@inbook{0d077dd17d3f4d27ad610181602db27d,
title = "To what extent it is possible to predict falls due to standing hypotension by using HRV and wearable devices? Study design and preliminary results from a proof-of-concept study",
abstract = "Falls are a major problem in later life reducing the well-being, mobility and quality of life. One of the main causes of falls is standing hypotension. This paper presents the design and the very preliminary results of a pilot study aiming to investigate if it is possible to predict standing hypotension and in projection those falls due to standing hypotension, using the HRV short term recording to estimate the blood pressure drop-down (ΔBP) due to fast rising up from a bed. The preliminary results shown that in the 79\% of the experiment conducted, the HRV acquired with commercial wearable devices could predict ΔBP due to standing hypotension with an error below the sphigmomanoter measurement error.",
keywords = "Blood Pressure drop-down Prediction, HRV analysis, Prediction of falls, Standing Hypotension",
author = "Giovanna Sannino and Paolo Melillo and \{de Pietro\}, Giuseppe and Saverio Stranges and Leandro Pecchia",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.",
year = "2014",
doi = "10.1007/978-3-319-13105-4\_26",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "167--170",
editor = "Leandro Pecchia and Leandro Pecchia and Chen, \{Liming Luke\} and Chris Nugent and Jos{\'e} Bravo",
booktitle = "Ambient Assisted Living and Daily Activities - 6th International Work-Conference, IWAAL 2014, Proceedings",
address = "Germany",
}