A modelling framework for local thermal comfort assessment related to bicycle helmet use.

Thermal discomfort due to accumulated sweat increasing head skin wettedness may contribute to low wearing rates of bicycle helmets. Using curated data on human head sweating and helmet thermal properties, a modelling framework for the thermal comfort assessment of bicycle helmet use is proposed. Local sweat rates (LSR) at the head were predicted as the ratio to the gross sweat rate (GSR) of the whole body or by sudomotor sensitivity (SUD), the change in LSR per change in body core temperature (Δtre). Combining those local models with Δtre and GSR output from thermoregulation models, we simulated head sweating depending on the characteristics of the thermal environment, clothing, activity, and exposure duration. Local thermal comfort thresholds for head skin wettedness were derived in relation to thermal properties of bicycle helmets. The modelling framework was supplemented by regression equations predicting the wind-related reductions in thermal insulation and evaporative resistance of the headgear and boundary air layer, respectively. Comparing the predictions of local models coupled with different thermoregulation models to LSR measured at the frontal, lateral and medial head under bicycle helmet use revealed a large spread in LSR predictions predominantly determined by the local models and the considered head region. SUD tended to overestimate frontal LSR but performed better for lateral and medial head regions, whereas predictions by LSR/GSR ratios were lower and agreed better with measured frontal LSR. However, even for the best models root mean squared prediction errors exceeded experimental SD by 18-30%. From the high correlation (R > 0.9) of skin wettedness comfort thresholds with local sweating sensitivity reported for different body regions, we derived a threshold value of 0.37 for head skin wettedness. We illustrate the application of the modelling framework using a commuter-cycling scenario, and discuss its potential as well as the needs for further research.

Posture normalisation of 3D body scans.

For product developers that design near-body products, virtual mannequins that represent realistic body shapes, are valuable tools. With statistical shape modelling, the variability of such body shapes can be described. Shape variation captured by statistical shape models (SSMs) is often polluted by posture variations, leading to less compact models. In this paper, we propose a framework that has low computational complexity to build a posture invariant SSM, by capturing and correcting the posture of an instance. The posture-normalised SSM is shown to be substantially more compact than the non-posture-normalised SSM. Practitioner summary: Statistical shape modelling is a technique to map out the variability of (body) shapes. This variability is often polluted by variations in posture. In this paper, we propose a framework to build a posture invariant statistical shape model. Abbreviations: SSM: statistical shape model; 1D: one-dimensional; 3D: three-dimensional; DHM: digital human model; LBS: linear blend skinning; PCA: princial component analysis; PC: principal component; TTR: thumb tip reach.

The effect of the scalp on the effectiveness of bicycle helmets' anti-rotational acceleration technologies.

OBJECTIVE: Medical data has lead to the common understanding that bicycle helmets need to be improved to better protect against brain injuries resulting from rotational acceleration. Although many different technologies exist for reducing rotational acceleration during impacts, the lack of an official testing standard means that their evaluation is based on customized set-ups that may differ and not represent real accident conditions. Previously, the authors have shown that scalp tissue plays an important role during helmet testing by absorbing energy and creating a low friction interface between head and helmet, thus reducing rotational accelerations and velocities. However, no published study has yet examined the effectiveness of anti-rotational helmet technologies in the presence of a biofidelic scalp layer. The objective of this study is to address this gap. METHODS: Three different commercially available helmet models, each one equipped with a different technology, were tested in the presence of scalp tissue, in two different scenarios; with and without the technology present. The effectiveness of each of these technologies is already documented in other studies, but only in the absence of a biofidelic scalp layer. Tests were carried out using HIII headform with porcine scalp attached to the outmost layer. Motion tracking was used to compare the impact kinematics of each helmet model in both scenarios. RESULTS: Results showed that when a biofidelic scalp layer is present, there is no statistical difference between helmet models with and without the anti-rotational technology in terms of rotational acceleration, velocity, relative rotation, impact duration and injury risk. CONCLUSIONS: Results suggest that the presence of the scalp can obscure the functionality of anti-rotational acceleration technologies. This could indicate that the effectiveness of technologies tested in previous studies, which have not tested anti-rotational acceleration technologies in the presence of a realistic scalp layer, may exaggerate the contribution of such technologies if compared with a more biofidelic set-up. The study supports the fact that headforms should be better designed by incorporating artificial skin layers that can better imitate scalp's behavior and, in addition, provides insights for the design of technologies against rotational acceleration.

