# An Empirical Study towards Understanding How Deep Convolutional Nets Recognize Falls

## TL;DR

This paper conducts an empirical analysis of how deep convolutional neural networks recognize falls, revealing learned patterns and influencing factors to inform better fall detection system designs.

## Contribution

It systematically investigates the fall recognition process of CNNs using multiple tasks, input modalities, and network instances, providing insights into their behavior.

## Key findings

- Identifies patterns learned by CNNs in fall recognition
- Highlights factors affecting CNN performance in fall detection
- Provides quantitative and qualitative analysis of CNN behaviors

## Abstract

Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone. In recent years, deep convolutional nets are widely used in human action analysis, based on which a number of fall detection methods have been proposed. Despite their highly effective performances, the behaviors of how the convolutional nets recognize falls are still not clear. In this paper, instead of proposing a novel approach, we perform a systematical empirical study, attempting to investigate the underlying fall recognition process. We propose four tasks to investigate, which involve five types of input modalities, seven net instances and different training samples. The obtained quantitative and qualitative results reveal the patterns that the nets tend to learn, and several factors that can heavily influence the performances on fall recognition. We expect that our conclusions are favorable to proposing better deep learning solutions to fall detection systems.