Commissariat &#xc0; L'&#xc9;nergie Atomique Et Aux &#xc9;nergies Alternatives

Commissariat a L'energie Atomique

Movea

Movea Sa

Elichens

Commissariat a L'energie Atomique Et Aux Alternatives

Telecom Paristech

Pierre Jallon

Philippe Ciblat

Abdelaziz Bouzegzi

Florence Gris

Raymond Campagnolo

Stéphane Bonnet

Eléonore Doron

Sylvain Lachal

In a method for calibrating a gas sensor—for determining a gaseous species concentration in a gas, which species absorbs light in an absorption spectral band—the gas sensor includes a chamber for containing the gas; a light source through which a supply electrical current is passable to raise the light source to a temperature; a measurement photodetector for measuring—in a measurement spectral band comprising the absorption spectral band—a measured intensity of a light beam emitted by the light source and transmitted by the gas in the chamber; and a reference photodetector for measuring a reference intensity of a reference light beam emitted by the light source in a reference spectral band. A non-linear calibration function is determined to estimate an intensity, measured in the measurement spectral band by the measurement photodetector in the absence of gaseous species, from a reference intensity measured in the reference spectral band.

Method for calibrating a gas sensor and method for measuring a gas using the calibration

An automated system of regulation of a patient's blood glucose, including: a blood glucose sensor; an insulin injection device; and a processing and control unit capable of predicting, from a physiological model, the future evolution of the patient's blood glucose over a prediction period, and of controlling the insulin injection device by taking the prediction into account, wherein the processing and control unit is capable of: a) calibrating the physiological model, taking into account the blood glucose measured by the sensor during a past observation period; b) at the end of the calibration, calculating an indicator representative of the error between the blood glucose estimated from the model and the real blood glucose measured by the sensor; and c) adjusting the prediction period by taking into account the value of the indicator.

Automated system for regulating a patient's blood glucose level

An automated system of regulation of a patient's blood sugar, including: a blood sugar sensor; an insulin injection device; and a processing and control unit capable of predicting the future evolution of the patient's blood sugar from a physiological model and of controlling the insulin injection device by taking the prediction into account, wherein: the physiological model includes a differential equation system describing the time variation of a plurality of state variables; and the processing and control unit is capable of implementing a step of automatic calibration of the physiological model including a step of estimation of initial values of the state variables by minimization of a quantity representative of the error, during a past observation period, between the blood sugar estimated based on the physiological model and the blood sugar measured by the sensor.

Automated system for controlling the blood glucose level of a patient

A system for injecting and controlling a flow of liquid to be injected, including: a peristaltic pump; a catheter delivering the liquid to be injected; a pressure sensor measuring liquid pressure in the catheter; a controller receiving data from the pressure sensor and controlling the pump on the basis of the data measured at different times.

Closed loop system for controlling the reflux of a fluid injection

A device identifying a cyclic movement type of a physical system from a set of possible cyclic movements types includes: a movement sensor providing an observation data sequence concerning the physical system; a storage storing at least one statistical hidden-state Markov model; and a computer programmed to analyze the observation data sequence and select one cyclic movement type based on the stored statistical model. The storage includes least one statistical hidden-state Markov model for each possible cyclic movement type and the computer is programmed to analyze compatibility of the observation data sequence with each possible cyclic movement type based on the statistical model associated with this cyclic movement type. The computer is further programmed to provide a sequence of hidden states of the statistical model associated with the selected cyclic movement type, based on the observation data sequence and the statistical model associated with the selected cyclic movement type.

Device and method for identifying a cyclic movement and corresponding computer program

This method for detecting activity (A) of a physical system carrying a motion sensor comprises the following steps: extracting () a sequence (M) of measurements provided by the motion sensor; deducing () therefrom an observation sequence (O) calculated on the basis of the measurement sequence (M); determining () the activity (A) of the physical system in the form of a sequence of states corresponding to the observation sequence, using a statistical model of components of the observation sequence in view of a plurality of possible predetermined states of the physical system. At least one component of the observation sequence (O) is calculated on the basis of a reference value ({circumflex over (&#x3bb;)}) of a vector representing a reference axis. In addition, the method comprises the following calibration steps: calculation () of a likelihood value of the observation sequence (O) in view of said statistical model; comparison () of this likelihood value with a predetermined threshold; on the basis of this comparison, activating an estimation () of a new reference value ({circumflex over (&#x3bb;)}) of the vector representing the reference axis.

