Computational prediction of head-ground impact kinematics in e-scooter falls

Head Impact Location, Speed and Angle from Falls and Trips in the Workplace

Traumatic brain injury (TBI) is a common injury in the workplace. Trips and falls are the leading causes of TBI in the workplace. However, industrial safety helmets are not designed for protecting the head under these impact conditions. Instead, they are designed to pass the regulatory standards which test head protection against falling heavy and sharp objects. This is likely to be due to the limited understanding of head impact conditions from trips and falls in workplace. In this study, we used validated human multi-body models to predict the head impact location, speed and angle (measured from the ground) during trips, forward falls and backward falls. We studied the effects of worker size, initial posture, walking speed, width and height of the tripping barrier, bracing and falling height on the head impact conditions. Overall, we performed 1692 simulations. The head impact speed was over two folds larger in falls than trips, with backward falls producing highest impact speeds. However, the trips produced impacts with smaller impact angles to the ground. Increasing the walking speed increased the head impact speed but bracing reduced it. We found that 41% of backward falls and 19% of trips/forward falls produced head impacts located outside the region of helmet coverage. Next, we grouped all the data into three sub-groups based on the head impact angle: [0°, 30°], (30°, 60°] and (60°, 90°] and excluded groups with small number of cases. We found that most trips and forward falls lead to impact angles within the (30°, 60°] and (60°, 90°] groups while all backward falls produced impact angles within (60°, 90°] group. We therefore determined five representative head impact conditions from these groups by selecting the 75th percentile speed, mean value of angle intervals and median impact location (determined by elevation and azimuth angles) of each group. This led to two representative head impact conditions for trips: 2.7 m/s at 45° and 3.9 m/s at 75°, two for forward falls: 3.8 m/s at 45° and 5.5 m/s at 75° and one for backward falls: 9.4 m/s at 75°. These impact conditions can be used to improve industrial helmet standards.

Assessing head injury risks in electric scooter accidents: A multi-body simulation study with insights into sex differences

E-scooters have become increasingly popular for short-distance travel in urban areas, but this rise in usage also brings about an increased risk of accidents. Studies have shown that approximately 40% of electric scooter accident victims admitted to hospitals suffer head injuries. Therefore, it is crucial to implement safety measures and improve safety systems and equipment to mitigate these risks. One approach to gaining insights into the injuries users face is through simulations using the multi-body method. This method allows for the reconstruction of accidents by modeling and analyzing the dynamic behavior of interconnected bodies. This study aims to assess the impacts on the user's head and the injuries they may sustain in electric scooter accidents using numerical methods. Initially, a reference scenario was established based on a YouTube video, with the assumption that the user was an average-height man. Simulations were conducted for various percentiles, including both males and females. Different velocities were simulated to determine the threshold velocity at which survival becomes practically impossible. Two scenarios were considered: one where the car braked for 0.333 s and another where the distance between the start the braking task and the collision was kept constant. The location of the first head impact on the vehicle was also examined. Injury assessment was conducted using two criteria: Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC). The study found that smaller individuals are more vulnerable to severe injuries, and higher car velocities correlate with more severe user injuries. Furthermore, the location of the first impact varies between genders, with women more likely to experience impacts in the lower part of the windshield, while men tend to experience impacts in the central zone. This study highlights the importance of considering user characteristics and accident dynamics in assessing injury risks associated with e-scooters.

Inherent uncertainty in pedestrian collision reconstruction: How evidence variability affects head kinematics and injury prediction

