Modality-Independent Effect of Gravity in Shaping the Internal Representation of 3D Space for Visual and Haptic Object Perception.

Visual and haptic perceptions of 3D shape are plagued by distortions, which are influenced by nonvisual factors, such as gravitational vestibular signals. Whether gravity acts directly on the visual or haptic systems or at a higher, modality-independent level of information processing remains unknown. To test these hypotheses, we examined visual and haptic 3D shape perception by asking male and female human subjects to perform a "squaring" task in upright and supine postures and in microgravity. Subjects adjusted one edge of a 3D object to match the length of another in each of the three canonical reference planes, and we recorded the matching errors to obtain a characterization of the perceived 3D shape. The results show opposing, body-centered patterns of errors for visual and haptic modalities, whose amplitudes are negatively correlated, suggesting that they arise in distinct, modality-specific representations that are nevertheless linked at some level. On the other hand, weightlessness significantly modulated both visual and haptic perceptual distortions in the same way, indicating a common, modality-independent origin for gravity's effects. Overall, our findings show a link between modality-specific visual and haptic perceptual distortions and demonstrate a role of gravity-related signals on a modality-independent internal representation of the body and peripersonal 3D space used to interpret incoming sensory inputs.

A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes.

Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents' consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations.

Non-Intrusive Contact Respiratory Sensor for Vehicles.

In this work, we propose a low-cost solution capable of collecting the driver's respiratory signal in a robust and non-intrusive way by contact with the chest and abdomen. It consists of a microcontroller and two piezoelectric sensors with their respective 3D printed plastic housings attached to the seat belt. An iterative process was conducted to find the optimal shape of the sensor housing. The location of the sensors can be easily adapted by sliding them along the seat belt. A few participants took part in three test sessions in a driving simulator. They had to perform various activities: resting, deep breathing, manual driving, and a non-driving-related task during automated driving. The subjects' breathing rates were calculated from raw data collected with a reference chest belt, each sensor alone, and the fusion of the two. Results indicate that respiratory rate could be assessed from a single sensor located on the chest with an average absolute error of 0.92 min-1 across all periods, dropping to 0.13 min-1 during deep breathing. Sensor fusion did not improve system performance. A 4-pole filter with a cutoff frequency of 1 Hz emerged as the best option to minimize the error during the different periods. The results suggest that such a system could be used to assess the driver's breathing rate while performing various activities in a vehicle.

A dataset on the physiological state and behavior of drivers in conditionally automated driving.

This dataset contains data of 346 drivers collected during six experiments conducted in a fixed-base driving simulator. Five studies simulated conditionally automated driving (L3-SAE), and the other one simulated manual driving (L0-SAE). The dataset includes physiological data (electrocardiogram (ECG), electrodermal activity (EDA), and respiration (RESP)), driving and behavioral data (reaction time, steering wheel angle, …), performance data of non-driving-related tasks, and questionnaire responses. Among them, measures from standardized questionnaires were collected, either to control the experimental manipulation of the driver's state, or to measure constructs related to human factors and driving safety (drowsiness, mental workload, affective state, situation awareness, situational trust, user experience). In the provided dataset, some raw data have been processed, notably physiological data from which physiological indicators (or features) have been calculated. The latter can be used as input for machine learning models to predict various states (sleep deprivation, high mental workload, ...) that may be critical for driver safety. Subjective self-reported measures can also be used as ground truth to apply regression techniques. Besides that, statistical analyses can be performed using the dataset, in particular to analyze the situational awareness or the takeover quality of drivers, in different states and different driving scenarios. Overall, this dataset contributes to better understanding and consideration of the driver's state and behavior in conditionally automated driving. In addition, this dataset stimulates and inspires research in the fields of physiological/affective computing and human factors in transportation, and allows companies from the automotive industry to better design adapted human-vehicle interfaces for safe use of automated vehicles on the roads.

Carrying a passenger and relaxation before driving: Classification of young drivers' physiological activation.

Drivers are often held responsible for road crashes. Previous research has shown that stressors such as carrying passengers in the vehicle can be a source of accidents for young drivers. To mitigate this problem, this study investigated whether the presence of a passenger behind the wheel can be predicted using machine learning, based on physiological signals. It also addresses the question whether relaxation before driving can positively influence the driver's state and help controlling the potential negative consequences of stressors. Sixty young participants completed a 10-min driving simulator session, either alone or with a passenger. Before their driving session, participants spent 10 min relaxing or listening to an audiobook. Physiological signals were recorded throughout the experiment. Results show that drivers experience a higher increase in skin conductance when driving with a passenger, which can be predicted with 90%-accuracy by a k-nearest neighbors classifier. This might be a possible explanation for increased risk taking in this age group. Besides, the practice of relaxation can be predicted with 80% accuracy using a neural network. According to the statistical analysis, the potential beneficial effect of relaxation did not carry out on the driver's physiological state while driving, although machine learning techniques revealed that participants who exercised relaxation before driving could be recognized with 70% accuracy. Analysis of physiological characteristics after classification revealed several relevant physiological indicators associated with the presence of a passenger and relaxation.

