1997)
Cumming, R.G., Klineberg, R.J.: Fall frequency and characteristic and the risk of hip fractures. J. Am. Geriatr. Soc. 42, 774–778 (1994)
Hartikainen, S., Lönnroos, E., Louhivuori, K.: Medication as a risk factor for falls. Critical systematic review. J. Gerontol. A Biol. Sci. Med. Sci. 62(10), 1172–1181 (2007)
Nevitt, M.C., Cummings, S.R., Kidd, A., Black, D.: Risk factors for recurrent non-syncopal falls. A prospective study. JAMA 261, 2663–2668 (1989)
Robinovitch, S.N., Feldman, F., Yang, Y., Schonnop, R., Leung, P.M., Sarraf, T., Gould, J.S., Loughin, M.: Video capture of the circumstances of falls in elderly people residing in long-term care an observational study. Lancet 381, 47–54 (2013)
Acknowledgments
This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Murata, E., Kitamura, K., Oono, M., Shirato, Y., Nishida, Y. (2018). Behavior Monitoring with Non-wearable Sensors for Precision Nursing. In: Arezes, P. (eds) Advances in Safety Management and Human Factors. AHFE 2017. Advances in Intelligent Systems and Computing, vol 604. Springer, Cham. https://doi.org/10.1007/978-3-319-60525-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-60525-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60524-1
Online ISBN: 978-3-319-60525-8
eBook Packages: EngineeringEngineering (R0)