The introduction of exoskeletons in industry has focused on improving worker safety. Exoskeletons have the objective of decreasing the risk of injury or fatigue when performing physically demanding tasks. Exoskeletons’ effect on the muscles is one of the most common focuses of their assessment. The present study aimed to analyze the muscle interactions generated during load-handling tasks in laboratory conditions with and without a passive lumbar exoskeleton. The electromyographic data of the muscles involved in the task were recorded from twelve participants performing load-handling tasks. The correlation coefficient, coherence coefficient, mutual information, and multivariate sample entropy were calculated to determine if there were significant differences in muscle interactions between the two test conditions. The results showed that muscle coordination was affected by the use of the exoskeleton. In some cases, the exoskeleton prevented changes in muscle coordination throughout the execution of the task, suggesting a more stable strategy. Additionally, according to the directed Granger causality, a trend of increasing bottom-up activation was found throughout the task when the participant was not using the exoskeleton. Among the different variables analyzed for coordination, the most sensitive to changes was the multivariate sample entropy.

In the car industry, manikins are commonly used to evaluate the ergonomics and safety of vehicle interiors. However, they are often simplified and unrealistic for certain purposes. One major limitation is that manikins tend to be based on average human measurements which can diminish accuracy when assessing interaction. Although many large-scale 3D scanning campaigns have been conducted, the transfer of this information to automotive innovation tools is limited. To date, there is no direct methodology that introduces 3D scans of real people into the design software tools commonly used in the automotive sector. This study presents a novel methodological approach to capturing, processing and generating digital human models for simulation. Relevant contributions include the design of a protocol for scanning people in postures that take autonomous driving use cases into consideration, as well as the development of advanced processing algorithms that solve the presence of occlusions due to the interaction between the body and seat. Finally, we have also created tools to mathematically transform and convert mesh objects into files to render them interoperable within design environments. Furthermore, the methodology has been validated using 12 participants representing different morphotypes of the target population.

The introduction of exoskeletons in the industry has focused on improving worker safety. Exoskeletons have the objective of decreasing the risk of injury or fatigue when performing physically demanding tasks. The exoskeleton’s effects on the muscles is one of the most common focus in the assessments. The present study aims to analyse the muscle interactions generated by using a passive lumbar exoskeleton during load-handling tasks in laboratory conditions with and without an exoskeleton. Electromyographic data of the muscles involved in the task were recorded from twelve participants performing load-handling tasks. Correlation Coefficient, Coherence Coefficient, Mutual Information, and Multivariate Sample Entropy were calculated to determine if there were significant differences in muscle interactions between the two test conditions. Results showed statistically significant differences for all pairs of muscles and indicated that the use of the exoskeleton implied more constant values throughout the exercise. The Directed Conditional Granger Causality was obtained to study the directionality of the interactions, with significant differences in two muscle pairs Gluteus-Quadriceps and Gluteus-Lumbar in the gravity-positive direction in both cases. In conclusion, EMG parameters chosen appear to be appropriate measurements for studying the exoskeleton effects over muscle couplings.

Emotion Recognition is an emerging area in the field of automotive research focused on improving the driving experience and driver behavior. In the framework of automated vehicles, it is vitally important to detect and understand the emotional state of occupants in order to provide them with tailored support and to develop corrective actions to increase their acceptance of such vehicles. The H2020 SUaaVE project aims to develop an emotional model able to estimate passenger state through the analysis of their physiological signals. 50 drivers have experienced different automated driving scenarios with our HAV driving simulator, gathering their biometric measurements and behavior. The application of the emotional model shows significant differences in the emotion experienced by the participants in terms of valence and arousal across the different simulated scenarios.

This preliminary study focuses on the development of a medical image segmentation algorithm based on artificial intelligence for calculating bone growth in contact with metallic implants. Two databases consisting of computerized microtomography images have been used throughout this work: 100 images for training and 196 images for testing. Both bone and implant tissue were manually segmented in the training data set. The type of network constructed follows the U-Net architecture, a convolutional neural network explicitly used for medical image segmentation. In terms of network accuracy, the model reached around 98%. Once the prediction was obtained from the new data set (test set), the total number of pixels belonging to bone tissue was calculated. This volume is around 15% of the volume estimated by conventional techniques, which are usually overestimated. This method has shown its good performance and results, although it has a wide margin for improvement, modifying various parameters of the networks or using larger databases to improve training.

