Alterations in gait patterns often indicate health status, and their analysis enables the diagnosis and assessment of various health conditions. This study aimed to develop a noncontact gait analysis system using red, green, and blue-depth (RGB-D) cameras and to evaluate its potential clinical applicability. A single case study was conducted to assess changes in the gait patterns of a patient with stroke before and after the application of an ankle-foot orthosis. Twenty walking trials were recorded to evaluate the key gait parameters. The custom RGB-D camera-based gait analysis system demonstrated the potential to rapidly quantify key gait parameters in the patient. Compared with normative data, it effectively identified characteristic stroke-related gait impairments such as shorter step lengths and slower gait speeds. However, the intraclass correlation coefficient analysis indicated low measurement reliability. Although the stance time and minimum knee angle on the left and right sides exceeded the standard error of measurement (SEM), no changes exceeded the minimal detectable change (MDC) criteria. Moreover, other gait parameters did not show significant changes beyond SEM or MDC, limiting the interpretability of the results. Therefore, further technological developments and data collection are required to improve test-retest reliability and sensitivity to change.

Objective: Previous studies focused primarily on drivers or front-seat passengers in motor vehicle accidents. Recent research has shown improvements in front-seat occupant protection. This study examined the risk factors contributing to severe injury in rear-seat occupants. Methods: This study was conducted retrospectively. The variables related to the crash data were acquired from the Emergency Department-based Injury In-depth Surveillance registry and electronic medical record of the authors’hospital between 2019 and 2020. Multivariate logistic regression analysis was used to identify the risk factors contributing to severe injury in rear-seat occupant. Results: One hundred and forty rear-seat occupants were analyzed. The occupants were categorized by the abbreviated injury scale into severe (n=39; 27.9%) and non-severe injury (n=101; 72.1%). The collision with a large-size vehicle (odds ratio [OR], 4.278; 95% confidence interval [CI], 1.617-18.000; P=0.015) and fixed objects (OR, 3.459; 95% CI, 1.347-8.883; P=0.049) was independently associated severe injury. Seatbelt use was also an independent risk factor of severe injury (OR, 2.649; 95% CI, 1.178-5.940; P=0.018). Common severe injuries encountered in rear seats were chest (12.1%), spine (9.3%), and abdomen (5.7%). Conclusion In contrast to the fact that seat belt use reduces the severity of injuries, seatbelt use was associated with severe injury in this study. Further research will be needed to assess the effects of seatbelt use. In addition, awareness should be raised about the relationship between the accident mechanism, seatbelt use, and the commonly injured body region.