  Falling, especially in the elderly, is a critical issue to care for and
surveil. There have been many studies focusing on fall detection. However, from
our survey, there is still no research indicating the prior-fall activities,
which we believe that they have a strong correlation with the intensity of the
fall. The purpose of this research is to develop a fall alert system that also
identifies prior-fall activities. First, we want to find a suitable location to
attach a sensor to the body. We created multiple-spot on-body devices to
collect various activity data. We used that dataset to train 5 different
classification models. We selected the XGBoost classification model for
detecting a prior-fall activity and the chest location for use in fall
detection from a comparison of the detection accuracy. We then tested 3
existing fall detection threshold algorithms to detect fall and fall to their
knees first, and selected the 3-phase threshold algorithm of Chaitep and
Chawachat [3] in our system. From the experiment, we found that the fall
detection accuracy is 88.91%, the fall to their knees first detection accuracy
is 91.25%, and the average accuracy of detection of prior-fall activities is
86.25%. Although we use an activity dataset of young to middle-aged adults
(18-49 years), we are confident that this system can be developed to monitor
activities before the fall, especially in the elderly, so that caretakers can
better manage the situation.
