Review of Fall Detection Techniques: A Data Availability Perspective
This work addresses the problem of fall detection for healthcare and safety applications, but it is incremental as it primarily reviews and categorizes existing techniques without introducing new methods.
The paper tackles the challenge of fall detection in machine learning by proposing a taxonomy based on data availability, as falls are rare events with insufficient training data. It reviews existing methods within this framework and identifies treating falls as abnormal activities as a promising research direction.
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.