Manual material handling tasks in industry cause work-related musculoskeletal disorders. Exoskeletons are being introduced to reduce the risk of musculoskeletal injuries. This study investigated the effect of using a passive lumbar exoskeleton in terms of moderate ergonomic risk. Eight participants were monitored by electromyogram (EMG) and motion capture (MoCap) while performing tasks with and without the lumbar exoskeleton. The results showed a significant reduction in the root mean square (VRMS) for all muscles tracked: erector spinae (8%), semitendinosus (14%), gluteus (5%), and quadriceps (10.2%). The classic fatigue parameters showed a significant reduction in the case of the semitendinosus: 1.7% zero-crossing rate, 0.9% mean frequency, and 1.12% median frequency. In addition, the logarithm of the normalized Dimitrov's index showed reductions of 11.5, 8, and 14% in erector spinae, semitendinosus, and gluteus, respectively. The calculation of range of motion in the relevant joints demonstrated significant differences, but in almost all cases, the differences were smaller than 10%. The findings of the study indicate that the passive exoskeleton reduces muscle activity and introduces some changes of strategies for motion. Thus, EMG and MoCap appear to be appropriate measurements for designing an exoskeleton assessment procedure.

Emotion recognition is crucial to increase user acceptance in autonomous driving. SUaaVE project aims to formulate ALFRED, defined as the human-centered artificial intelligence to humanize the vehicle actions by estimating the emotions felt by the passengers and managing preventive or corrective actions, providing tailored support. This paper presents the development of an emotional model able to estimate the values of valence (how negative or positive a stimulus is) and arousal (the level of excitement) from the analysis of physiological signals. The model has been validated with an experimental test simulating different driving scenarios of autonomous vehicles. The results found that driving mode can influence the emotional state felt by the passengers. Further exploration of this emotional model is therefore advised to detect on board experiences and to lead to new applications in the framework of empathic vehicles.

Anthropometric data can be measured manually, through traditional methods, or obtained from a 3D body scan. In both cases, anthropometric dimensions are measured in a static posture (e.g. standing, sitting) however, people interact with products and environments in movement. Anthropometry applied to the ergonomic design of spaces (e.g. workplace, cockpits) includes measurements of reaches and considers dynamic anthropometry, that is the functional ranges of movements of the limbs. In the case of wearables, products that are worn in contact to the body (e.g. clothing, protective gear), the variability of the shape and dimensions during the moment is crucial information to achieve a good fitting, comfort and performance. The appearance of new 4D body scanning technology enables the generation of digital human models in movement which reproduce the actual body shape in motion. Anthropometry in movement is a new category of body metrics that can be obtained from a sequence of scans. In this paper, the variability of eight anthropometric dimensions (neck to waist length, back length, arm length, thigh girth, crotch length, arm girth, waist girth and hip girth) is analyzed in different movements. For this purpose, ten subjects, with a variety of morphotypes, have been measured performing different movements using a 4D scanning system. The methodology to process the sequence of body scans is described to obtain automatically anatomical references of the anthropometric measurements along the movement. The results presented show the evolution of the eight anthropometric dimensions during the movement for the different subjects and movements. The mean ranges of variation are also reported and can reach values between 2-14 cm that will be relevant information for wearable design. Anthropometric dimensions in movement is a new body metric that require further research to establish new protocols, better anthropometric definitions and the creation of new datasets.