Reconstructing individual cases from real-world collision data is used as a tool to better understand injury biomechanics and determine injury thresholds. However, real-world data tends to have inherent uncertainty within parameters, such as ranges of impact speed, pre-impact pedestrian stance or pedestrian anthropometric characteristics. The implications of this input parameter uncertainty on the conclusions made from case reconstruction about injury biomechanics and risk is not well investigated, with a 'best-fit' approach more frequently adopted, leaving uncertainty unexplored. This study explores the implications of uncertain parameters in real-world data on the biomechanical kinematic metrics related to head injury risk in reconstructed real-world pedestrian-car collisions. We selected six pedestrian-car cases involving seven pedestrians from the highly detailed GB Road Accident In-Depth Studies (RAIDS) database. The collisions were reconstructed from the images, damage measurements and dynamics available in RAIDS. For each case, we varied input parameters within uncertain ranges and report the range of head kinematic metrics from each case. This includes variations of reconstructed collision scenarios that fits within the constraints of the available evidence. We used a combination of multibody and finite element modelling in Madymo to test whether the effect of input data uncertainty is the same on the initial head-vehicle and latter head-ground impact phase. Finally, we assessed whether the predicted range of head kinematics correctly predicted the injuries sustained by the pedestrian. Varying the inputs resulted in a range of output head kinematic parameters. Real-world evidence such as CCTV footage enabled predicted simulated values to be further constrained, by ruling out unrealistic scenarios which do not fit the available evidence. We found that input data uncertainty had different implications for the initial head-vehicle and latter head-ground impact phase. There was a narrower distribution of kinematics associated with the head-vehicle impact (initial 400 ms of the collision) than in the latter head-ground impact. The mean head-vehicle kinematics were able to correctly predict the presence or absence of both subdural haematoma (using peak rotational acceleration) and skull vault fracture (using peak contact force) in all pedestrians presented. This study helps increase our understanding of the effects of uncertain parameters on head kinematics in pedestrian-car collision reconstructions. Extending this work to a broad range of pedestrian-vehicle collision reconstructions spanning broad population demographics will improve our understanding of injury mechanisms and risk, leading to more robust design of injury prevention measures.

Fatal traffic accidents involving electric scooters in Poland in 2019-2023

With the introduction of mobile applications that allow short-term rentals, electric scooters (e-scooters) are gaining popularity as a means of micromobility in urban areas. The aim of the study was to assess the circumstances and causes of death in traffic accidents involving electric scooters in Poland. The inclusion criteria for the study were met by 9 cases (7 M,2F; mean age: 40.3 years). Accidents usually occur during working days during the warm months, especially during the morning traffic rush hour. Usually, these accidents involved another vehicle (4/9 cases). In addition, the victim was most often the driver (8/9 cases) and rarely uses a helmet (1/9 cases). In only two cases did the test reveal the presence of alcohol in the blood at the time of the accident. In studied cases, head injuries occurred in every case, and injuries to the limbs and chest occurred in more than half of the cases. Within the head, in addition to minor injuries like bruises and epidermal abrasions, skull fractures, and intracranial bleedings predominated. Similar minor injuries were also observed in the extremities, with significant fractures observed only in the lower extremities. Among chest injuries, lung contusions predominated. The most common cause of death was craniocerebral injury (6/9 cases), but there were two deaths each from chest injuries and polytrauma. To increase the safety of e-scooter users, it is recommended that measures be taken to educate users about the potential risks of using the vehicle and that measures be taken to increase the use of helmets, e.g. through legislative action. Further studies involving larger study groups are needed to assess the correlation between potential risk factors and the fatal outcome of the accident.

Pilot Study Investigating Effects of Changing Process Variables on Elastic and Energy-Absorbing Characteristics in Polyurethane/Agglomerated Cork Mix for Use in Micro-Transport Helmet

This pilot investigation identifies the influence that changing the process variables of curing pressure, curing temperature, and mix ratio of a polyurethane/agglomerated cork matrix has on the mechanical properties of energy absorption, Young's modulus of elasticity, and spring stiffness in safety helmets intended for micro-transport riders. The results are compared to expanded polystyrene, a material commonly used in micro-transport helmets. Mechanical testing of the various samples found that, over the range tested, curing pressure had no effect on any of the mechanical properties, while increasing amounts of resin caused a stiffer structure, and increasing curing temperature led to increased energy absorption. Consistent with the elastic modulus findings, all polyurethane/agglomerated cork test samples demonstrated higher median levels of spring stiffness, ranging from 7.1% to 61.9% greater than those found for expanded polystyrene. The sample mixed at a 1.5:1 binder/cork ratio and cured at 40 °C displayed the closest spring stiffness to EPS. While the mechanical properties of the eco-friendly polyurethane/agglomerated cork matrix did not match those of expanded polystyrene, the difference in performance found in this study is promising. Further investigation into process variables could characterise this more ecologically based matrix with equivalent energy-absorbing and structural characteristics, making it equivalent to currently used expanded polystyrene and suitable for use in micro-transport helmets.

