Abstract: A device including microelectromechanical systems (MEMS) sensors are used in dead reckoning in conditions where Global Positioning System (GPS) signals or Global Navigation Satellite System (GNSS) signals are lost. The device is capable of tracking the location of the device after the GPS/GNSS signals are lost by using MEMS sensors such as accelerometers and gyroscopes. By calculating a misalignment angle between a forward axis of a sensor frame of the device and a forward axis of a vehicle frame using the data received from the MEMS sensors, the device can accurately calculate the location of a user or the vehicle of the device even without the GPS/GNSS signals. Accordingly, a device capable of tracking the location of the user riding in the vehicle in GPS/GNSS signals absent environment can be provided.

Abstract: A device including microelectromechanical systems (MEMS) sensors is used in dead reckoning in conditions where Global Positioning System (GPS) signals or Global Navigation Satellite System (GNSS) signals are lost. The device is capable of tracking the location of the device after the GPS/GNSS signals are lost by using MEMS sensors such as accelerometers and gyroscopes. By calculating a misalignment angle between a sensor frame of the device with either the movement direction of the vehicle or the walking direction of a pedestrian using the MEMS sensors, the device can accurately calculate the location of a user of the device even without the GPS/GNSS signals. Accordingly, a device capable of tracking the location of a pedestrian and a user riding in a vehicle without utilizing GPS/GNSS signals can be provided.

Abstract: A system may include a motion sensor configured to generate a motion signal in response to a movement of an electronic device, and at least one feature detection circuit configured to determine at least one metric based on the motion signal. The system may further include a classifying circuit configured to determine whether the electronic device is in contact with a human body based on the at least one metric.

Abstract: Disclosed herein is a method of operating an electronic device. The method includes activating a first sensing device, and determining a first probabilistic context of the electronic device relative to its surroundings. The method includes outputting the first probabilistic context, and determining a confidence measure of the first probabilistic context. Where the confidence measure of the first probabilistic context is below a threshold, the method includes activating a second sensing device, determining a second probabilistic context of the electronic device relative to its surroundings. outputting the second probabilistic context, and determining a confidence measure of the second probabilistic context. Where the confidence measure of the second probabilistic context is above the threshold, the second sensing device is deactivated and the method returns to determining the first probabilistic context.

Abstract: A sensor chip includes registers storing and outputting configuration data, an extraction circuit receiving digital data and extracting features of the digital data in accordance with the configuration data, and a classification circuit applying a decision tree to the extracted features to generate a context of an electronic device into which the sensor chip is incorporated relative to its surroundings, the decision tree operating according to the configuration data. The classification unit outputs the context to the registers for storage. The configuration data includes which features for the extraction circuit to extract from the digital data, and a structure for the decision tree. The structure for the decision tree includes conditions that the decision tree is to apply to the at least one extracted feature, and outcomes to be effectuated based upon whether the extracted features meet or do not meet the conditions.

Abstract: The disclosure describes methods and apparatus for quickly prototyping of a solution developed using one or more sensing devices (e.g., sensors), functional blocks, algorithm libraries, and customized logic. The methods produce firmware executable by a processor (e.g., a microcontroller) on an embedded device such as a development board, expansion board, or the like. By performing these methods on the apparatus described, a user is able to create a function prototype without having deep knowledge of the particular sensing device or any particular programming language. Prototypes developed as described herein enable the user to rapidly test ideas and develop sensing device proofs-of-concept. The solutions produced by the methods and apparatus improve the functioning of the sensor being prototyped and the operation of the embedded device where the sensor is integrated.

Abstract: Motion activity data is collected from at least one sensor. An initial motion activity classifier function is applied to the motion activity data to produce an initial motion activity posteriorgram. Pre-processing and segmenting the motion activity data into windows produces segmented motion activity data from which sensor specific features are extracted. An updated motion activity classifier function is generated from the extracted sensor specific features. Subsequent motion activity data is also collected from the at least one sensor, and the updated motion activity classifier function is applied to the subsequent motion activity data to produce an updated motion activity posteriorgram.

Abstract: A method and apparatus for classifying a spatial environment as open or enclosed are provided. In the method and apparatus, one or more microphones detect ambient sound in a spatial environment and output an audio signal representative of the ambient sound. A processor determines a spatial environment impulse response (SEIR) for the audio signal and extracts one or more features of the SEIR. The processor classifies the spatial environment as open or enclosed based on the one or more features of the SEIR.

Abstract: A portable device includes one or more memories and travel mode control circuitry coupled to the one or more memories. The travel mode control circuitry, in operation, monitors motion data and temperature data to detect a first travel state of the device. When the first travel state of the device is detected, motion data and pressure data are monitored to detect a transition from the first travel state to a second travel state of the device. When the transition to the second travel state of the device is detected, one or more control signals are generated to cause the device to enter a first travel mode of operation.

Abstract: In general, the invention relates to an algorithm and process for automated and/or continuous calibration of magnetic sensor, for example such as a sensor installed in a mobile positioning system handset. According to certain aspects, the calibration process can use the normal motion of the handset such that all measurement data from the three orthogonal axes of sensor when exposed to Earth's magnetic field is collected. According to still further aspects, the process includes fitting measurement data to an ellipsoid that characterizes the actual magnetic field measurements from a magnetic sensor, so that anomalies such as hard iron effect, soft iron effect and scale factors can be extracted and/or corrected by comparison to a sphere represented by magnetic field data from a model at the sensor's location.

