Experimental Procedure for the Metrological Characterization of Time-of-Flight Cameras for Human Body 3D Measurements.

Time-of-flight cameras are widely adopted in a variety of indoor applications ranging from industrial object measurement to human activity recognition. However, the available products may differ in terms of the quality of the acquired point cloud, and the datasheet provided by the constructors may not be enough to guide researchers in the choice of the perfect device for their application. Hence, this work details the experimental procedure to assess time-of-flight cameras' error sources that should be considered when designing an application involving time-of-flight technology, such as the bias correction and the temperature influence on the point cloud stability. This is the first step towards a standardization of the metrological characterization procedure that could ensure the robustness and comparability of the results among tests and different devices. The procedure was conducted on Kinect Azure, Basler Blaze 101, and Basler ToF 640 cameras. Moreover, we compared the devices in the task of 3D reconstruction following a procedure involving the measure of both an object and a human upper-body-shaped mannequin. The experiment highlighted that, despite the results of the previously conducted metrological characterization, some devices showed evident difficulties in reconstructing the target objects. Thus, we proved that performing a rigorous evaluation procedure similar to the one proposed in this paper is always necessary when choosing the right device.

Wheelchair driving strategies: A comparison between standard joystick and gaze-based control.

This paper aims to evaluate and compare the driving performances achieved with a power wheelchair using a standard joystick versus a novel gaze-based technology. The gaze-based interface, called RoboEYE, involves a novel paradigm of computer interaction that handles the receipt of information from an eye tracker, using it as a continuous input for wheelchair navigation. A pool of 36 subjects has tested both technologies in a circuit designed considering the Wheelchair Skill Test. The experimental analysis involved evaluations of specific metrics of motion and the submission of questionnaires to collect required information about perceived feelings and mental workload. The joystick proved to be the best driving interface. It turned out to be more accurate and efficient than the gaze-based solution. However, the latter achieved only small differences in driving kinematics. These differences can be considered negligible from an operational point of view, offering a driving experience similar to that achievable with the joystick. Testers reported no particular stress, fatigue, or frustration when switching from one interface to another. These elements suggest that the proposed gaze-based solution is an appropriate alternative for a technology transition driven by a pathological change in the user's condition.

Hydrogen Detection Limits and Instrument Sensitivity of High-Resolution Broadband Neutron Spectrometers.

The limits of detection (LOD) and quantitation (LOQ) in the mass domain, for broadband vibrational spectroscopy with neutrons on the TOSCA spectrometer at the ISIS Pulsed Neutron and Muon Source (UK), have been studied. The well-known 3σ and 10σ approaches are used through a specifically developed analytical procedure that is based on the calculation of the integrated spectral intensities in selected energy-transfer ranges, as a function of mass of standard reference materials and calibrants, such as ZrH2, 2,5-diiodothiophene, and low-density polyethylene. The analysis shows that the blank, that is, the instrument setup without the analyte, plays a critical role in the measurement performance, especially for small specimen quantities. The results point that TOSCA enables detection of 128 µmol (LODH) and quantitation of 428 µmol (LOQH) of elemental hydrogen analytes in ZrH2. The determined values for this and other standards allow for the assessment of the calibration curve design and instrument sensitivity and define a method to be used for inelastic neutron scattering spectrometers such as TOSCA, or VESPA, the new beamline under construction at the European Spallation Source in Lund (Sweden).

A Python Algorithm to Analyze Inelastic Neutron Scattering Spectra Based on the y-Scale Formalism.

This paper presents a Python-based algorithm, named INSCorNorm, to correct the inelastic neutron scattering (INS) spectra for both sample and container self-shielding and to normalize the experimental spectral intensity to an absolute physical scale (barn/energy unit) facilitating the comparison with computer simulations and interpretation. The algorithm is benchmarked against INS measurements of ZrH2 performed on the TOSCA spectrometer at the ISIS Facility. We also apply the algorithm to the INS spectra from l-lysine, a system of broad interest in biology and medicine, and we discuss how corrected INS data provide an experimental benchmark for theoretical calculations of nuclear anisotropic displacement parameters in molecular systems. The total neutron sample cross section to use for the self-shielding corrections is discussed, as well as the best approach to derive experimentally the cross section at the VESUVIO spectrometer, together with the experimental value of the hydrogen nuclear mean kinetic energy, ⟨Ek⟩. The algorithm is made available to the neutron user community within the MANTID software.

Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress.

In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and ß electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain-body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η-ρ and δ-θ, θ-α, α-ß), but also statistically significant interactions between the two subnetworks (mainly η-ß and η-δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain-heart interactions and of brain-brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.

Mutual Information Analysis of Brain-Body Interactions during different Levels of Mental stress.

In this work, we analyze brain-heart interactions during different mental states computing mutual information (MI) between the dynamic activity of different physiological systems. In 18 healthy subjects monitored in a relaxed resting state and during a mental arithmetic and a serious game task, multichannel EEG, one lead ECG, respiration and blood volume pulse were collected via wireless non-invasive biosensors. From these signals, synchronous 300-second time series were extracted measuring brain activity via the δ, θ, α, and ß EEG power, and activity of the body district via the ECG R-R interval η, the respiratory amplitude ρ and the pulse arrival time π. MI was computed using a linear estimator: (i) between {η,ρ,π} and {δ,θ,α,ß}, to measure overall brain-body interactions; (ii) between each time series and the others of the same district, to measure information shared within a district; and (iii) between each time series of a district and all series of the other district, to evaluate individual contributions to the information shared between brain and body. Results document the existence of statistically significant brain-body interactions, with high MI values involving mainly the η body dynamics and the δ and ß brain dynamics. State-dependent variations were mostly relevant to the MI of the brain system involving δ, θ, α during mental arithmetic, and α and ß during serious game. Thus, MI can be useful to detect correlated activity within and between brain and body systems monitored simultaneously during different mental states.

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress.

In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical activity of seven physiological systems was probed with one-second time resolution measuring the time series of the δ , θ , α and ß brain wave amplitudes, the cardiac period (RR interval), the respiratory amplitude, and the duration of blood pressure wave propagation (pulse arrival time, PAT). Synchronous 5-min windows of these time series, obtained from 18 subjects during resting wakefulness (REST), mental stress induced by mental arithmetic (MA) and sustained attention induced by serious game (SG), were taken to describe the dynamics of the nodes composing the observed physiological network. Network activity and connectivity were then assessed in the framework of information dynamics computing the new information generated by each node, the information dynamically stored in it, and the information transferred to it from the other network nodes. Moreover, the network topology was investigated using directed measures of conditional information transfer and assessing their statistical significance. We found that all network nodes dynamically produce and store significant amounts of information, with the new information being prevalent in the brain systems and the information storage being prevalent in the peripheral systems. The transition from REST to MA was associated with an increase of the new information produced by the respiratory signal time series (RESP), and that from MA to SG with a decrease of the new information produced by PAT. Each network node received a significant amount of information from the other nodes, with the highest amount transferred to RR and the lowest transferred to δ , θ , α and ß . The topology of the physiological network underlying such information transfer was node- and state-dependent, with the peripheral subnetwork showing interactions from RR to PAT and between RESP and RR, PAT consistently across states, the brain subnetwork resulting more connected during MA, and the subnetwork of brain-peripheral interactions involving different brain rhythms in the three states and resulting primarily activated during MA. These results have both physiological relevance as regards the interpretation of central and autonomic effects on cardiovascular and respiratory variability, and practical relevance as regards the identification of features useful for the automatic distinction of different mental states.