Characteristics and Injury Patterns in Traumatic Brain Injury Related to E-Scooter Use in Riga, Latvia: Multicenter Case Series

Background and Objectives: In recent years, electronic scooters (e-scooters) have gained popularity, whether for private use or as a publicly available transportation method. With the introduction of these vehicles, reports of e-scooter-related accidents have surged, sparking public debate and concern. The aim of this study was to analyze the epidemiological data, characteristics, and severity of traumatic brain injury (TBI) related to e-scooter accidents. Materials and Methods: This retrospective case series evaluated patients who were admitted to the three largest neurosurgery clinics in Riga, Latvia, from the time period of April to October in two separate years-2022 and 2023-after e-scooter-related accidents. The data were collected on patient demographics, the time of the accident, alcohol consumption, helmet use, the type of TBI, other related injuries, and the treatment and assessment at discharge. Results: A total of 28 patients were admitted with TBI related to e-scooter use, with a median age of 30 years (Q1-Q3, 20.25-37.25), four individuals under the age of 18, and the majority (64%) being male. In 23 cases, the injury mechanism was falling, in 5 cases, collision. None were wearing a helmet at the time of the injury. Alcohol intoxication was evident in over half of the patients (51.5%), with severe intoxication (>1.2 g/L) in 75% of cases among them. Neurological symptoms upon admission were noted in 50% of cases. All patients had intracranial trauma: 50% had brain contusions, 43% traumatic subdural hematoma, and almost 30% epidural hematoma. Craniofacial fractures were evident in 71% of cases, and there were fractures in other parts of body in three patients. Six patients required emergency neurosurgical intervention. Neurological complications were noted in two patients; one patient died. Conclusions: e-scooter-related accidents result in a significant number of brain and other associated injuries, with notable frequency linked to alcohol influence and a lack of helmet use. Prevention campaigns to raise the awareness of potential risks and the implementation of more strict regulations should be conducted.

Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation

Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the injury biomechanics research field. Understanding the mechanics of a fall can help researchers develop better protective gear and safety measures to reduce the risk of injury. However, little is known about cyclist fall kinematics or dynamics. Therefore, in this study, a video analysis of cyclist falls is performed to investigate common kinematic forms and impact patterns. Furthermore, a pipeline involving deep learning-based human pose estimation and inverse kinematics optimisation is created for extracting human motion from real-world footage of falls to initialise forward dynamics computational human body models. A bracing active response is then optimised for using a genetic algorithm. This is then applied to a case study of a cyclist fall. The kinematic forms characterised in this study can be used to inform initial conditions for computational modelling and injury estimation in cyclist falls. Findings indicate that protective response is an important consideration in fall kinematics and dynamics, and should be included in computational modelling. Furthermore, the novel reconstruction pipeline proposed here can be applied more broadly for traumatic injury biomechanics tasks. The tool developed in this study is available at https://kevgildea.github.io/KinePose/.

Lessons learned? Increasing injury severity of electric-scooter accidents over a period of one year: a monocentric follow-up study at a level 1 trauma center

PURPOSE

After major COVID-19 lockdown measures were suspended in 2021, E-scooter mobility regrew rapidly. In the meantime, multiple studies were published on the potential risks for e-scooter drivers and the necessity for wearing protective equipment. But did the drivers learn their lessons?

METHODS

We observed data of E-scooter-related accidents admitted to the emergency department of a level 1 German trauma center in the year 2021 and compared the data with our previous report (July 2019-July 2020).

RESULTS

N = 97 E-scooter-related accidents were included, marking a 50% increase when compared to the previous observation. Most patients were young adults (28.18 ± 1.13 years) with a notable shift towards a male population (25 vs. 63, p = 0.007). While the injury pattern remained unchanged, injury severity, reflected by a significant increase in shock room treatments (p = 0.005), hospital admissions (p = 0.45), and ICU admissions (p = 0.028), increased. Lastly, we report a higher injury severity of patients driving under the influence of alcohol, expressed by significant differences in hospital admissions, shock room treatments, ICU admissions, intracerebral bleeding (p < 0.0001), and injuries requiring surgery (p = 0.0017).

CONCLUSION

The increase in injury severity and especially the substantial number of accidents due to driving under the influence of alcohol, are alarming for both trauma- and neurosurgeons. As the controversy surrounding the general use of E-scooters will continue, we urge representatives to intensify their efforts regarding prevention campaigns focusing on the potential dangers of E-scooters, especially when driving under the influence of alcohol.