Abstract: Described herein is a method of operating an electronic device that includes collecting initial motion activity data from at least one sensor of the electronic device, and generating a initial probabilistic context of the electronic device relative to its surroundings from the initial collected motion activity data using a motion activity classifier function. The collected motion activity data is stored in a training data set, and the motion activity classifier function is updated using the training data set. The method also includes collecting subsequent motion activity data from the at least one sensor of the electronic device, and generating a subsequent probabilistic context of the electronic device relative to its surroundings from the subsequently collected motion activity data using the updated motion activity classifier function.

Abstract: A sensor chip includes registers storing and outputting configuration data, an extraction circuit receiving digital data and extracting features of the digital data in accordance with the configuration data, and a classification circuit applying a decision tree to the extracted features to generate a context of an electronic device into which the sensor chip is incorporated relative to its surroundings, the decision tree operating according to the configuration data. The classification unit outputs the context to the registers for storage. The configuration data includes which features for the extraction circuit to extract from the digital data, and a structure for the decision tree. The structure for the decision tree includes conditions that the decision tree is to apply to the at least one extracted feature, and outcomes to be effectuated based upon whether the extracted features meet or do not meet the conditions.

Abstract: A portable device includes one or more memories and travel mode control circuitry coupled to the one or more memories. The travel mode control circuitry, in operation, monitors motion data and temperature data to detect a first travel state of the device. When the first travel state of the device is detected, motion data and pressure data are monitored to detect a transition from the first travel state to a second travel state of the device. When the transition to the second travel state of the device is detected, one or more control signals are generated to cause the device to enter a first travel mode of operation.

Abstract: An electronic device described herein includes a sensing unit having at least one sensor to acquire sensing data. An associated computing device extracts sensor specific features from the sensing data, and generates a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features. The motion activity vector, voice activity vector, and spatial environment vector are processed to determine a base level context of the electronic device relative to its surroundings, with the base level context having aspects each based on the motion activity vector, voice activity vector, and spatial environment vector. Meta level context of the electronic device relative to its surroundings is determined as a function of the base level context, with the meta level context being at least one inference made from at least two aspects of the plurality of aspects of the base level context.

Abstract: A sensor chip is mounted on a PCB and electrically connected to a SOC mounted on the PCB via at least one conductive trace. The sensor chip includes configuration registers storing and outputting configuration data, and a PLD receiving digital data. The PLD performs an extraction of features of the digital data in accordance with the configuration data, and the configuration data includes changeable parameters of the extraction. A classification unit processes the extracted features of the digital data so as to generate a context of an electronic device into which the sensor chip is incorporated relative to its surroundings, the processing being performed in using a processing technique operating in accordance with the configuration data. The configuration data also includes changeable parameters of the processing technique. The classification unit outputs the context to data registers for storage.

Abstract: The disclosure describes methods and apparatus for quickly prototyping of a solution developed using one or more sensing devices (e.g., sensors), functional blocks, algorithm libraries, and customized logic. The methods produce firmware executable by a processor (e.g., a microcontroller) on an embedded device such as a development board, expansion board, or the like. By performing these methods on the apparatus described, a user is able to create a function prototype without having deep knowledge of the particular sensing device or any particular programing language. Prototypes developed as described herein enable the user to rapidly test ideas and develop sensing device proofs-of-concept. The solutions produced by the methods and apparatus improve the functioning of the sensor being prototyped and the operation of the embedded device where the sensor is integrated.

Abstract: A distributed computing system for artificial intelligence in autonomously appreciating a circumstance context of a smart device. Raw context data is detected by sensors associated with the smart device. The raw context data is pre-processed by the smart device and then provided to a cloud based server for further processing. At the cloud based server, various sets of feature data are obtained from the pre-processed context data. The various sets of feature data are compared with corresponding classification parameters to determine a classification of a continuous event and/or a classification of transient event, if any, which occur in the context. The determined classification of the continuous event and the transient event will be used to autonomously configure the smart device or another related smart device to fit the context.

Abstract: A method and apparatus for auto-directive adaptive beamforming for a microphone array using microelectromechanical systems (MEMS) sensor orientation information are provided. The microphone array captures audio and the MEMS sensor detects an orientation of the microphone array. A direction of arrival of a source signal is estimated based on the data representative of the audio. A change in an orientation of the microphone array is detected based on the orientation and the direction of arrival is compensates based on the change in the orientation of the microphone array. The apparatus pre-steers a beam of a beam pattern of the microphone array based on the compensated direction of arrival to retain the source signal in a broadside of the microphone array and performs adaptive wideband beamforming to null one or more interfering sources in the beam pattern while retaining the source signal in the broadside of the microphone array.

Abstract: A sensor chip is mounted on a PCB and electrically connected to a SOC mounted on the PCB via at least one conductive trace. The sensor chip includes configuration registers storing and outputting configuration data, and a PLD receiving digital data. The PLD performs an extraction of features of the digital data in accordance with the configuration data, and the configuration data includes changeable parameters of the extraction. A classification unit processes the extracted features of the digital data so as to generate a context of an electronic device into which the sensor chip is incorporated relative to its surroundings, the processing being performed in using a processing technique operating in accordance with the configuration data. The configuration data also includes changeable parameters of the processing technique. The classification unit outputs the context to data registers for storage.

Abstract: The present invention relates generally to position determination, and more particularly to methods and apparatuses to determine the walking direction (heading of a user) using MEMS inertial sensors commonly found in mobile devices such as cellphones. According to some aspects, the invention includes a methodology to allow the walking angle to be determined dynamically. In embodiments, the methodology includes pattern matching accelerometer measurement data to a reference acceleration pattern.