论文标题
音频变压器的音频检测
Fall Detection from Audios with Audio Transformers
论文作者
论文摘要
老年人的跌落检测是一些提出的解决方案，包括可穿戴和不可磨损技术的一个经过深入研究的问题。尽管现有技术的检测率很高，但由于需要佩戴设备和用户隐私问题，因此缺乏目标人群的采用。我们的论文提供了一种新颖的，不可磨损的，不受欢迎的和可扩展的解决方案，用于秋季检测，该解决方案部署在配备麦克风的自主移动机器人上。所提出的方法使用了人们家中记录的环境声音输入。我们专门针对浴室环境，因为它很容易落下，并且在不危及用户隐私的情况下无法部署现有技术。目前的工作基于变压器体系结构开发了一种解决方案，该解决方案从浴室中获取嘈杂的声音输入，并将其分为秋季/禁止班级，准确性为0.8673。此外，提出的方法可扩展到其他室内环境，除了浴室外，还适合在老年家庭，医院和康复设施中部署，而无需用户佩戴任何设备或不断被传感器“看”。
Fall detection for the elderly is a well-researched problem with several proposed solutions, including wearable and non-wearable techniques. While the existing techniques have excellent detection rates, their adoption by the target population is lacking due to the need for wearing devices and user privacy concerns. Our paper provides a novel, non-wearable, non-intrusive, and scalable solution for fall detection, deployed on an autonomous mobile robot equipped with a microphone. The proposed method uses ambient sound input recorded in people's homes. We specifically target the bathroom environment as it is highly prone to falls and where existing techniques cannot be deployed without jeopardizing user privacy. The present work develops a solution based on a Transformer architecture that takes noisy sound input from bathrooms and classifies it into fall/no-fall class with an accuracy of 0.8673. Further, the proposed approach is extendable to other indoor environments, besides bathrooms and is suitable for deploying in elderly homes, hospitals, and rehabilitation facilities without requiring the user to wear any device or be constantly "watched" by the sensors.
