Fall detection is a vital task in health monitoring, as it allows the system to trigger an alert and therefore enabling faster interventions when a person experiences a fall. Although most previous approaches rely on standard RGB video data, such detailed appearance-aware monitoring poses significant privacy concerns. Depth sensors, on the other hand, are better at preserving privacy as they merely capture the distance of objects from the sensor or camera, omitting color and texture information. In this paper, we introduce a privacy-supporting solution that makes the RGB-trained model applicable in depth domain and utilizes depth data at test time for fall detection. To achieve cross-modal fall detection, we present an unsupervised RGB to Depth (RGB2Depth) cross-modal domain adaptation approach that leverages labelled RGB data and unlabelled depth data during training. Our proposed pipeline incorporates an intermediate domain module for feature bridging, modality adversarial loss for modality discrimination, classification loss for pseudo-labeled depth data and labeled source data, triplet loss that considers both source and target domains, and a novel adaptive loss weight adjustment method for improved coordination among various losses. Our approach achieves state-of-the-art results in the unsupervised RGB2Depth domain adaptation task for fall detection. Code is available at https://github.com/1015206533/privacy_supporting_fall_detection.

翻译：跌倒检测在健康监测中是一项关键任务，它能使系统在人员发生跌倒时触发警报，从而实现更快速的干预。尽管以往大多数方法依赖于标准RGB视频数据，但这种涉及外观细节的监控方式引发了严重的隐私问题。相比之下，深度传感器能更好地保护隐私，因为它仅捕捉物体到传感器或摄像头的距离，而省略了颜色和纹理信息。本文提出了一种支持隐私保护的解决方案，使RGB训练的模型能够适用于深度域，并在测试阶段利用深度数据进行跌倒检测。为实现跨模态跌倒检测，我们提出了一种无监督RGB到深度（RGB2Depth）跨模态域自适应方法，该方法在训练过程中利用有标签的RGB数据和无标签的深度数据。我们的流程包含一个用于特征桥接的中间域模块、用于模态判别的模态对抗损失、针对伪标签深度数据和有标签源数据的分类损失、同时考虑源域和目标域的三元组损失，以及一种用于改善多种损失协调性的自适应损失权重调整方法。所提方法在无监督RGB2Depth域自适应跌倒检测任务中达到了最优结果。代码可在https://github.com/1015206533/privacy_supporting_fall_detection获取。