Interpreting Psychophysiological States Using Unobtrusive Wearable Sensors in Virtual Reality

One of the main challenges in the study of human be- havior is to quantitatively assess the participants’ affective states by measuring their psychophysiological signals in ecologically valid conditions. The quality of the acquired data, in fact, is often poor due to artifacts generated by natural interactions such as full body movements and gestures. We created a technology to address this problem. We enhanced the eXperience Induction Machine (XIM), an immersive space we built to conduct experiments on human behavior, with unobtrusive wearable sensors that measure electrocardiogram, breathing rate and electrodermal response. We conducted an empirical validation where participants wearing these sensors were free to move in the XIM space while exposed to a series of visual stimuli taken from the International Affective Picture System (IAPS). Our main result consists in the quan- titative estimation of the arousal range of the affective stimuli through the analysis of participants’ psychophysiological states. Taken together, our findings show that the XIM constitutes a novel tool to study human behavior in life-like conditions

Towards a synthetic tutor assistant: The EASEL project and its architecture

Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learner’s progress, discrimination of the learner’s utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASEL’s unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions

Commissioning of a synchrotron-based proton beam therapy system for use with a Monte Carlo treatment planning system

This work tackles the commissioning and validation of a novel combination of a synchrotron-based proton beam therapy system (Hitachi, Ltd.) for use with a Monte Carlo treatment planning system (TPS). Four crucial aspects in this configuration have been investigated: (1) Monte Carlo-based correction performed by the TPS to the measured integrated depth-dose curves (IDD), (2) circular spot modelling with a single Gaussian function to characterize the synchrotron physical spot, which is elliptical, (3) the modelling of the range shifter that enables using only one set of measurements in open beams, and (4) the Monte Carlo dose calculation model in small fields. Integrated depth-dose curves were measured with a PTW Bragg peak chamber and corrected, with a Monte Carlo model, to account for energy absorbed outside the detector. The elliptical spot was measured by IBA Lynx scintillator, EBT3 films and PTW microDiamond. The accuracy of the TPS (RayStation, RaySearch Laboratories) at spot modelling with a circular Gaussian function was assessed. The beam model was validated using spread-out Bragg peak (SOBP) fields. We took single-point doses at several depths through the central axis using a PTW Farmer chamber, for fields between 2 × 2cm and 30 × 30cm. We checked the range-shifter modelling from open-beam data. We tested clinical cases with film and an ioni- zation chamber array (IBA Matrix). Sigma differences for spots fitted using 2D images and 1D profiles to elliptical and circular Gaussian models were below 0.22 mm. Differences between SOBP measurements at single points and TPS calculations for all fields between 5 × 5 and 30 × 30cm were below 2.3%. Smaller fields had larger differences: up to 3.8% in the 2 × 2cm field. Mean differences at several depths along the central axis were generally below 1%. Differences in range- shifter doses were below 2.4%. Gamma test (3%, 3 mm) results for clinical cases were generally above 95% for Matrix and film. Approaches for modelling synchrotron proton beams have been validated. Dose values for open and range- shifter fields demonstrate accurate Monte Carlo correction for IDDs. Elliptical spots can be successfully modelled using a circular Gaussian, which is accurate for patient calculations and can be used for small fields. A double-Gaussian spot can improve small-field calculations. The range-shifter modelling approach, which reduces clinical commissioning time, is adequat

Alignment of the CMS tracker with LHC and cosmic ray data

© CERN 2014 for the benefit of the CMS collaboration, published under the terms of the Creative Commons Attribution 3.0 License by IOP Publishing Ltd and Sissa Medialab srl. Any further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation and DOI.The central component of the CMS detector is the largest silicon tracker ever built. The precise alignment of this complex device is a formidable challenge, and only achievable with a significant extension of the technologies routinely used for tracking detectors in the past. This article describes the full-scale alignment procedure as it is used during LHC operations. Among the specific features of the method are the simultaneous determination of up to 200 000 alignment parameters with tracks, the measurement of individual sensor curvature parameters, the control of systematic misalignment effects, and the implementation of the whole procedure in a multi-processor environment for high execution speed. Overall, the achieved statistical accuracy on the module alignment is found to be significantly better than 10μm

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Volitional learning promotes theta phase coding in the human hippocampus

Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4–12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3–8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Active learning increased memory performance and hippocampal theta oscillations and promoted a more accurate reactivation of stimulus-specific information during memory retrieval. Representational signals were clustered to opposite phases of the theta cycle during encoding and retrieval. Critically, during active but not passive learning, the temporal structure of intracycle reactivations in theta reflected the semantic similarity of stimuli, segregating conceptually similar items into more distant theta phases. Taken together, these results demonstrate a multilayered mechanism by which active learning improves memory via a phylogenetically old phase coding scheme.This work has received funding from the Horizon2020-EU program under grant agreement of the project ReHyb, ID: 871767, and from the Virtual Brain Cloud project under the program H2020-EU, ID: 826421. N.A. received funding from Deutsche Forschungsgemeinschaft (German Research Foundation) Projektnummer 316803389–SFB 1280, as well as via Projektnummer 122679504–SFB 874

XIM-engine: a software framework to support the development of interactive applications that uses conscious and unconscious reactions in immersive mixed reality

The development of systems that allow multimodal interpretation of human-machine interaction is crucial to advance our understanding and validation of theoretical models of user behavior. In particular, a system capable of collecting, perceiving and interpreting unconscious behavior can provide rich contextual information for an interactive system. One possible application for such a system is in the exploration of complex data through immersion, where massive amounts of data are generated every day both by humans and computer processes that digitize information at different scales and resolutions thus exceeding our processing capacity. We need tools that accelerate our understanding and generation of hypotheses over the datasets, guide our searches and prevent data overload. We describe XIM-engine, a bio-inspired software framework designed to capture and analyze multi-modal human behavior in an immersive environment. The framework allows performing studies that can advance our understanding on the use of conscious and unconscious reactions in interactive systems.Postprint (published version

Commissioning of a synchrotron-based proton beam therapy system for use with a Monte Carlo treatment planning system

This work tackles the commissioning and validation of a novel combination of a synchrotron-based proton beam therapy system (Hitachi, Ltd.) for use with a Monte Carlo treatment planning system (TPS). Four crucial aspects in this configuration have been investigated: (1) Monte Carlo-based correction performed by the TPS to the measured integrated depth-dose curves (IDD), (2) circular spot modelling with a single Gaussian function to characterize the synchrotron physical spot, which is elliptical, (3) the modelling of the range shifter that enables using only one set of measurements in open beams, and (4) the Monte Carlo dose calculation model in small fields. Integrated depth-dose curves were measured with a PTW Bragg peak chamber and corrected, with a Monte Carlo model, to account for energy absorbed outside the detector. The elliptical spot was measured by IBA Lynx scintillator, EBT3 films and PTW microDiamond. The accuracy of the TPS (RayStation, RaySearch Laboratories) at spot modelling with a circular Gaussian function was assessed. The beam model was validated using spread-out Bragg peak (SOBP) fields. We took single-point doses at several depths through the central axis using a PTW Farmer chamber, for fields between 2 × 2cm and 30 × 30cm. We checked the range-shifter modelling from open-beam data. We tested clinical cases with film and an ioni- zation chamber array (IBA Matrix). Sigma differences for spots fitted using 2D images and 1D profiles to elliptical and circular Gaussian models were below 0.22 mm. Differences between SOBP measurements at single points and TPS calculations for all fields between 5 × 5 and 30 × 30cm were below 2.3%. Smaller fields had larger differences: up to 3.8% in the 2 × 2cm field. Mean differences at several depths along the central axis were generally below 1%. Differences in range- shifter doses were below 2.4%. Gamma test (3%, 3 mm) results for clinical cases were generally above 95% for Matrix and film. Approaches for modelling synchrotron proton beams have been validated. Dose values for open and range- shifter fields demonstrate accurate Monte Carlo correction for IDDs. Elliptical spots can be successfully modelled using a circular Gaussian, which is accurate for patient calculations and can be used for small fields. A double-Gaussian spot can improve small-field calculations. The range-shifter modelling approach, which reduces clinical commissioning time, is adequat

XIM-engine: a software framework to support the development of interactive applications that uses conscious and unconscious reactions in immersive mixed reality

The development of systems that allow multimodal interpretation of human-machine interaction is crucial to advance our understanding and validation of theoretical models of user behavior. In particular, a system capable of collecting, perceiving and interpreting unconscious behavior can provide rich contextual information for an interactive system. One possible application for such a system is in the exploration of complex data through immersion, where massive amounts of data are generated every day both by humans and computer processes that digitize information at different scales and resolutions thus exceeding our processing capacity. We need tools that accelerate our understanding and generation of hypotheses over the datasets, guide our searches and prevent data overload. We describe XIM-engine, a bio-inspired software framework designed to capture and analyze multi-modal human behavior in an immersive environment. The framework allows performing studies that can advance our understanding on the use of conscious and unconscious reactions in interactive systems