Method of detecting activity with a motion sensor, and corresponding device and computer program

A system for observing a swimming activity of a person includes a waterproof housing (BET) having a motion sensor (MS), and is furnished with fixing means (BEL) for securely fastening the housing (BET) to a part of the body of a user. The system has analysis means (AN) for analyzing the signals transmitted by the motion sensor (MS) to at least one measurement axis and which are adapted for determining the type of swimming of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of swimming.

System and method for observing the swimming activity of a person

A method for configuring a device for detecting a situation from a set of situations where a physical system comprises the following steps: reception of a learning sequence corresponding to a given situation of the physical system; determination of parameters of a hidden-state Markov statistical model recorded in the detection device and relating to the given situation, on the basis of a prior initialization of these parameters. The prior initialization comprises the following steps: determination of a plurality of probability distributions from the learning sequence; distribution of the probability distributions between the various hidden states of the statistical model in question by global optimization of a function of adaptation of these probability distributions to the various hidden states and to impossible transition constraints; and initialization of the parameters of the statistical model in question using given representative probability distributions for each hidden state of the statistical model in question.

Method for configuring a sensor detection device, and corresponding computer program and adaptive device

A system for determining activity of a mobile element, includes at least one motion sensor having a measurement axis, and which is securely connected to the mobile element. A filter for selects, for each measurement axis of the motion sensor, high frequencies above a first threshold, and processing equipment for determines a unidimensional high-frequency component (y(n)) equal to the square of the Euclidean norm of the high frequencies along the measurement axes of the motion sensor. A calculator calculates, for each state, probability density functions (Py,i) of the high-frequency component, the probability density function corresponding to each state according to a Chi-square law with a degree of freedom equal to the number of measurement axes of the motion sensor. An analyzer defines states of the mobile element, utilizing the probability density function of the high-frequency component for each state, together with the probabilities of transitions between two successive states.

System and method for determining the activity of a mobile element

A system for determining the posture of a person has a motion sensor (CM) with at least one axis of measurement, which is provided with fixing means (MF) for rigidly connecting said motion sensor (CM) to a user. Analysis means (AN) are also included for determining a posture of the user. The analysis means (AN) utilize: (A) joint densities of probabilities of a low-frequency component and a high-frequency component, these densities of probabilities being defined for each posture; and (B) probabilities of transition between two successive postures.

System and method for determining the posture of a person

A device detecting at least one scenario from predetermined scenarios wherein a physical system observed by at least one sensor is likely to be found, including: at least one sensor providing a physical system observation data sequence; a storage storing at least one statistical model associating possible observation data values with the predetermined scenarios; a computer programmed to select at least one of the scenarios based on the observation data sequence and at least one statistical model. The storage includes plural statistical models broken down into plural ordered levels between an input level, wherein each statistical model associates possible values of at least a portion of the observation data with intermediate states for the statistical model, and an output level, wherein each statistical model associating possible values of at least a portion of the intermediate states of a lower level with at least a portion of the predetermined scenarios.

Sensor detection device, corresponding detection method and computer program

This method of configuring a device for detecting a situation from among a set of situations in which it is possible to find a physical system observed by a least one sensor, comprises the following steps: receiving () a training sequence corresponding to a determined situation of the physical system; determining () parameters of a statistical hidden Markov model recorded on the detection device and related to the determined situation, based on a prior initialization (-) of these parameters. The prior initialization (-) comprises the following steps: determining () multiple probability distributions from the training sequence; distributing (-) the determined probability distributions between the hidden states of the statistical model being used; and initializing the parameters of the statistical model being used from representative probability distributions determined for each hidden state of the statistical model being used.

Method of configuring a sensor-based detection device and a corresponding computer program and adaptive device

An OFDM signal identification method in a received signal, wherein the received signal is sampled and cut off into blocks of P samples, each block being subjected to an inverse Fourier transform to obtain a plurality of symbols at a plurality of frequencies. For each frequency of the plurality, the kurtosis of the set of symbols thus obtained at this frequency is calculated, and then it is determined whether the kurtosis has a periodicity in frequency domain from kurtosis values calculated for the frequencies of the plurality, and the presence of an OFDM signal in the received signal is identified if the kurtosis has such a periodicity.

Identification method of an OFDM signal

A system for detecting an epileptic seizure in a prone person, comprising:

System and method for detecting an epileptic seizure in a prone epileptic person

A system for observing a walking activity of a person comprises a device (DISP) adapted for delivering at output, for a footstep of the person, a first difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and the instant at which the foot is laid flat, a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and the instant at which the last toe of the foot is lifted, and an angular speed of the corresponding tibia at the instant at which the foot is laid flat. The system comprises analysis means (AN) for analyzing the signals delivered by the device and adapted for determining a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking.

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Grenoble, France

Pierre Jallon