Effective home-based rehabilitation depends on accurate assessment. This study evaluates the accuracy of LiDAR technology for home measurements and has significant implications for the future of home-based rehabilitation. Three researchers from different professional backgrounds—a healthcare professional, a public health researcher, and an engineer with expertise in the LiDAR system—scanned the interior of a typical UK house using LiDAR-equipped devices (iPhone-13-ProMax, iPad-Pro 11-inch, and Leica_BLK360-G1). Room dimensions were also measured using tape as a reference standard with an accuracy of ±0.1 cm. The environmental light level in each room was measured with a LUX-light meter app (Google-Pixel3a). The reliability of the measurements from the LiDAR devices was assessed against the tape measure, which served as the reference standard, using the intraclass correlation coefficient (ICC) for absolute agreement. The environmental light ranged from 54 to 1051 LUX. All three devices demonstrated high reliability in measuring room dimensions: iPad Pro (ICC =0.989 to 1.000), iPhone (ICC =0.967 to 0.999), and Leica (ICC =0.998 to 1.000). However, the Leica device showed limitations under low-light conditions. These findings support using LiDAR technology by healthcare professionals to accurately assess patients’ home environments, facilitate sustainable rehabilitation services, and potentially reduce the need for home visits.

Introduction Axial spondylarthritis (axSpA) is a chronic inflammatory disease and commonly results in pain and joint stiffness. Using remote technology, such as a computer vision-aided system, has the potential to monitor functional movement and posture. Methods The validity of the remote technology measurement of functional movement and posture were tested cross-sectionally and compared to a standard clinical measurement by a physiotherapist. The feasibility of remote implementation was tested in a home environment. In addition, a cost-benefit analysis was conducted. Results Thirty-one participants with axSpA (42% female, 54(SD 13) years old and 27.4(SD 5.3) kg/m2) and 31 participants without back pain (65% female, 36(SD 10) years old and 25.9(3.7) kg/m2). In the axSpA group, the validity of assessment on cervical rotation, lumbar flexion, lumbar side flexion, shoulder flexion, hip abduction, tragus-to-wall and thoracic kyphosis showed significant moderate to strong correlation; in the non-back pain group, the same measures showed significant correlation ranging from weak to strong. Conclusions Remote technology systems in rehabilitation have the potential to reduce health inequality and improve cost and time effectiveness for both patients and the health system. Additionally, results show that using this Computer Vision-aided system in a home environment is a safe method.