No abstract

A leading cause of death and serious injury in people, especially for the older people, are falls. In addition, fall accidents have a direct economic cost to healthcare systems and have an indirect impact, to the society's productivity. Among the most significant problems in fall detection systems is privacy, limitations of operating devices, and the comparison of machine learning techniques for detection. This article presents a system of fall detection by means of a k-Nearest Neighbor (KNN) classifier based on camera-vision using pose detection of the human skeleton for the features extraction. The proposed method is evaluated with UP-FALL dataset, surpassing the results of other fall detection systems that use the same database. This method achieves a 98.84% accuracy and an F 1 -Score of 97.41%.