Heart rate-based nighttime awakening detection.

Sleep fragmentation is a cause of impaired daytime performance. We have developed an algorithm for detection of nighttime awakenings based on heart rate. As much as 15 healthy normal sleepers, 23 +/- 3 years, participated in this study. The dataset contains 33 nights of polysomnographic (PSG) and electrocardiographic (ECG) measurements. After a habituation night, the subjects underwent a reference night without interventions, followed by some nights with interventions. These included noise, light, physical and cognitive interventions. Nighttime awakenings were subdivided in to awakenings (>15 s) and short awakenings (<15 s). The overall number of awakenings was 18.5 (+/-10.5) and short awakenings 13.2 (+/-10.5). The number of nighttime awakenings did not differ significantly between the reference and intervention nights; a repeated measures ANOVA resulted in a p value of 0.66 for awakenings and 0.57 for short awakenings. As much as 5 reference nights were used as training set, 28 as validation set. The algorithm detects the awakening periods with a sensitivity of 80.5% (confidence interval 77.9-82.9%). Heart rate is an adequate measure that allows for detection of nighttime awakenings and hence sleep quality.

Awake 18F-FDG PET Imaging of Memantine-Induced Brain Activation and Test-Retest in Freely Running Mice.

PET scans of the mouse brain are usually performed with anesthesia to immobilize the animal. However, it is desirable to avoid the confounding factor of anesthesia in mouse-brain response. Methods: We developed and validated brain PET imaging of awake, freely moving mice. Head-motion tracking was performed using radioactive point-source markers, and we used the tracking information for PET-image motion correction. Regional 18F-FDG brain uptake in a test, retest, and memantine-challenge study was measured in awake (n = 8) and anesthetized (n = 8) C57BL/6 mice. An awake uptake period was considered for the anesthesia scans. Results: Awake (motion-corrected) PET images showed an 18F-FDG uptake pattern comparable to the pattern of anesthetized mice. The test-retest variability (represented by the intraclass correlation coefficient) of the regional SUV quantification in the awake animals (0.424-0.555) was marginally lower than that in the anesthetized animals (intraclass correlation coefficient, 0.491-0.629) over the different regions. The increased memantine-induced 18F-FDG uptake was more pronounced in awake (+63.6%) than in anesthetized (+24.2%) animals. Additional behavioral information, acquired for awake animals, showed increased motor activity on a memantine challenge (total distance traveled, 18.2 ± 5.28 m) compared with test-retest (6.49 ± 2.21 m). Conclusion: The present method enables brain PET imaging on awake mice, thereby avoiding the confounding effects of anesthesia on the PET reading. It allows the simultaneous measurement of behavioral information during PET acquisitions. The method does not require any additional hardware, and it can be deployed in typical high-throughput scan protocols.

The Importance of the Scalp in Head Impact Kinematics.

The best way to reduce the risk of head injury (up to 69% reduction) is to wear a helmet. In recent years, the improvement of helmet standard tests focused on reproducing realistic impact conditions and including the effect of rotational acceleration. However, less importance has been given to the development of a realistic headform. The goal of this work was to evaluate the role of scalp tissue in head impact kinematics; both with respect to its mechanical properties and with respect to its sliding properties. An EN960 and HIII headform were subjected to linear and oblique impacts, respectively, both with and without porcine scalp attached. Different speeds, impact locations and impact surfaces were tested. Standard linear drop tests (EN960) showed that the scalp reduced the impact energy by up to 68.7% (rear impact). Oblique head impact tests showed how the headform-anvil friction coefficient changes when the HIII is covered with scalp, affecting linear and rotational accelerations. Therefore, the scalp plays an important role in head impacts and it should be realistically represented in headforms used for impact tests and in numerical models of the human head.

