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Abstract
It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.
Keywords
OLDER-ADULTS, FALLS

Citation

Please use this url to cite or link to this publication:

Chicago
Baldewijns, Greet, Stijn Luca, William Nagels, Bart Vanrumste, and Tom Croonenborghs. 2015. “Automatic Detection of Health Changes Using Statistical Process Control Techniques on Measured Transfer Times of Elderly.” In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5046–5049. New York, NY, USA: IEEE.
APA
Baldewijns, G., Luca, S., Nagels, W., Vanrumste, B., & Croonenborghs, T. (2015). Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly. 2015 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5046–5049). Presented at the 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), New York, NY, USA: IEEE.
Vancouver
1.
Baldewijns G, Luca S, Nagels W, Vanrumste B, Croonenborghs T. Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly. 2015 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York, NY, USA: IEEE; 2015. p. 5046–9.
MLA
Baldewijns, Greet et al. “Automatic Detection of Health Changes Using Statistical Process Control Techniques on Measured Transfer Times of Elderly.” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York, NY, USA: IEEE, 2015. 5046–5049. Print.
@inproceedings{8581146,
  abstract     = {It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82\% and an average detection time of 9.64 days.},
  author       = {Baldewijns, Greet and Luca, Stijn and Nagels, William and Vanrumste, Bart and Croonenborghs, Tom},
  booktitle    = {2015 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  isbn         = {9781424492718},
  issn         = {1557-170X},
  language     = {eng},
  location     = {Milan, Italy},
  pages        = {5046--5049},
  publisher    = {IEEE},
  title        = {Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly},
  url          = {http://dx.doi.org/10.1109/embc.2015.7319525},
  year         = {2015},
}

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