An Intelligent Human Fall Detection System Using a Vision-Based Strategy

Elderly people is increasing dramatically during the current years, and it is expected that this population reaches 2.1 billion of individuals by 2050. In this regard, new care strategies are required. Assisted living technologies have proposed alternatives to support professional caregivers and families to take care of elderly people, such as in risk of falls. Currently, fall detection systems are able to alleviate the latter problem and reduce the time a person who suffered a fall receives assistance. Thus, this paper proposes a fall detection system based on image processing strategy to extract motion features through an optical flow method. For classification, we use these features as inputs to a convolutional neural network. We applied our approach in a dataset comprises video recordings of one subject performing different types of falls. In experimental results, our approach showed 92% accuracy on the dataset used.

I. Introduction

Elderly people is increasing dramatically during the current years, and it is expected that this population reaches 2.1 billion of individuals by 2050 [1] . Moreover, this growth in elderly people population in addition to higher life expectancy boost the necessity of creation of new human care strategies. Assisted living technologies have proposed alternatives to support professional caregivers and families to take care of elderly people. Some examples of issues presented in elderly people, that assisted living technologies can tackle, are loss of mobility, independent living diminishing, or risk of falls [2] .