Stmicroelectronics S.r.l.

Marco Leo

Marco Castellano

Paolo Rosingana

Luca Gandolfi

Alessandra Maria Rizzo Piazza Roncoroni

Eugenio Miluzzi

Enrico Rosario Alessi

Fabio Passaniti

Francesca Giacoma Mignemi

Alessia Cagidiaco

Enea Dimroci

Marco Catellano

Alessandro Giuliano Locardi

Roberta Priolo

Francesco Battini

Francesco Bruni

A device for monitoring the health state is made in a chip including a semiconductor die integrating an electric potential sensor and a cardiac parameter determination unit. The potential sensor is configured to detect potential variations on the body of a living being and associated with a heart rhythm and to generate a cardiac signal. The cardiac parameter determination unit is configured to receive the cardiac signal and determine cardiac parameters indicative of a health state. In particular, the cardiac parameter determination unit is configured to detect triggering events and to determine features of the cardiac signal in time windows defined by the triggering events. The die also integrates a decision unit, configured to receive the cardiac parameters and generate a health signal based on a comparison with threshold values. The cardiac parameters include heart rate and QRS-complex.

Health state monitoring device and method

System, method, and circuitry for utilizing sequential input inertial sensor data to calculate recursive features for training a machine learning algorithm or for classifying the data as a known class. The recursive feature values of a current data sample are calculating based on comparisons between the current data sample value and previous recursive feature values. The recursive features include a recursive maximum, recursive minimum, recursive peak to peak, recursive average, recursive root mean square, and recursive variance.

Method, system, and circuit for extracting features for use in embedded artificial intelligence mechanisms

A processor includes a synchronous circuit including a plurality of processing stages, wherein each processing stage includes a selection data bus; and an asynchronous circuit coupled to each selection data bus, wherein the asynchronous circuit includes an asynchronous state machine whose states correspond to a process phase or a plurality of circuits, wherein the asynchronous circuit further includes a selectable delay circuit whose delay is determined by a present state of the asynchronous state machine, and wherein the asynchronous circuit is configured for generating a plurality of processing stage clock signals each having a selectable delay provided by the selectable delay circuit.

Asynchronous controller for processing unit

A method to test an asynchronous finite state machine for faults, the method including disabling state transitions out of a state of the asynchronous finite state machine and inputting test data to the AFSM to trigger a transition from the state to an expected state. The method further including enabling transitions out of the state of the asynchronous finite state machine, and determining whether the asynchronous finite state machine has performed a successful transition to the expected state.

Systems and methods to test an asynchronous finite machine

A device includes sensing circuitry, compression circuitry, and a memory. The sensing circuitry, in operation, generates sensor data. The compression circuitry is coupled to the sensing circuitry, and, in operation, determines environmental contexts based on variation rates of sensor data and compresses sensor data based on determined environmental contexts. The compressed data is stored in the memory.

Electronic device for efficiently saving historic data of ambient sensors and associated method

Disclosed herein is a method including receiving multi-axis accelerometer data representing a potential step taken by a user of an electronic device. The method also includes determining whether the potential step represented by the multi-axis accelerometer data is a false. This determination is made by calculating statistical data from the multi-axis accelerometer data, and applying a decision tree to the statistical data to perform a cross correlation that determines whether the potential step is a false positive. If the potential step is not a false positive, a step detection process is performed to determine whether the potential step is a countable step and, if the potential step is found to be a countable step, a step counter is incremented.

Electronic device for performing step counting with false-positive rejection

A sensor includes an accelerometer, which, in operation, generates accelerometer data, and digital signal processing circuitry. The digital signal processing circuitry, in operation, generates, based on the generated accelerometer data, a value indicative of a cosine of an angle between an acceleration vector associated with current accelerometer data and a reference acceleration vector, compares the generated value indicative of the cosine of the angle between the vector associated with current accelerometer data and the reference acceleration vector with one or more thresholds and generates a tilt signal based on the comparison of the generated value indicative of the cosine of the angle between the vector associated with current accelerometer data and the reference acceleration vector with the one or more thresholds. The tilt signal may be used as an interrupt signal to an application processor.

Tilt event detection device, system and method

A MEMS inertial sensor device has a package and a gyroscopic sensor, an accelerometric sensor, and an ASIC electronic circuit integrated within the package. The ASIC is operatively coupled to the gyroscopic sensor and the accelerometric sensor for supplying at an output a gyroscopic signal indicative of an angular velocity and an acceleration signal indicative of an acceleration acting on the MEMS inertial sensor device. The ASIC is provided with a processing module, which may be of a purely hardware type, for processing jointly the gyroscopic signal and the accelerometric signal and determining a bias value present on the gyroscopic signal.

MEMS inertial sensor device with determination of the bias value of a gyroscope thereof and corresponding method

A system includes a multi-conductor bus, a master device coupled to the multi-conductor bus, and at least one slave device coupled to the multi-conductor bus. The multi-conductor bus has a clock line and a data line. The master device is arranged to transmit an address configuration sequence, and the at least one slave device is arranged to configurably determine its own address based on at least one portion of the address configuration sequence. The at least one slave device has a physical address configuration input coupled to either a fixed voltage potential or a changing voltage potential. The at least one slave device is arranged with a first address during a pre-initialization state and arranged with a second address during a post-initialization state. During the post-initialization state, the first address and the second address are a same address when the address configuration sequence represents the first address and the first address and the second address are different addresses when the address configuration sequence does not represent the first address.

Dynamic definition of slave address in I2C protocol

An integrated data concentrator, so-called 'sensor hub', for a multi-sensor MEMS system, implements: a first interface module, for interfacing, in a normal operating mode, with a microprocessor through a first communication bus; and a second interface module, for interfacing, in the normal operating mode, with a plurality of sensors through a second communication bus; the sensor hub further implements a pass-through operating mode, distinct from the normal operating mode, in which it sets the microprocessor in direct communication with the sensors, through the first communication bus and the second communication bus. In particular, the sensor hub implements the direct pass-through operating mode in a totally digital manner.

Integrated data concentrator for multi-sensor MEMS systems

A signal interface has a compression unit and a data memory. The compression unit is configured to input an input datum from signal data generated by at least one sensor and further configured to identify the presence or absence of at least one repetition condition in the input datum. If the presence of the at least one repetition condition of the input datum is identified, the compression unit encodes the input datum in a compressed way to generate a compressed datum and saves the compressed datum in the data memory. If the presence of the at least one repetition condition of the input datum is not identified, the compression unit saves the uncompressed input datum in the data memory.

Method of smart saving high-density data and memory device

A plurality of sensors provide respective output data rates, with a first sensor that has a highest output data rate, while one or more other sensors have output data rates that are submultiples of the aforesaid highest output data rate. The data signals coming from the sensors are stored in a memory, e.g., a FIFO memory, by storing the data signals of the first sensor at the aforesaid highest output data rate, accompanying storage of the data signals coming from said first sensor with storage of the data signals coming from the sensors as supplied by said other sensors at the aforesaid submultiple output data rates, so that the data signals are stored in the memory according to a repeated pattern that is common to the various sensors.

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Marco Leo