Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring

2026-06-23

AI Summary

This study addresses the lack of systematic evaluation of camera performance under real-world deployment conditions in existing indoor healthcare monitoring systems, which hinders informed device selection and reproducible results. To bridge this gap, the authors propose two standardized validation protocols that jointly assess the metrological accuracy of both RGB and RGB-D cameras and their algorithmic performance in human pose estimation, explicitly incorporating practical deployment variables such as lighting conditions, occlusion, and mounting height. Comprehensive experiments across multiple mainstream cameras and state-of-the-art pose estimation algorithms reveal substantial depth measurement errors (50–1400 mm at 5 m), 2D pose estimation mAP ranging from 78% to 90%, and 3D reconstruction errors (MPJPE) between 104 and 365 mm—strongly influenced by depth data quality. This work establishes the first systematic framework for technical validation of cameras in medical monitoring contexts.

Abstract

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 camera selection 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. We present two technical validation protocols: the first evaluates the metrological performance of RGB and RGB-D 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 RGB-D 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 50 mm to over 1400 mm depending on the device. For 2D pose estimation, all cameras achieve broadly comparable accuracy, with mean mAP between approximately 78% and 90% across cameras and estimators, whereas 3D reconstruction error differs markedly across devices, with 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.

Problem

• camera validation
• indoor healthcare monitoring
• metrological performance
• pose estimation
• environmental variability

Innovation

• validation protocol
• RGB-D camera
• pose estimation
• metrological performance
• indoor healthcare monitoring