Kinematic responses of child as second rider of electric-two-wheelers under lateral impact with vehicle

Electric-two-wheeler (E2W) related accidents have become a major safety concern on road due to the growing prevalence and the high casualty rate. Most existing studies focus on drivers of the E2W, while ignore the second rider (usually a child) as passenger. This study aims at investigating the kinematic response of the child rider upon vehicle impact and analyzing how motion patterns are influenced by the geometric parameters of the vehicle and E2W. A computational framework was established for the intended task. We modeled the E2W-rider system in Madymo, including an E2W with parametric geometry and two riders, one adult and one child respectively. This study focuses on lateral impact in terms of the accident scenarios, as the case dominates in the field data reports. Vehicle types, seating height of the E2W and sitting position of the child rider were considered as variables in the simulation matrix. Results show that the relative height between child's sitting and vehicle hood front-edge, and the sitting position (back-seated or front-seated) are two main influencing parameters on kinematic responses of child rider. The child rider tends to bounce higher on hood upon impact when sitting above the hood front-edge, while might be laterally pushed away by the car-front when sitting below the hood front-edge. Meanwhile, back-seated child rider is more likely to rise higher and rotate faster upon impact compared to a front-seated one. These findings may guide safe riding and safety countermeasure development for child riders of E2W.

A Numerical Investigation of Rider Injury Risks During Falls Caused by E-Scooter-Stopper Impacts

Within the past decade, injuries caused by electric scooter (e-scooter) crashes have significantly increased. A primary cause is front wheel collisions with a vertical surface such as a curb or object, generically referred to as a "stopper." In this study, various e-scooter-stopper crashes were simulated numerically across different impact speeds, approach angles, and stopper heights to characterize the influence of crash type on rider injury risk during falls. A finite element (FE) model of a standing Hybrid III anthropomorphic test device was used as the rider model after being calibrated against certification test data. Additionally, an FE model of an e-scooter was developed based on reconstructed scooter geometry. Forty-five FE simulations were run to investigate various e-scooter crash scenarios. Test parameters included impact speed (from 3.2 m/s to 11.16 m/s), approach angle (30 deg to 90 deg), and stopper height (52 mm, 101 mm, and 152 mm). Additionally, the perpendicular (90 deg) impact scenarios were run twice: once with Hybrid-III arm activation to mimic a rider attempting to break a fall with their hands and once without this condition. Overall, the risks of serious injury to the rider varied greatly; however, roughly half the impact scenarios indicated serious risk to the rider. This was expected, as the speeds tested were in the upper 25th percentile of reported scooter speeds. The angle of approach was found to have the greatest effect on injury risk to the rider, and was shown to be positively correlated with injury risk. Smaller approach angles were shown to cause the rider to land on their side, while larger approach angles caused the rider to land on their head and chest. Additionally, arm bracing was shown to reduce the risk of serious injury in two thirds of the impact scenarios.

Head impact kinematics and injury risks during E-scooter collisions against a curb

E-scooters as a mode of transportation is rapidly growing in popularity. This study evaluates head impact conditions and injury risk associated with E-scooter crashes. A multibody model of E-scooter falls induced by wheel-curb collision was built and compared with an experimental E-scooter crash test. A total of 162 crash scenarios were simulated to assess the effect of fall conditions (E-scooter initial speed and inclination, obstacle orientation, and user size) on the head impact kinematics. The forehead hit the ground first in 44% of simulations. The average tangential and normal impact speeds were 3.5 m/s and 4.8 m/s respectively. Nearly 100% of simulations identified a risk of concussion (linear acceleration peak >82 g and rotational acceleration peak >6383 rad/s2) and 90% of simulations suggested a risk of severe head injuries (HIC>700). This work provides preliminary data useful for the assessment and design of protective gears.

A Review of Cyclist Head Injury, Impact Characteristics and the Implications for Helmet Assessment Methods

