Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN
Alexy Carlier (IETR), Paul Peyramaure (IETR), Ketty Favre (UR1),, Muriel Pressigout (IETR)

TL;DR
This paper presents a vision-based fall detection system for nursing homes using a CNN trained with a custom sensitivity metric, emphasizing the importance of temporal data to improve detection accuracy and reduce false alarms.
Contribution
It introduces a CNN-based fall detection method tailored to medical needs, incorporating a custom metric and decision process for better clinical relevance.
Findings
Detects 86.2% of falls
Produces 11.6% false alarms
Highlights importance of temporal data
Abstract
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…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Balance, Gait, and Falls Prevention · Pressure Ulcer Prevention and Management