Under-reporting bicycle accidents to police in the COST TU1101 international survey: Cross-country comparisons and associated factors.

Police crash reports are often the main source for official data in many countries. However, with the exception of fatal crashes, crashes are often underreported in a biased manner. Consequently, the countermeasures adopted according to them may be inefficient. In the case of bicycle crashes, this bias is most acute and it probably varies across countries, with some of them being more prone to reporting accidents to police than others. Assessing if this bias occurs and the size of it can be of great importance for evaluating the risks associated with bicycling. This study utilized data collected in the COST TU1101 action "Towards safer bicycling through optimization of bicycle helmets and usage". The data came from an online survey that included questions related to bicyclists' attitudes, behaviour, cycling habits, accidents, and patterns of use of helmets. The survey was filled by 8655 bicyclists from 30 different countries. After applying various exclusion factors, 7015 questionnaires filled by adult cyclists from 17 countries, each with at least 100 valid responses, remained in our sample. The results showed that across all countries, an average of only 10% of all crashes were reported to the police, with a wide range among countries: from a minimum of 0.0% (Israel) and 2.6% (Croatia) to a maximum of a 35.0% (Germany). Some factors associated with the reporting levels were type of crash, type of vehicle involved, and injury severity. No relation was found between the likelihood of reporting and the cyclist's gender, age, educational level, marital status, being a parent, use of helmet, and type of bicycle. The significant under-reporting - including injury crashes that do not lead to hospitalization - justifies the use of self-report survey data for assessment of bicycling crash patterns as they relate to (1) crash risk issues such as location, infrastructure, cyclists' characteristics, and use of helmet and (2) strategic approaches to bicycle crash prevention and injury reduction.

Ergonomic design of an EEG headset using 3D anthropometry.

Although EEG experiments over the past decades have shown numerous applications for brain-computer interfacing (BCI), there is a need for user-friendly BCI devices that can be used in real-world situations. 3D anthropometry and statistical shape modeling have been shown to improve the fit of devices such as helmets and respirators, and thus they might also be suitable to design BCI headgear that better fits the size and shape variation of the human head. In this paper, a new design method for BCI devices is proposed and evaluated. A one-size-fits-all BCI headset frame is designed on the basis of three digital mannequins derived from a shape model of the human head. To verify the design, the geometric fit, stability and repeatability of the prototype were compared to an EEG cap and a commercial BCI headset in a preliminary experiment. Most design specifications were met, and all the results were found to be similar to those of the commercial headset. Therefore, the suggested design method is a feasible alternative to traditional anthropometric design for BCI headsets and similar headgear.

Evaluation of an anthropometric shape model of the human scalp.

This paper presents the evaluation a 3D shape model of the human head. A statistical shape model of the head is created from a set of 100 MRI scans. The ability of the shape model to predict new head shapes is evaluated by considering the prediction error distributions. The effect of using intuitive anthropometric measurements as parameters is examined and the sensitivity to measurement errors is determined. Using all anthropometric measurements, the average prediction error is 1.60 ± 0.36 mm, which shows the feasibility of the new parameters. The most sensitive measurement is the ear height, the least sensitive is the arc length. Finally, two applications of the anthropometric shape model are considered: the study of the male and female population and the design of a brain-computer interface headset. The results show that an anthropometric shape model can be a valuable tool for both research and design.

Spatial differences in sensible and latent heat losses under a bicycle helmet.

This research aims at quantifying spatial gradients in skin temperature and sweat production under a bicycle helmet. Distribution of sweat production, skin temperature and air temperature was measured at different positions under a bicycle helmet on five male and four female test persons. Effort level was 100 and 150 watt for men (low and high effort level) and 80 and 120 W for women (low and high effort level). Skin temperatures were found to be spatially different (P < 0.05): frontal and lateral region varied 4.6 degrees C at low effort level and 5.3 degrees C at high effort level. Sweat production was found to be not significantly different (P > 0.05). Finally, air temperature variations were found to be spatially different (P < 0.05). Average air temperature differed 2.3 degrees C between lateral and frontal region at high effort level and 2.7 degrees C at low effort level. The results of this research can be used to help designing helmets with better thermal comfort.