  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.