"One of the main causes of lack of acceptance in innovation is ignoring the needs and preferences of potential customers in the development phases. In the case of the connected automated vehicle (CAV), there is an important degree of user skepticism based on the awareness of the complexity and the risks of this technology. Public acceptance is a multi-faceted construct, tightly related to emotional processes and trust in a new technology, beyond the accomplishment of functional performance. However, the current approach based on the technology push threatens social viability of innovative technology like CAV, as it creates a gap between the well-thought technical reliability and public acceptance. The H2020 project SUaaVE (SUpporting acceptance of automated VEhicle) aims to make a change in the current situation of public acceptance of CAV. SUaaVE formulates a new concept called ALFRED, a human centered artificial intelligence to humanize the vehicle actions by understanding the emotions of the passengers of the CAV while also managing corrective actions in vehicle for enhancing trip experience. In line with this research, the H2020 project DIAMOND (Revealing fair and actionable knowledge from data to support women’s inclusion in transport systems) seeks to generate knowledge from data for more inclusive and efficient transport systems, being one of the main objectives to enhance the acceptance of women using and driving automated vehicles. This paper presents the main results obtained of two experimental tests carried out in each of the two projects. More than 50 subjects participated in each test, experiencing different scenarios of L4 automated vehicles in an immersive dynamic driving simulator. In both tests, the physiological response of the participants was measured (HR, EDA and facial EMG), considering other additional biometrics (breathing rate, temperature, sweating) and behavioral (facial expression, blinking, etc.) in the case of SUaaVE project. In case of DIAMOND, the experimentation was focused on estimating the participant emotional state, arousal and valence, by HR, EDA and facial EMG in autonomous driving scenarios. The goal was to explore the influence of gender and related intersectional variables in the emotional response that could lead to autonomous vehicle acceptance. The analysis of the test in SUaaVE has allowed a scientific advance defining an emotional model based on the contextual factors involving the experience in the Ego Car - The trip purpose (work travel, day shift, holidays, etc.) and the state of road (density of cars, weather conditions, safety envelop, etc.) – together with complete monitoring the passenger’s physiology and behavior. The approach presented will facilitate that automated vehicles are able to understand how we feel and use such information to make system more empathic, responding to the occupant emotions in real time. This will allow to OEMs and Tier 1 suppliers a detailed characterization of the passenger needs, enabling them the development of strategies to enhance the in-cabin experiences and, in case of DIAMOND project, include needs in women in CAV deployment strategies."

Users' acceptance is one of the predominant barriers of connected and automated vehicles (CAVS), which should be addressed at the highest priority. Loss of control, perceived safety and therefore lack of trust are some of the main aspects that lead to scepticism about the adoption of this technology. Addressing this issue, the H2020 project SUaaVE seeks to enhance the acceptance of CAVS through understanding the passengers' state and managing corrective actions in vehicle for enhancing trip experience. The research to understand passenger emotions is mainly based on experimental tests consisting in immersive experiences with subject's participation in a simulated CAV, specifically adapted to SUaaVE research purposes. This paper present different strategies to obtain realistic simulations with high levels of immensity in these tests using a dynamic platform with the objective of studying the emotional reaction of the subjects in representative scenarios and events within the framework of CA Vs.

One of the main reasons for contested innovations to fail is the negligence of societal needs and public acceptance in due time in the development phase. In the specific case of the connected automated vehicle (CAV), there is an important degree of scepticism based on the awareness of the complexity and the risks of this technology. The SUaaVE project aims to make a change in the current situation of public acceptance of CAV by enhancing synergies amongst social science, human factors research and automotive market. The main ambition in SUaaVE is the formulation of ALFRED, defined as a human centred artificial intelligence to humanize the vehicle actions by understanding the emotions of the passengers of the CAV and managing corrective actions in vehicle for enhancing trip experience.

One critical factor of success and user acceptance in connected automated vehicles (CAVs) is trust in technology, being the main obstacle that remains from a customer’s perspective. Trust in automated systems is based on feelings of safety and acceptance, being the emotional process the most influential aspect. One of the main ambitions of SUaaVE project (SUpporting acceptance of automated VEhicle) is to develop an emotional model to understand the passenger’s state during the trip (in Real-Time), based on body biometrics, allowing to adapt the vehicle features to enhance the in-vehicle user experience, while increasing trust, and therefore acceptance. This research addressed a initial experiments to identify changes in the emotional state of the occupants in different driving experiences (in a driving simulator and in real conditions) by measuring and analysing the physiological signals of the participants, serving as a basis for the generation of the emotional model. The results showed that it is possible to estimate the level of Arousal and Valence of the participants during the journey from the analysis of ECG, EMG and GSR signals. These results have positive implications for the automobile industry facilitating a better integration of human factor in the deployment of