Head injuries are common for cyclists involved in collisions. Such collision scenarios result in a range of injuries, with different head impact speeds, angles, locations, or surfaces. A clear understanding of these collision characteristics is vital to design high fidelity test methods for evaluating the performance of helmets. We review literature detailing real-world cyclist collision scenarios and report on these key characteristics. Our review shows that helmeted cyclists have a considerable reduction in skull fracture and focal brain pathologies compared to non-helmeted cyclists, as well as a reduction in all brain pathologies. The considerable reduction in focal head pathologies is likely to be due to helmet standards mandating thresholds of linear acceleration. The less considerable reduction in diffuse brain injuries is likely to be due to the lack of monitoring head rotation in test methods. We performed a novel meta-analysis of the location of 1809 head impacts from ten studies. Most studies showed that the side and front regions are frequently impacted, with one large, contemporary study highlighting a high proportion of occipital impacts. Helmets frequently had impact locations low down near the rim line. The face is not well protected by most conventional bicycle helmets. Several papers determine head impact speed and angle from in-depth reconstructions and computer simulations. They report head impact speeds from 5 to 16 m/s, with a concentration around 5 to 8 m/s and higher speeds when there was another vehicle involved in the collision. Reported angles range from 10° to 80° to the normal, and are concentrated around 30°-50°. Our review also shows that in nearly 80% of the cases, the head impact is reported to be against a flat surface. This review highlights current gaps in data, and calls for more research and data to better inform improvements in testing methods of standards and rating schemes and raise helmet safety.

Head-ground impact conditions and helmet performance in E-scooter falls

OBJECTIVE

Head injuries are common injuries in E-scooter accidents which have dramatically increased in recent years. The head impact conditions and helmet performance during E-scooter accidents are barely investigated. This study aims to characterize the head-ground impact biomechanics and evaluate bicycle helmet protection in typical E-scooter falls.

METHOD

The finite element (FE) model of a hybrid III dummy riding an E-scooter was developed and validated. The FE model with and without a bicycle helmet was used to reproduce twenty-seven E-scooter falls caused by the collision with a curb, in which different riding speeds (10, 20, and 30 km/h), curb orientations (30, 60, and 90°), and E-scooter orientations (-15, 0, and 15°) were simulated. Head-ground impact velocities and locations were evaluated for the unhelmeted configurations while the helmet performance was evaluated with the reduction of head injury metrics.

RESULTS

E-scooter falls always resulted in an oblique head-ground impact, with 78 % on the forehead. The mean vertical and tangential head-ground impact velocities were respectively 5.7 ± 1.5 m/s and 3.7 ± 2.0 m/s. The helmet significantly (p < 0.1) reduced the head linear acceleration, angular velocity, HIC_36, and BrIC, but not the angular acceleration. However, even with the helmet, the head injury metrics were mostly above the thresholds of severe head injuries.

CONCLUSION

Typical E-scooter falls might cause severe head injuries. The bicycle helmet was efficient to reduce head injury metrics but not to prevent severe head injuries. Future helmet standard evaluations should involve higher impact energy and the angular acceleration assessment in oblique impacts.

Trauma Characteristics Associated with E-Scooter Accidents in Switzerland-A Case Series Study

E-scooters have gained popularity worldwide in the last few years. Due to the increase in users, more accidents related to e-scooters can be observed. The present study aimed to analyse epidemiological data, characteristics, and severity of injuries in patients admitted to a Level I trauma centre in Switzerland (Inselspital Bern, University Hospital Bern) after accidents associated with e-scooters. This retrospective case series evaluated 23 patients who presented to the University Hospital of Bern between 1 of May 2019 and 31 of October 2021 after an e-scooter accident. Data were collected on patient demographics, time and cause of the accident, speed, alcohol consumption, helmet use, type and localisation of injury, number of injuries per patient, and outcome. Men were most frequently affected (61.9%). The mean age was 35.8 (STD 14.8) years. Slightly more than half (52.2%) of all accidents were self-inflicted. Most accidents were reported during the night (7 p.m. to 7 a.m., 60.9%) and in summer (43.5%). Alcohol consumption was reported in 43.5% of cases, with a mean blood alcohol level of 1.4 g/l. Most injuries were observed in the face (25.3%) and head/neck area (20.25%). Skin abrasions (56.5%) and traumatic brain injury (43.5%) were the most common types of traumata in terms of total number of patients. Only in one case it was reported that a protective helmet had been worn. Five patients required hospitalisation and four patients underwent surgery. Three patients underwent emergency orthopaedic surgery, and one patient underwent emergency neurosurgery. E-scooter accidents result in a significant number of facial and head/neck injuries. E-scooter riders would potentially benefit from a helmet to protect them in the event of an accident. Additionally, the results of this study indicate that a significant number of e-scooter accidents in Switzerland occurred under the influence of alcohol. Prevention campaigns to raise awareness of the risks of driving e-scooters under the influence of alcohol could help prevent future accidents.

Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios

As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.