  Fall detection in specialized homes for the elderly is challenging.
Vision-based fall detection solutions have a significant advantage over
sensor-based ones as they do not instrument the resident who can suffer from
mental diseases. This work is part of a project intended to deploy fall
detection solutions in nursing homes. The proposed solution, based on Deep
Learning, is built on a Convolutional Neural Network (CNN) trained to maximize
a sensitivity-based metric. This work presents the requirements from the
medical side and how it impacts the tuning of a CNN. Results highlight the
importance of the temporal aspect of a fall. Therefore, a custom metric adapted
to this use case and an implementation of a decision-making process are
proposed in order to best meet the medical teams requirements. Clinical
relevance This work presents a fall detection solution enabled to detect 86.2%
of falls while producing only 11.6% of false alarms in average on the
considered databases.