PEGASO e-Diary: User Engagement and Dietary Behavior Change of a Mobile Food Record for Adolescents.

Background: Obesity amongst children and adolescents is becoming a major health problem globally and mobile food records can play a crucial role in promoting healthy dietary habits. Objective: To describe the methodology for the implementation of the e-Diary mobile food record, to assess its capability in promoting healthy eating habits, to evaluate the factors associated with its usage and engagement. Methods: This is a descriptive study that compared the characteristics of participants engaged in the e-Diary, which was part of the PEGASO project in which an app to provide proactive health promotion was given to 365 students at 4 European sites enrolled during October to December 2016: England (UK), Scotland (UK), Lombardy (Italy), and Catalonia (Spain). The e-Diary tracked the users' dietary habits in terms of food groups, dietary indexes, and 6 dietary target behaviors relating to consumption of: fruit; vegetable; breakfast; sugar-sweetened beverages; fast-food; and snacks. The e-Diary provided also personalized suggestions for the next meal and gamification. Results: The e-Diary was used for 6 months by 357 adolescents (53.8% females). The study showed that females used the e-Diary much more than males (aOR 3.8, 95% CI 1.6-8.8). Participants aged 14 years were more engaged in the e-Diary than older age groups (aOR 5.1, 95% CI 1.4-18.8) as were those with a very good/excellent self-perceived health status compared to their peers with fair/poor health perception (aOR 4.2, 95% CI 1.3-13.3). Compared to the intervention sites, those living in Catalonia (aOR 13.2 95% CI 2.5-68.8) were more engaged. In terms of behavior change, a significant positive correlation between fruit (p < 0.0001) and vegetables (p = 0.0087) intake was observed in association with increased engagement in the e-Diary. Similarly, adolescents who used the app for more than 2 weeks had significantly higher odds of not skipping breakfast over the study period (aOR 2.5, 95% CI 1.0-6.3). Conclusions: The users highly engaged with the e-Diary were associated with improved dietary behaviors: increased consumption of fruit and vegetables and reduced skipping of breakfast. Although the overall usage of the e-Diary was high during the first weeks, it declined thereafter. Future applications should foster user engagement, particularly targeting adolescents at high risk. Clinical Trial Registration: https://www.clinicaltrials.gov/, identifier: NCT02930148.

Locomotor illusions are generated by perceptual body-environment organization.

While one is walking, the stimulation by one's body forms a structure with the stimulation by the environment. This locomotor array of stimulation corresponds to the human-environment relation that one's body forms with the environment it is moving through. Thus, the perceptual experience of walking may arise from such a locomotor array of stimulation. Humans can also experience walking while they are sitting. In this case, there is no stimulation by one's walking body. Hence, one can experience walking although a basic component of a locomotor array of stimulation is missing. This may be facilitated by perception organizing the sensory input about one's body and environment into a perceptual structure that corresponds to a locomotor array of stimulation. We examined whether locomotor illusions are generated by this perceptual formation of a locomotor structure. We exposed sixteen seated individuals to environmental stimuli that elicited either the perceptual formation of a locomotor structure or that of a control structure. The study participants experienced distinct locomotor illusions when they were presented with environmental stimuli that elicited the perceptual formation of a locomotor structure. They did not experience distinct locomotor illusions when the stimuli instead elicited the perceptual formation of the control structure. These findings suggest that locomotor illusions are generated by the perceptual organization of sensory input about one's body and environment into a locomotor structure. This perceptual body-environment organization elucidates why seated human individuals experience the sensation of walking without any proprioceptive or kinaesthetic stimulation.

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation.

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance.

Conversational Agents for Health and Well-being Across the Life Course: Protocol for an Evidence Map.

