Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring

Camera-based monitoring systems are increasingly adopted in healthcare settings for the continuous assessment of patient movement and activities. However, their technical performance under real-world indoor conditions remains insufficiently characterised, preventing appropriate selection when choosing cameras for clinical or home adoption and reproducibility. Existing validation studies typically assess either device metrological performance or algorithm accuracy in isolation, and often do not systematically account for practical deployment factors, such as lighting variability, occlusions, and camera positioning. To address this, we present two technical validation protocols that evaluate the same cameras at both the metrological and pose-estimation levels under systematically controlled deployment conditions rarely addressed together in prior work: the first evaluates the metrological performance of RGB and RGBD cameras, and the second assesses their use in supporting human pose estimation, validated using state-of-the-art pose estimators. The proposed protocols systematically assess five cameras (four RGBD and one RGB) under controlled variations in lighting, camera height, viewing angle, and occlusion level, within representative indoor scenarios. The experimental results show that metrological performance varies substantially across cameras, with depth bias at 5 m ranging from ∼10 mm to over 1400 mm depending on the device. For 2D pose estimation, all cameras achieve broadly comparable accuracy (mean mAP between ~78% and ~90%) across cameras and estimators, whereas 3D reconstruction error differs markedly across devices (MPJPE ranging from 104 mm to 365 mm), closely reflecting underlying depth sensing quality. Environmental factors have a camera- and estimator-dependent effect on 3D performance, while camera mounting height has minimal influence within the evaluated range. This work provides evidence-based guidance for the selection and deployment of cameras in healthcare monitoring applications, addressing an important gap in current technical validation practice.