Stmicroelectronics S.r.l.

Stmicroelectronics International N.v.

Stmicroelectronics Gmbh

Other

Federico Rizzardini

Lorenzo Bracco

Stefano Paolo Rivolta

Marco Bianco

Roberto Mura

Alessandra Maria Rizzo Piazza Roncoroni

Alberto Zancanato

Paolo Rosingana

Michele Ferraina

Chandandeep Singh Pabla

Karimuddin Sayed

Daniele Arceri

Antonio Micali

Mahesh Chowdhary

Andrea Labombarda

Federico Iaccarino

Mauro Bardone

In an embodiment, a device comprises a memory, which, in operation, stores data samples associated with a plurality of data sensors, and circuitry, coupled to the memory, wherein the circuitry, in operation, generates synchronized output data sets associated with the plurality of data sensors. Generating a synchronized output data set includes: determining a reference sample associated with a sensor of the plurality of sensors; verifying a timing validity of a data sample associated with another sensor of the plurality of sensors; identifying a closest-in-time data sample associated with the another sensor of the plurality of sensors with respect to the reference sample; and generating the synchronized output data set based on interpolation.

Device, system and method for synchronizing of data from multiple sensors

A crash detection system includes first and second sensors and a processor. The processor receives first sensor data output by the first sensor, determines whether the first sensor data indicates a first class or a second class, outputs an enable signal to the second sensor if the first sensor data indicates the second class, receives second sensor data output by the second sensor after the enable signal is output, determines whether the second sensor data indicates a high acceleration value, determines whether the first sensor data indicates the first class within a predetermined amount of time after the second sensor data is determined to indicate the high acceleration value, and outputs a signal indicating a crash has occurred in response to determining that the first sensor data indicates the first class within the predetermined amount of time after the second sensor data is determined to indicate the high acceleration value.

In-sensor crash detection with motion sensors

The present disclosure is directed to accelerometer measurement compensation for a device with first and second accelerometers. The first and second accelerometers are included in first and second components, respectively, of the device that are configured to rotate with respect to a hinge. The device detects a stuck condition of the first accelerometer, and compensates acceleration measurements of the first accelerometer by exploiting redundant information from the second accelerometer and applying a runtime calibration of undesired offsets.

Accelerometer stuck condition compensation for devices with multiple accelerometers

A device includes a memory and processing circuitry coupled to the memory. The processing circuitry, in operation: estimates an angular rate of change and determines a rotational versor based on the rotational data; and estimates a gravity vector based on the angular rate of change and the rotational versor. The processing circuitry generates a dynamic gravity vector based on the estimated gravity vector, a correction factor and an estimated error in estimated gravity vector. The processing circuitry estimates a linear acceleration and determines an acceleration versor based on the acceleration data, and determines the correction factor based on the linear acceleration. The processing circuitry estimates the error in the estimated gravity vector based on the acceleration versor.

Dynamic gravity vector estimation for memory constrained devices

The present disclosure is directed to a device and method for human fall detection solution. Fall detection is performed by a low power inertial measurement unit (IMU) that is communicatively coupled between a pressure sensor and an application processor. The IMU includes one or more motions sensors, such as an accelerometer and gyroscope. The application processor is the main processor of the containing device. The IMU receives pressure sensor data from the pressure sensor, and executes the fall detection using both the pressure sensor data and accelerometer data.

In-sensor fall detection

The present disclosure is directed to lift-up gesture detection for electronic devices. An initial lift-up gesture is detected in response to an orientation change and a lift-up motion of the device being detected. The initial lift-up gesture is validated as a true lift-up gesture in a case where a shaking motion of the device is not being detected when the initial lift-up gesture is detected. If a shaking motion of the device is detected when the initial lift-up gesture is detected, the initial lift-up gesture is rejected.

