Scoping Review of Depth-Sensing Technologies for Balance and Gait Assessments in Older Adults: Implications for Fall Prevention in Rehabilitation

Background: Falls among older adults are a significant public health concern with a global prevalence of approximately 26.5%, leading to substantial morbidity, mortality, and healthcare burden.Depth cameras offer promising non-invasive methods for evaluating balance and gait in geriatric rehabilitation settings; however, the evidence remains heterogeneous and requires systematic mapping.Objective: This scoping review examined the extent, nature, and characteristics of evidence on the use of depth cameras for physical therapy and rehabilitation evaluations among older adults, with afocus on fall prevention applications.Methods: Following Arksey and O'Malley's five-stage framework and PRISMA-ScR guidelines, we searched six databases (PubMed/MEDLINE, Scopus, Web of Science, CINAHL, IEEE Xplore, and Embase) without date restrictions.Eligibility criteria included primary research involving older adults ( 65 years), depth camera evaluations in rehabilitation contexts, and a focus on ≥ fall prevention.Data extraction encompassed study characteristics, technologies, evaluation methods, and outcomes.Results: Seven studies (2018-2023) met the inclusion criteria, predominantly utilizing Microsoft Kinect platforms.They demonstrated moderate to excellent reliability (ICC: 0.633-1.00)and high validity (r 0.7) for automated assessments, including ≥ the Berg Balance Scale, Timed Up and Go, and gait parameters.Virtual reality-based interventions significantly improved balance scores and reduced fear of falling.Systems achieved >95% accuracy for gait speed and >97% for time-based tests, with excellent usability scores (SUS: 84.4±18.5).Conclusions: Depth-sensing technologies demonstrate promising effectiveness for objective fall risk assessments and intervention delivery in older adults.Despite favorable outcomes, limitations include small sample sizes, moderate validity for depth-change tasks, and environmental sensitivity.Future research requires larger randomized controlled trials, algorithm optimization, and expanded inclusion of high-risk populations to establish clinical superiority and inform evidence-based fall prevention guidelines.