Accuracy of Kinovea Software in Estimating Body Segment Movements During Falls Captured on Standard Video: Effects of Fall Direction, Camera Perspective and Video Calibration Technique

Falls are a major cause of unintentional injuries. Understanding the movements of the body during falls is important to the design of fall prevention and management strategies, including exercise programs, mobility aids, fall detectors, protective gear, and safer environments. Video footage of real-life falls is increasingly available, and may be used with digitization software to extract kinematic features of falls. We examined the validity of this approach by conducting laboratory falling experiments, and comparing linear and angular positions and velocities measured from 3D motion capture to estimates from Kinovea 2D digitization software based on standard surveillance video (30 Hz, 640x480 pixels). We also examined how Kinovea accuracy depended on fall direction, camera angle, filtering cut-off frequency, and calibration technique. For a camera oriented perpendicular to the plane of the fall (90 degrees), Kinovea position data filtered at 10 Hz, and video calibration using a 2D grid, mean root mean square errors were 0.050 m or 9% of the signal amplitude and 0.22 m/s (7%) for vertical position and velocity, and 0.035 m (6%) and 0.16 m/s (7%) for horizontal position and velocity. Errors in angular measures averaged over 2-fold higher in sideways than forward or backward falls, due to out-of-plane movement of the knees and elbows. Errors in horizontal velocity were 2.5-fold higher for a 30 than 90 degree camera angle, and 1.6-fold higher for calibration using participants’ height (1D) instead of a 2D grid. When compared to 10 Hz, filtering at 3 Hz caused velocity errors to increase 1.4-fold. Our results demonstrate that Kinovea can be applied to 30 Hz video to measure linear positions and velocities to within 9% accuracy. Lower accuracy was observed for angular kinematics of the upper and lower limb in sideways falls, and for horizontal measures from 30 degree cameras or 1D height-based calibration

Injuries from falls by older adults in long-term care captured on video: Prevalence of impacts and injuries to body parts

Background Falls are the leading cause of injuries in older adults. However, most falls in older adults do not cause serious injury, suggesting that older adults may fall in a manner that reduces the likelihood of impact to body sites that are most vulnerable to injury. In this observational study of falls in long-term care (LTC), we tested whether body parts differed in their probability of impact and injury. Methods We recorded and analyzed videos of 2388 falls by 658 LTC residents (mean age 84.0 (SD = 8.1); 56.4% female). We used Linear Mixed Models to test for differences between body parts in the probability of impact and injury, and injury when impacts occurred. Results Injuries were reported in 38.2% of falls, and 85.9% of injuries involved direct impact to the injured body part. Impact occurred most often to the hip/pelvis (probability (standard error) = 0.95 (0.01); p < .001 relative to other body parts), and least often to the head (0.35 (0.01)). Conversely, injury occurred most often to the head (p < .001 relative to other body parts). The probability of injury when impacts occurred was 0.40 (0.01) for the head, and 0.11 or less for all other body parts. Conclusion Our results help to explain why most falls by older adults in LTC do not cause serious injury: residents land on body parts that are the most resilient to injury. The high susceptibility of the head to injury reinforces the need to enhance upper limb protective responses for fall arrest. The dominant role of direct impact as the mechanism of injury supports approaches to attenuate impact forces through strategies like protective clothing and compliant flooring.Applied Science, Faculty ofNon UBCEngineering, School of (Okanagan)ReviewedFacultyResearche