Abstract: The present invention relates to a method for determining the presence of a target microorganism in a biological sample comprising the steps of: providing a strip made of porous material, said strip having at least one fixation zone on which at least one phage exposing a peptide selective for said microorganism is fixed, and a deposition zone, separated from said fixation zone and intended to receive a portion of said biological sample, said phage being bound to a marker in deactivated form; contacting said biological sample with said strip on said deposition zone and eluting said microorganism through said strip so that said microorganism reaches said fixation zone to form a phage-target microorganism complex and release said marker in activated form; detecting said marker in activated form.

Abstract: An embodiment method comprises collecting at least one electrophysiological signal of a human over a limited time duration, and computing a set of electrophysiological signal features. The computing comprises at least one of: providing at least one reference electrophysiological signal and applying dynamic time warping processing to the at least one collected and at least one reference electrophysiological signals, applying stacked-auto-encoder artificial neural network processing to the collected electrophysiological signal, or filtering the electrophysiological signal collected via joint low-pass and high-pass filtering. The method further comprises applying pattern recognition processing to the computed set of features, producing a virtual key signal indicative of an identity of the human, and applying the virtual key signal to a user circuit to switch it between a first state and a second state as a result of the virtual key signal matching an authorized key signal stored in the user circuit.

Abstract: A time series of face images of a human during a human activity are captured. A first artificial neural network (ANN) processing pipeline processes the captured time series of face images to provide a first attention level indicator signal. An electrophysiological signal indicative of the level of attention of the human during the activity is also captured. A second ANN processing pipeline processes the sensed electrophysiological signal to providing a second attention level indicator signal. A risk indicator signal is then generated based on at least one of the first attention level indicator and second attention level indicator. A user circuit is then triggered as a result of the risk indicator reaching or failing to reach at least one attention level threshold.

Abstract: An image classification method, in particular medical images, for example radiographic images, wherein a sub-image RI which contains, for example, a Region Of Interest (ROI) in which a portion of limb and a prosthesis inserted into the same limb are visible is subjected to a classification process designed to define whether the sub-image RI belongs to a first class C1 of images with a respective first probability P1 or to a second class of images C2 with a respective probability P2.

Abstract: The present invention relates to a method for the antibacterial treatment of a solid surface comprising the steps of: a) activation of said surface by means of oxygen plasma; b) deposition from the vapor phase of a silane or siloxane having at least one fluorocarburic terminal group. A method is also provided for the antibacterial treatment of a solid surface comprising the step of functionalizing said surface with nanoparticles of gamma-Fe2O3.

Abstract: A method includes receiving a video signal that comprises a time series of images of a face of a human, wherein the images in the time series of images comprise a set of landmark points in the face, applying tracking processing to the video signal to reveal variations over time of at least one image parameter at the set of landmark points in the human face, generating a set of variation signals indicative of variations revealed at respective landmark points in the set of landmark points, applying processing to the set of variation signals, the processing comprising artificial neural network processing to produce a reconstructed PhotoPletysmoGraphy (PPG) signal, and estimating a heart rate variability of a variable heart rate of the human as a function of the reconstructed PPG signal.

Abstract: A method includes receiving a video signal that comprises a time series of images of a face of a human, wherein the images in the time series of images comprise a set of landmark points in the face, applying tracking processing to the video signal to reveal variations over time of at least one image parameter at the set of landmark points in the human face, generating a set of variation signals indicative of variations revealed at respective landmark points in the set of landmark points, applying processing to the set of variation signals, the processing comprising artificial neural network processing to produce a reconstructed PhotoPletysmoGraphy (PPG) signal, and estimating a heart rate variability of a variable heart rate of the human as a function of the reconstructed PPG signal.

Abstract: A method includes receiving a video signal that comprises a time series of images of a face of a human, wherein the images in the time series of images comprise a set of landmark points in the face, applying tracking processing to the video signal to reveal variations over time of at least one image parameter at the set of landmark points in the human face, generating a set of variation signals indicative of variations revealed at respective landmark points in the set of landmark points, applying processing to the set of variation signals, the processing comprising artificial neural network processing to produce a reconstructed PhotoPletysmoGraphy (PPG) signal, and estimating a heart rate variability of a variable heart rate of the human as a function of the reconstructed PPG signal.

Abstract: A time series of face images of a human during a human activity are captured. A first artificial neural network (ANN) processing pipeline processes the captured time series of face images to provide a first attention level indicator signal. An electrophysiological signal indicative of the level of attention of the human during the activity is also captured. A second ANN processing pipeline processes the sensed electrophysiological signal to providing a second attention level indicator signal. A risk indicator signal is then generated based on at least one of the first attention level indicator and second attention level indicator. A user circuit is then triggered as a result of the risk indicator reaching or failing to reach at least one attention level threshold.

Abstract: In an embodiment, PhotoPlethysmoGraphy (PPG) signals are processed by detecting peaks and valleys in the PPG signal, segmenting the PPG signal to provide a time series of PPG waveforms located between two subsequent valleys in the PPG signal, applying to the waveforms in the time series pattern recognition with respect to a reference PPG waveform pattern produced based on a mathematical model of the PPG signal by assigning to the waveforms in the time series a recognition score. A resulting PPG signal is produced by retaining the waveforms in the time series having an assigned recognition score reaching a recognition threshold, and discarding the waveforms in the time series having an assigned recognition score failing to reach the recognition threshold.

