论文标题
通过基于光流的CNN适应疗养院需求的秋季探测器
Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN
论文作者
论文摘要
在专业房屋中对老年人的秋季发现具有挑战性。基于视觉的秋季检测解决方案比基于传感器的检测解决方案没有造成精神疾病的居民的能力。这项工作是旨在在疗养院中部署秋季检测解决方案的项目的一部分。提出的基于深度学习的解决方案是建立在经过培训的卷积神经网络（CNN）基于最大化基于灵敏度度量的。这项工作介绍了医疗方面的要求及其如何影响CNN的调整。结果突出了秋天时间方面的重要性。因此，提出了适合此用例的自定义指标，并提出了决策过程的实施，以便最能满足医疗团队的要求。临床相关性这项工作提出了一种跌落检测解决方案，可以检测到86.2％的跌倒，而在考虑的数据库中平均产生了11.6％的错误警报。
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.