BACKGROUND: Conversational agents, which we defined as computer programs that are designed to simulate two-way human conversation by using language and are potentially supplemented with nonlanguage modalities, offer promising avenues for health interventions for different populations across the life course. There is a lack of open-access and user-friendly resources for identifying research trends and gaps and pinpointing expertise across international centers. OBJECTIVE: Our aim is to provide an overview of all relevant evidence on conversational agents for health and well-being across the life course. Specifically, our objectives are to identify, categorize, and synthesize-through visual formats and a searchable database-primary studies and reviews in this research field. METHODS: An evidence map was selected as the type of literature review to be conducted, as it optimally corresponded to our aim. We systematically searched 8 databases (MEDLINE; CINAHL; Web of Science; Scopus; the Cochrane, ACM, IEEE, and Joanna Briggs Institute databases; and Google Scholar). We will perform backward citation searching on all included studies. The first stage of a double-stage screening procedure, which was based on abstracts and titles only, was conducted by using predetermined eligibility criteria for primary studies and reviews. An operational screening procedure was developed for streamlined and consistent screening across the team. Double data extraction will be performed with previously piloted data collection forms. We will appraise systematic reviews by using A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2. Primary studies and reviews will be assessed separately in the analysis. Data will be synthesized through descriptive statistics, bivariate statistics, and subgroup analysis (if appropriate) and through high-level maps such as scatter and bubble charts. The development of the searchable database will be informed by the research questions and data extraction forms. RESULTS: As of April 2021, the literature search in the eight databases was concluded, yielding a total of 16,351 records. The first stage of screening, which was based on abstracts and titles only, resulted in the selection of 1282 records of primary studies and 151 records of reviews. These will be subjected to second-stage screening. A glossary with operational definitions for supporting the study selection and data extraction stages was drafted. The anticipated completion date is October 2021. CONCLUSIONS: Our wider definition of a conversational agent and the broad scope of our evidence map will explicate trends and gaps in this field of research. Additionally, our evidence map and searchable database of studies will help researchers to avoid fragmented research efforts and wasteful redundancies. Finally, as part of the Harnessing the Power of Conversational e-Coaches for Health and Well-being Through Swiss-Portuguese Collaboration project, our work will also inform the development of an international taxonomy on conversational agents for health and well-being, thereby contributing to terminology standardization and categorization. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/26680.

Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age.

We implement the dynamical Ising model on the large-scale architecture of white matter connections of healthy subjects in the age range 4-85 years, and analyze the dynamics in terms of the synergy, a quantity measuring the extent to which the joint state of pairs of variables is projected onto the dynamics of a target one. We find that the amount of synergy in explaining the dynamics of the hubs of the structural connectivity (in terms of degree strength) peaks before the critical temperature, and can thus be considered as a precursor of a critical transition. Conversely, the greatest amount of synergy goes into explaining the dynamics of more central nodes. We also find that the aging of structural connectivity is associated with significant changes in the simulated dynamics: There are brain regions whose synergy decreases with age, in particular the frontal pole, the subcallosal area, and the supplementary motor area; these areas could then be more likely to show a decline in terms of the capability to perform higher order computation (if structural connectivity was the sole variable). On the other hand, several regions in the temporal cortex show a positive correlation with age in the first 30 years of life, that is, during brain maturation.

Teenagers' Usage of a Mobile-Wearable-Cloud Platform to Promote Healthy Lifestyles: the PEGASO Experience.

In contemporary society, non-communicable diseases linked to unhealthy lifestyles, such as obesity, are on the rise with a major impact on global deaths. Prevention is the new frontier, promising to increase life expectancy and quality, while reducing costs related to healthcare. The PEGASO project developed a mobile ecosystem where the digital Companion aims at empowering teenagers in the adoption of healthy lifestyles. The pilot study conducted in three European countries (Spain, UK and Italy) shows a good acceptance of the system and that teenagers are keen to use mobile technology to improve their lifestyle, although wearable devices did not engage the young users.

Multiscale analysis of short term heart beat interval, arterial blood pressure, and instantaneous lung volume time series.

MOTIVATIONS: Physiological systems are ruled by mechanisms operating across multiple temporal scales. A recently proposed approach, multiscale entropy analysis, measures the complexity at different time scales and has been successfully applied to long term electrocardiographic recordings. The purpose of this work is to show the applicability of this methodology, rooted on statistical physics ideas, to short term time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with chronic heart failure. In the same spirit, we also propose a multiscale approach, to evaluate interactions between time series, by performing a multivariate autoregressive (AR) modeling of the coarse grained time series. METHODS: We apply the multiscale entropy analysis to our data set of short term recordings. Concerning the multiscale version of the multivariate AR approach, we apply it to the four dimensional time series so as to detect scale dependent patterns of interactions between the physiological quantities. RESULTS: Evaluating the complexity of signals at the multiple time scales inherent in physiologic dynamics, we find new quantitative indicators which are statistically correlated with the pathology. Our results show that multiscale entropy calculated on all the measured quantities significantly differs (P<10(-2) and less) in patients and control subjects, and confirms the complexity-loss theory of aging and disease. Also applying the multiscale autoregressive approach significant differences were found between controls and patients; in the sight of finding a possible diagnostic tools, satisfactory results came also from a receiver-operating-characteristic curve analysis (with some values above 0.8). CONCLUSIONS: The multiscale entropy analysis can give useful information also when only short term physiological recordings are at disposal, thus enlarging the applicability of the methodology. Also the proposed multiscale version of the multivariate regressive analysis, applied to short term time series, can shed light on patterns of interactions between cardiorespiratory variables.