Abstract: In an embodiment, PhotoPlethysmoGraphy (PPG) signals are processed by detecting peaks and valleys in the PPG signal, segmenting the PPG signal to provide a time series of PPG waveforms located between two subsequent valleys in the PPG signal, applying to the waveforms in the time series pattern recognition with respect to a reference PPG waveform pattern produced based on a mathematical model of the PPG signal by assigning to the waveforms in the time series a recognition score. A resulting PPG signal is produced by retaining the waveforms in the time series having an assigned recognition score reaching a recognition threshold, and discarding the waveforms in the time series having an assigned recognition score failing to reach the recognition threshold.

Abstract: Blood pressure signals are reconstructed from PhotoPlethysmoGraphy (PPG) signals by: receiving PPG signals including systolic, diastolic and dicrotic phases; and determining first and second derivatives of the PPG signals and: a first set of values indicative of lengths of the signal paths of the PPG signal, the first derivative and the second derivative thereof in the systolic, diastolic and dicrotic phases; a second set of values indicative of relative durations of the PPG signal and the first and second derivatives thereof in the systolic, diastolic and dicrotic phases; and a third set of values indicative of the time separation of peaks and/or valleys in subsequent waveforms of the PPG signal. Reconstruction also includes applying artificial neural network processing to the first, second and third set of values. The artificial neural network processing includes artificial neural network training as a function of blood pressure signals to produce reconstructed blood pressure signals.

Abstract: Blood pressure signals are reconstructed from PhotoPlethysmoGraphy (PPG) signals by: receiving PPG signals including systolic, diastolic and dicrotic phases; and determining first and second derivatives of the PPG signals and: a first set of values indicative of lengths of the signal paths of the PPG signal, the first derivative and the second derivative thereof in the systolic, diastolic and dicrotic phases; a second set of values indicative of relative durations of the PPG signal and the first and second derivatives thereof in the systolic, diastolic and dicrotic phases; and a third set of values indicative of the time separation of peaks and/or valleys in subsequent waveforms of the PPG signal. Reconstruction also includes applying artificial neural network processing to the first, second and third set of values. The artificial neural network processing includes artificial neural network training as a function of blood pressure signals to produce reconstructed blood pressure signals.

Abstract: A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.

Abstract: An embodiment method includes segmenting at least one electrophysiological signal and producing a set of sampled waveforms, applying artificial neural network processing to the set of sampled waveforms and a set of randomly generated noise samples and producing at least one altered data pattern, the altered data pattern comprising the set of filtered waveforms altered as a function of the randomly generated noise samples, providing calibration data comprising expected waveforms for filtered waveforms in the set of filtered waveforms, applying classifier processing to the produced at least one altered data pattern to detect a degree of resemblance between the produced at least one altered data pattern and the calibration data patterns, the classifier processing producing classification signals having values above or below at least one threshold value as a function of the detected degree of resemblance, and triggering a user circuit as a function of the classification signal.

Abstract: A method of processing electrophysiological signals includes: receiving, over a limited time duration, sample electrocardiography (ECG) signals indicative of heart pulsatile activity occurring with a variable heart rate, wherein receiving the sample ECG signals is discontinued at an expiry of the limited time duration; receiving photoplethysmography (PPG) signals indicative of the heart pulsatile activity; determining a correlation between the sample ECG signals and the PPG signals; determining reconstructed ECG signals from the PPG signals as a function of the correlation between the sample ECG signals and the PPG signals; and estimating a heart rate variability of the variable heart rate as a function of the reconstructed ECG signals.

Abstract: An embodiment method comprises collecting at least one electrophysiological signal of a human over a limited time duration, and computing a set of electrophysiological signal features. The computing comprises at least one of: providing at least one reference electrophysiological signal and applying dynamic time warping processing to the at least one collected and at least one reference electrophysiological signals, applying stacked-auto-encoder artificial neural network processing to the collected electrophysiological signal, or filtering the electrophysiological signal collected via joint low-pass and high-pass filtering. The method further comprises applying pattern recognition processing to the computed set of features, producing a virtual key signal indicative of an identity of the human, and applying the virtual key signal to a user circuit to switch it between a first state and a second state as a result of the virtual key signal matching an authorized key signal stored in the user circuit.

Abstract: In an embodiment, a method of processing an electrophysiological signal includes collecting the electrophysiological signal that is indicative of a level of attention of a human; filtering the electrophysiological signal via joint low-pass and high-pass filtering using a set of filtering parameters including low-pass filters parameters and high-pass filters parameters having a set of low-pass cut-off frequencies and a set of high-pass cut-off frequencies respectively. The method further includes applying artificial neural network processing to the filtered electrophysiological signal to extract therefrom a set of features of the electrophysiological signal. The method further includes applying classifier processing to the set of features extracted from the filtered electrophysiological signal and producing a classification signal indicative of the level of attention of the human. The method further includes generating a trigger signal to trigger a user circuit based on the classification signal.

Abstract: A method includes receiving a video signal that comprises a time series of images of a face of a human, wherein the images in the time series of images comprise a set of landmark points in the face, applying tracking processing to the video signal to reveal variations over time of at least one image parameter at the set of landmark points in the human face, generating a set of variation signals indicative of variations revealed at respective landmark points in the set of landmark points, applying processing to the set of variation signals, the processing comprising artificial neural network processing to produce a reconstructed PhotoPletysmoGraphy (PPG) signal, and estimating a heart rate variability of a variable heart rate of the human as a function of the reconstructed PPG signal.

Abstract: A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.