Pediatric bed fall computer simulation model: parametric sensitivity analysis

Impact forces in backward falls: Subject-specific video-based rigid body simulation of backward falls

A critical missing component in the study of real-world falls is the ability to accurately determine impact forces resulting from the fall. Subject-specific rigid body dynamic (RBD) models calibrated to video captured falls can quantify impact forces and provide additional insights into injury risk factors. RBD models were developed based on five backward falls captured on surveillance video in long-term care facilities in British Columbia, Canada. Model joint stiffness and initial velocities were calibrated to match the kinematics of the fall and contact forces were calculated. The effect of joint stiffnesses (neck, lumbar spine, hip, and knee joint) on head contact forces were determined by modifying the calibrated stiffness values ±25%. Fall duration, fall trajectories, and maximum velocities showed a close match between fall events and simulations. The maximum value of pelvic velocity difference between Kinovea (an open-source software 2D digitization software) and Madymo multibody modeling was found to be 6% ± 21.58%. Our results demonstrate that neck and hip stiffness values have a non-significant yet large effect on head contact force (t(3) = 1, p = 0.387 and t(3) = 2, p = 0.138), while lower effects were observed for knee stiffness, and the effect of lumbar spine stiffness was negligible. The subject-specific fall simulations constructed from real world video captured falls allow for direct quantification of force outcomes of falls and may have applications in improving the assessment of fall-induced injury risks and injury prevention methods.

Assessment System for Child Head Injury from Falls Based on Neural Network Learning

Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots' frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers' daily falling at home from their parents to evaluate the framework's performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven.

Head biomechanics of video recorded falls involving children in a childcare setting

The objective of this study was to characterize head biomechanics of video-recorded falls involving young children in a licensed childcare setting. Children 12 to < 36 months of age were observed using video monitoring during daily activities in a childcare setting (in classrooms and outdoor playground) to capture fall events. Sensors (SIM G) incorporated into headbands worn by the children were used to obtain head accelerations and velocities during falls. The SIM G device was activated when linear acceleration was ≥ 12 g. 174 video-recorded falls activated the SIM G device; these falls involved 31 children (mean age = 21.6 months ± 5.6 SD). Fall heights ranged from 0.1 to 1.2 m. Across falls, max linear head acceleration was 50.2 g, max rotational head acceleration was 5388 rad/s², max linear head velocity was 3.8 m/s and max rotational head velocity was 21.6 rad/s. Falls with head impact had significantly higher biomechanical measures. There was no correlation between head acceleration and fall height. No serious injuries resulted from falls—only 1 child had a minor injury. In conclusion, wearable sensors enabled characterization of head biomechanics during video-recorded falls involving young children in a childcare setting. Falls in this setting did not result in serious injury.

Imaging of Abusive Head Trauma : A Radiologists' Perspective

Abusive head trauma (AHT) is the most common and serious form of child abuse and a leading cause of traumatic death in infants and young children. The biomechanics of head injuries include violent shaking, blunt impact, or a combination of both. Neuroimaging plays an important role in recognizing and distinguishing abusive injuries from lesions from accidental trauma or other causes, because clinical presentation and medical history are often nonspecific and ambiguous in this age group. Understanding common imaging features of AHT can increase recognition with high specificity for AHT. In this review, we discuss the biomechanics of AHT, imaging features of AHT, and other conditions that mimic AHT.

Impact sites representing potential bruising locations associated with bed falls in children

Bruising can occur as a result of accidental or abusive trauma in children. Bruises are an early sign of child abuse and their locations on the body can be an effective delineator of abusive trauma. Since falls are often reported as false histories in abuse, the ability to predict potential bruising locations in falls could be valuable when attempting to differentiate between abuse and accident. In our study we used an anthropomorphic test device (ATD), a surrogate representing a 12 month old child, adapted with a custom developed force sensing skin to predict potential bruising locations during simulated bed falls. The sensing skin is made of custom resistive force sensors integrated into a conformable skin, adapted to fit the contours of the ATD. The sensing skin measured and displayed recorded force data on a computerized body image mapping system when sensors were activated. Simulated bed fall experiments were performed from two initial positions (FF - facing forward and FR - facing rearward) and two fall heights of 61cm (24 in) and 91cm (36 in) onto a padded carpet impact surface. Findings indicated potential bruising primarily in two planes of the ATD body. The majority of contact regions and greater forces were recorded in one plane, with fewer regions of contact and decreased force exhibited in an adjoining second plane. Additionally, no contact was recorded in the two planes opposite the impact planes. Differences in contact regions were observed for varying heights and initial position. Limitations of ATD biofidelity and soft tissue properties must be considered when interpreting these findings.

The dynamics of electric powered wheelchair sideways tips and falls: experimental and computational analysis of impact forces and injury

Background

To reduce the occurrence of wheelchair falls and to develop effective protection systems, we aimed to quantify sideways tip and fall dynamics of electric power wheelchairs (EPWs). We hypothesized that driving speed, curb height and angle of approach would affect impact forces and head injury risk for wheelchair riders. We further expected that fall dynamics and head injury risk would be greater for unrestrained riders compared to restrained riders.

Methods

Sideways wheelchair tip and fall dynamics were reconstructed using a remotely operated rear wheel drive EPW and a Hybrid III test dummy driving at different approach angles (5 to 63°) over an adjustable height curb (0.30 to 0.41 m) at speeds of 0.6–1.5 m/s. Rigid body dynamics models (Madymo, TASS International, Livonia, MI) were developed in parallel with the experiments to systematically study and quantify the impact forces and the sideways tip or fall of an EPW user in different driving conditions.

Results

Shallower approach angles (25°) (p < 0.05) and higher curbs (0.4 m) (p < 0.05) were the most significant predictors of tipping for restrained passengers. Unrestrained passengers were most affected by higher curbs (0.4 m) (p < 0.005) and fell forward from the upright wheelchair when the approach angle was 60°. Head impact forces were greater in unrestrained users (6181 ± 2372 N) than restrained users (1336 ± 827 N) (p = 0.00053). Unrestrained users had significantly greater head impact severities than restrained users (HIC = 610 ± 634 vs HIC = 29 ± 38, p = 0.00013) and several tip events resulted in HICs > 1000 (severe head injury) in unrestrained users.

Conclusions

Sideways tips and forward falls from wheelchairs were most sensitive to curb height and approach angle but were not affected by driving speed. Sideways tips and falls resulted in impact forces that could result in concussions or traumatic brain injury and require injury prevention strategies. Seat belts eliminated the risk of falling from an upright chair and reduced head impact forces in sideways wheelchair tips in this study; however, their use must be considered within the ethical and legal definitions of restraints.