Consensus clustering approach to group brain connectivity matrices.

A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (a) define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node in all pairs of subjects; (b) cluster the distance matrix for each node; (c) build the consensus network from the corresponding partitions; and (d) extract groups of subjects by finding the communities of the consensus network thus obtained. Different from the previous implementations of consensus clustering, we thus propose to use the consensus strategy to combine the information arising from the connectivity patterns of each node. The proposed approach may be seen either as an exploratory technique or as an unsupervised pretraining step to help the subsequent construction of a supervised classifier. Applications on a toy model and two real datasets show the effectiveness of the proposed methodology, which represents heterogeneity of a set of subjects in terms of a weighted network, the consensus matrix.

Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

OBJECTIVES: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. METHODS: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. RESULTS: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. CONCLUSIONS: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. SIGNIFICANCE: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.

Visually evoked phase synchronization changes of alpha rhythm in migraine: correlations with clinical features.

OBJECTIVE: This study aimed to compute phase synchronization of the alpha band from a multichannel electroencephalogram (EEG) recorded under repetitive flash stimulation from migraine patients without aura. This allowed examination of ongoing EEG activity during visual stimulation in the pain-free phase of migraine. METHODS: Flash stimuli at frequencies of 3, 6, 9, 12, 15, 18, 21, 24, and 27 Hz were delivered to 15 migraine patients without aura and 15 controls, with the EEG recorded from 18 scalp electrodes, referred to the linked earlobes. The EEG signals were filtered in the alpha (7.5-13 Hz) band. For all stimulus frequencies that we evaluated, the phase synchronization index was based on the Hilbert transformation. RESULTS: Phase synchronization separated the patients and controls for the 9, 24 and 27 Hz stimulus frequencies; hyper phase synchronization was observed in patients, whereas healthy subjects were characterized by a reduced phase synchronization. These differences were found in all regions of the scalp. CONCLUSIONS: During migraine, the brain synchronizes to the idling rhythm of the visual areas under certain photic stimulations; in normal subjects however, brain regions involved in the processing of sensory information demonstrate desynchronized activity. Hypersynchronization of the alpha rhythm may suggest a state of cortical hypoexcitability during the interictal phase of migraine. SIGNIFICANCE: The employment of non-linear EEG analysis may identify subtle functional changes in the migraine brain.

Information transfer and criticality in the Ising model on the human connectome.

We implement the Ising model on a structural connectivity matrix describing the brain at two different resolutions. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the the sum of the weights to that node and to the average time between consecutive time flips of spins. The results for the connectome of 66 nodes and for that of 998 nodes are similar, thus suggesting that these properties are scale-independent. Finally, we also find that the brain dynamics at criticality is organized maximally to a rich-club w.r.t. the network of information flows.

PEGASO: A Personalised and Motivational ICT System to Empower Adolescents Towards Healthy Lifestyles.

Unhealthy alimentary behaviours and physical inactivity habits are key risk factors for major non communicable diseases. Several researches demonstrate that juvenile obesity can lead to serious medical conditions, pathologies and have important psycho-social consequences. PEGASO is a multidisciplinary project aimed at promoting healthy lifestyles among teenagers through assistive technology. The core of this project is represented by the ICT system, which allows providing tailored interventions to the users through their smartphones in order to motivate them. The novelty of this approach consists of developing a Virtual Individual Model (VIM) for user characterization, which is based on physical, functional and behavioural parameters opportunely selected by experts. These parameters are digitised and updated thanks to the user monitoring through smartphone; data mining algorithms are applied for the detection of activity and nutrition habits and this information is used to provide personalised feedback. The user interface will be developed using gamified approaches and integrating serious games to effectively promote health literacy and facilitate behaviour change.

Information flow in networks and the law of diminishing marginal returns: evidence from modeling and human electroencephalographic recordings.

We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon.