Stmicroelectronics S.r.l.

Stmicroelectronics International N.v.

Stmicroelectronics Gmbh

Other

Stefano Paolo Rivolta

Federico Rizzardini

Lorenzo Bracco

Roberto Mura

Marco Bianco

Alessandra Maria Rizzo Piazza Roncoroni

Alberto Zancanato

Paolo Rosingana

Andrea Labombarda

Mahaveer Jain

Michele Ferraina

Ashish Bhargava

Daniele Arceri

Mahesh Chowdhary

Federico Iaccarino

Edoardo Nagali

Piergiorgio Arrigoni

Mauro Bardone

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 a wearable electronic device, such as a watch, that includes one or more optical sensors. In order to determine accuracy of measurements by the optical sensors, the device detects whether or not the optical sensors are in physical contact with the user's skin. The device detects a level of contact between the user's skin and the optical sensors based on electrostatic charge variation measurements, and generates a contact reliability index (CRI) based on the level of contact. Operation of the optical sensors are adjusted based on the CRI.

Sensor reliability detection and power management

The present disclosure is directed to human presence detection with an infrared sensor. The human presence detection utilizes signal regularization and edge detection to minimize the effect of drift on the human presence detection. The human presence detection is accurate regardless of changes in ambient temperatures.

Presence detection with signal regularization and edge detection

The present disclosure is directed to pick-up state detection for an electronic device, such as a laptop. In a pick-up state, the device is picked or lifted up from a surface, such as a table. A power state of the device is adjusted in response to detecting the pick-up state. For example, the device is in a hibernate state while set on the table, and is switched to a working state in response to detecting the pick-up state.

Device pick-up 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 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 with enhanced human activity recognition. The device detects a human activity using one more motion sensors, and enhances the detected human activity depending on whether the device is in an indoor environment or an outdoor environment. The device utilizes one or more electrostatic charge sensors to determine whether the device is in an indoor environment or an outdoor environment.

Enhanced human activity recognition

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 a device configured to detect whether the device is in a bag or being taken out of the bag. The device determines whether the device is in a bag or being taken out of the bag based on motion measurements generated by a motion sensor and electrostatic charge measurements generated by an electrostatic charge sensor. By using both distance measurements and motion measurements, the device is able to detect whether the device is in the bag or being taken out of the bag with high efficiency, accuracy, and robustness.

Bag detection using an electrostatic charge sensor

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.

Predictive data-reconstruction system and method for a pointing electronic device

The present disclosure is directed to devices and methods for detecting dimming gestures using infrared detection. Infrared signals are detected using a thermal metal-oxide-semiconductor (TMOS) infrared (IR) sensor solution. The TMOS IR sensor is highly accurate and has low power consumptions compared to traditional IR sensors that utilize IR receivers.

Infrared based dimming gesture detection

A method is provided for controlling an electronic apparatus on the basis of a value of a lid angle between a first hardware element accommodating a first magnetometer and a second hardware element accommodating a second magnetometer. The method includes acquiring, through the magnetometers, first signals representing an orientation of the hardware elements. A calibration parameter indicative of a condition of calibration of the magnetometers is generated on the basis of the first signals. A reliability value indicative of a condition of reliability of the first signals is generated on the basis of the first signals. A first intermediate value of the lid angle is calculated on the basis of the first signals. A current value of the lid angle is calculated on the basis of the calibration parameter, of the reliability value, and of the first intermediate value, and the electronic apparatus is controlled on the basis of the current value.

Electronic apparatus control method performed through lid angle calculation, electronic apparatus thereof and software product

The present disclosure is directed to a device and method for detecting presence or absence of human speech. The device and method utilize a low-power accelerometer. The device and method generate an acceleration signal using the accelerometer, filter the acceleration signal with a band pass filter or a high pass filter, determine at least one calculation of the filtered acceleration signal, detect a presence or absence of a voice based on the at least one calculation, and output a detection signal that indicates the presence or absence of the voice. The device and method are well suited for portable audio devices, such as true wireless stereo headphones, that have a limited power supply.

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Desio, Italy

Stefano Paolo Rivolta