  A fall is an abnormal activity that occurs rarely; however, missing to
identify falls can have serious health and safety implications on an
individual. Due to the rarity of occurrence of falls, there may be insufficient
or no training data available for them. Therefore, standard supervised machine
learning methods may not be directly applied to handle this problem. In this
paper, we present a taxonomy for the study of fall detection from the
perspective of availability of fall data. The proposed taxonomy is independent
of the type of sensors used and specific feature extraction/selection methods.
The taxonomy identifies different categories of classification methods for the
study of fall detection based on the availability of their data during training
the classifiers. Then, we present a comprehensive literature review within
those categories and identify the approach of treating a fall as an abnormal
activity to be a plausible research direction. We conclude our paper by
discussing several open research problems in the field and pointers for future
research.