Over the years, the industry’s interest in using external support devices, such as exoskeletons, is increasing. They are introduced as a new technique for improving the conditions of workers and for reducing the risk of musculoskeletal injuries. An investigation of muscle activity, Jonsson’s (Jonsson, 1982) ergonomic acceptance ranges, and shoulder range of motion was conducted with a sample of 12 workers using an upper extremity exoskeleton in an automotive assembly line. The operators performed continuous cycles of dynamic overhead work consisting of the assembly of the car body at the underside of the car making use of pneumatic screwdrivers. The EMGs (anterior part of deltoid, trapezius, latissimus dorsi and erector spinae) were measured for the muscle activity analysis on the one hand, and for the ergonomics study on the other hand. The latter consisted of an approach based on Jonsson’s work, that establishes acceptance thresholds of cumulative percentage of maximum voluntary contraction of muscle activity (%MVC) in a work cycle. The joint angles motion capture was carried out by measuring the angles of the neck, back, and arms joints. All measurements were performed during experimental sessions with and without an exoskeleton. The key findings show reductions of 34% and 18% of the deltoid and the trapezius muscular activities, respectively, which in turn could lead to a reduction of discomfort and fatigue. The erector spinae and latissimus dorsi muscles were not significantly affected by exoskeleton. The values of muscular activity were also represented over Jonsson’s acceptance areas. Referring to the posture, some differences were found in the range of movement of back, neck, and arms owing to the use of the exoskeleton; however, the differences were smaller than 5% in all cases.

In this contribution, large SiPM arrays (8 x 8 elements of 6 x 6 mm(2) each) are processed with an ASIC-based readout and coupled to a monolithic LYSO crystal to explore their potential use for TOF-PET applications. The aim of this work is to study the integration of this technology in the development of clinical PET systems reaching sub-300 ps coincidence resolving time (CRT). The SiPM and readout electronics have been evaluated first, using a small size 1.6 mm (6 mm height) crystal array (32 x 32 elements). All pixels were well resolved and they exhibited an energy resolution of about 20% (using Time-over-Threshold methods) for the 511 keV photons. Several parameters have been scanned to achieve the optimum readout system performance, obtaining a CRT as good as 330 +/- 5 ps FWHM. When using a black-painted monolithic block, the spatial resolution was measured to be on average 2.6 +/- 0.5 mm, without correcting for the source size. Energy resolution appears to be slightly above 20%. CRT measurements with the monolithic crystal detector were also carried out. Preliminary results as well as calibration methods specifically designed to improve timing performance, are being analyzed in the present manuscript. (C) 2017 Elsevier B.V. All rights reserved.

In this work we are describing a novel approach to the scintillator crystal configuration as used in nuclear medicine imaging. Our design is related to the coupling in one PET module of the two separate crystal configurations used so far there: monolithic and crystal arrays. The particular design we have studied is based on a two-layer scintillator approach (hybrid) composed of a monolithic LYSO crystal (5–6 mm thickness) and a LYSO crystal array with 4–5 mm height (0.8 and 1 mm pixels). We show here the detector block performance, in terms of spatial, energy and DOI information, to be used as a module in the design of PET scanners. The design we propose allows one to achieve accurate three-dimensional spatial resolution (including DOI information) while assuring high detection efficiency at reasonable cost. Moreover, the proposed design improves the spatial response uniformity across the whole detector module, and especially at the edge region. The crystal arrays are mounted in the front and were well resolved. The monolithic crystal inserted between crystal array and the photosensor, provided measured FWHM resolution as good as 1.5–1.7 mm including the 1 mm source size. The monolithic block achieved a DOI resolution (FWHM) nearing 3 mm. We compared these results with an approach in which we use a single monolithic block with total volume equals to the hybrid approach. In general, comparable performances were obtained.

The aim of this work is to show the potential capabilities of monolithic-based LYSO crystals, coupled to large SiPM arrays to be considered as detector blocks for TOF-PET scanners. An ASIC read-out with accurate timing capabilities is used to independently process each SIPM element. Several studies have been carried out using both crystal arrays and monolithic blocks showing an overall good performance. The most relevant parameters evaluated in this work are: spatial, energy and time resolutions. We obtained coincidence resolving times as good as 340 ps FWHM for one to one coupling in crystal arrays and 1.2 ns FWHM using a monolithic block in coincidence with a 1 pixel reference detector.

The main aim of this work is to provide a method to retrieve the intrinsic resolution of detector blocks based on monolithic crystals in a fully assembled scanner. This method suggests a software collimation to the original data. The results are compared with the traditional approach of separating two detector blocks far enough, resulting in geometrical collimation.An empirical equation has been deduced to fit the experimental data in which the detector intrinsic resolution follows a Gaussian distribution and the contribution of the source, given the small size of 0.25 mm in diameter, follows a Lorentzian profile. The experiments resulted in an average detector intrinsic spatial resolution of 0.6 mm FWHM, with a standard deviation error of 0.1 mm. These tests show a method to determine the intrinsic resolution of monolithic-based detector blocks, once assembled in the PET system, with high accuracy.