Low power lift-up gesture detection with shake rejection

A recognition system for recognition of a gesture of bringing an electronic device, of a mobile or wearable type, to a user's ear, designed to be integrated in the electronic device and having: a movement sensor, configured to provide a movement signal indicative of the movement of the electronic device; an electrostatic charge variation sensor, configured to provide a charge variation signal associated with the movement; a processing module, operatively coupled to the movement sensor and to the electrostatic charge variation sensor and configured to perform a joint processing of the movement signal and the charge variation signal for the recognition of the gesture.

System and method for automatic recognition of the gesture of bringing an electronic device to the ear

A microelectromechanical weather pattern recognition system includes: at least one movement sensor, of a MEMS type, which generates a movement signal, in the presence and as a function of at least one weather pattern to be recognized; and a recognition circuitry, which is coupled to the movement sensor and which receives the movement signal; extracts given features of the movement signal; and perform processing operations, based on the given features of the movement signal, in order to recognize the weather pattern by executing at least one, appropriately trained, machine-learning algorithm.

Microelectromechanical system and corresponding method for weather pattern recognition

The present disclosure is directed to a device and method for lid angle detection that is accurate even if the device is activated in an upright position. While the device is in a sleep state, first and second sensor units measure acceleration and angular velocity, and calculate orientations of respective lid components based on the acceleration and angular velocity measurements. Upon the device exiting the sleep state, a processor estimates the lid angle using the calculated orientations, sets the estimated lid angle as an initial lid angle, and updates the initial lid angle using, for example, two accelerometers; two accelerometers and two gyroscopes; two accelerometers and two magnetometers; or two accelerometers, two gyroscopes, and two magnetometers.

Lid angle detection

An electronic device has an input which, in operation, receives an input stream of accelerometer data samples indicative of acceleration values along at least one axis. The devices includes circuitry, coupled to the input. The circuitry, in operation, executes an automatic-learning algorithm on blocks of samples of the input stream of accelerometer data samples to identify, for each block, a corresponding condition-of-user-movement from among a plurality of determined conditions-of-user-movement. The circuitry generates a plurality of streams of samples based on the input stream of accelerometer data samples, and for each condition of movement identified, selects a corresponding stream of samples of the plurality of streams of samples. The circuitry executes a wrist-tilt gesture detection algorithm using samples of the selected stream of the plurality of streams of samples.

Method for detecting a wrist-tilt gesture and an electronic unit and a wearable electronic device which implement the same

A method includes receiving electrostatic sensor data in a processor of an electronic device from an electrostatic sensor mounted behind a touchscreen of the electronic device and using the electrostatic sensor data to determine when the touchscreen is being used. Based on whether or not the touchscreen is being used, an on-table detection (OTD) algorithm is selected from a plurality of available OTD algorithms. In one or more examples, the OTD algorithm may also be selected based on the current device mode of the electronic device, which may be determined from a lid angle, a screen angle, and a keyboard angle of the electronic device. The selected OTD algorithm is run to determine whether or not the electronic device is located on a stationary or stable surface.

System and method for determining whether an electronic device is located on a stationary or stable surface

The present disclosure is directed to input detection for electronic devices using electrostatic charge sensors. The devices and methods disclosed herein utilize electrostatic charge sensors to detect various touch gestures, such as long and short touches, single/double/triple taps, and swipes; and perform in-ear detection.

Input detection with electrostatic charge sensors

A pointing electronic device is provided with: an inertial measurement module, to generate motion input data, indicative of motion of the pointing electronic device, at an input data rate; a pointing determination unit, to implement a pointing algorithm at a processing data rate based on the motion input data, to generate screen-frame displacement data corresponding to 3D-space movements of the pointing electronic device, the processing data rate being higher than the input data rate. The pointing electronic device is further provided with a rate upscaling unit, interposed between the inertial measurement module and the pointing determination unit, to implement a data-rate upscaling of the motion input data, to generate upscaled motion input data to be processed by the pointing determination unit at a data rate matching the processing data rate, via a predictive data reconstruction of missing samples based on the actual motion input data.

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Settimo Milanese, Italy

Federico Rizzardini