[HTML][HTML] Fall detection in individuals with lower limb amputations using mobile phones: machine learning enhances robustness for real-world applications
Background: Automatically detecting falls with mobile phones provides an opportunity for
rapid response to injuries and better knowledge of what precipitated the fall and its
consequences. This is beneficial for populations that are prone to falling, such as people
with lower limb amputations. Prior studies have focused on fall detection in able-bodied
individuals using data from a laboratory setting. Such approaches may provide a limited
ability to detect falls in amputees and in real-world scenarios. Objective: The aim was to …
rapid response to injuries and better knowledge of what precipitated the fall and its
consequences. This is beneficial for populations that are prone to falling, such as people
with lower limb amputations. Prior studies have focused on fall detection in able-bodied
individuals using data from a laboratory setting. Such approaches may provide a limited
ability to detect falls in amputees and in real-world scenarios. Objective: The aim was to …
Addendum of: fall detection in individuals with lower limb amputations using mobile phones: machine learning enhances robustness for real-world applications
The corrected article will appear in the online version of the paper on the JMIR website on
December 20, 2017, together with the publication of this correction notice. Because this was
made after submission to PubMed Central, the corrected article will also be re-submitted to
PubMed Central.
December 20, 2017, together with the publication of this correction notice. Because this was
made after submission to PubMed Central, the corrected article will also be re-submitted to
PubMed Central.