Verizon Patent and Licensing Inc.

Verizon Telematics Inc.

Other

Hti Ip, LLC

Stephen Christopher Welch

James Ronald Barfield

Thomas Steven Taylor

Robert S Siegel

Marc Edmond Barbier

A wearable fall-detection device has a variety of sensors, including a pressure sensor, that provide signals for sampling environmental conditions acting on the device. An average of pressure data samples is used to determine a resultant that may indicate an amount of noise in a pressure data signal, and statistical analysis of the noise and the pressure signal average may be used to determine a confidence estimate value that indicates a level of confidence in the amount of noise that a pressure signal is subject to, or includes. The confidence estimate and known fall data, such as change in pressure between a person standing and lying, can create a threshold function that may adapt according to sampled data thus providing a customizable (either statically or dynamically) threshold function for comparing sensor data against rather than comparing data with just a linear threshold function.

Method to determine a pressure noise metric for fall detection systems

Techniques described herein may be used to provide a driver of a vehicle with an accurate assessment of the remaining life of the vehicle battery. An on-board device may collect information from one or more sensors or devices within the vehicle. The information may be processed to generate a data set that accurately describes the current status and operating conditions of the battery. The data set may be used to evaluate the health of the battery and make predictions regarding the future performance of the battery, which may be communicated to the driver of the vehicle. Machine-learning techniques may be implemented to improve upon methodologies to evaluate the health of the battery and make predictions regarding battery performance.

Systems and methods for determining vehicle battery health

Vehicle data may be analyzed to predict potential component failures, diagnostic trouble codes (DTCs), or other mechanical failures relating to the vehicle. In one implementation the vehicle data may be received from a number of vehicles, the vehicle data including DTCs generated by on-board diagnostic (OBD) systems of the vehicles. The vehicle data may be evaluated using a predictive model to output predictions of DTCs that are likely to occur for a particular vehicle.

Analysis of vehicle data to predict component failure

Information identifying one or more conditions associated with a vehicle is collected. This information may include first data associated with the vehicle, second data associated with a driver of the vehicle, and third data associated with an environment around the vehicle. The information may be collected from a vehicle sensor, a telematics unit, a user device associated with the driver or a passenger, or a remote server. A likelihood of a collision involving the vehicle, a likelihood of avoiding the collision, and a risk associated with the collision may be determined based on the collected data. An appropriate response may be selected based on the likelihood of the collision, the likelihood of avoiding the collision, and the risk associated with the collision.

Collision prediction system

Techniques described herein relate to the classification of fall events for PER (personal emergency response) devices. In one implementation, data relating to acceleration events that occurred at the PER devices may be received. The data relating to the acceleration events may be associated with indications of whether the acceleration events correspond to fall events of users of the PER devices. A classification model may be trained based on the data relating to the acceleration events and the indications of whether the data relating to the acceleration events corresponds to the fall events. The classification model may be transmitted to at least some of the PER devices to update a previous version of the classification model at the at least some of the PER devices.

Adaptive classification of fall detection for personal emergency response systems

A system comprise a server configured to communicate vehicle information with a vehicle transceiver of a vehicle moving along a vehicle route and communicate drone information with a drone transceiver of a drone moving along a drone route. A computing device with a memory and a processor may be configured to communicatively connect with the server, process the vehicle information and the drone information, identify a plurality of pickup locations based in part on the vehicle information and drone information, select at least one of the plurality of pickup locations based in part on a priority score associated with a travel time to or wait time for each of the plurality of pickup locations, and update the drone route based in part on the selected pickup location.

Method and system for drone deliveries to vehicles in route

Vehicle collisions may be automatically detected and reported based to a call center. The collisions may be automatically detected based on a collision detection model that receives sensor data, or other data, as input, and outputs an indication of whether there is a collision. The collision detection model may be trained on historical sensor data associated with potential vehicle collisions, where the historical sensor data is labeled to indicate whether the data corresponds to an actual collision.

Automatic vehicle crash detection using onboard devices

A personal emergency response (PER) device may include an audio sensor that is used to enhance the operation of the PER device. The audio sensor may obtain audio data that can be used to verify whether a fall event that is detected by the PER device is a false positive signal and/or enhance the reliability of a detected fall event. In some implementations, the audio data may be used in the detection of vehicle crashes in which a user of the PER device is involved.

Using audio signals in personal emergency response systems

A device may receive sensor data regarding a user device; and determine, based on the sensor data, that a user of the user device is in a vehicle or driving the vehicle. When determining that the user of the user device is in the vehicle or driving the vehicle, the device may compare the sensor data, received from the user device, with a reference dataset that includes reference data associated with users being present in or driving a vehicle, or determine that a value of a measurement, received as part of the sensor data, satisfies a threshold that is related to whether the user is in the vehicle or driving the vehicle. The device may output a particular control instruction to the user device based on determining that the user is in the vehicle or is driving the vehicle.

Determining that a user is in a vehicle or driving a vehicle based on sensor data gathered by a user device

The axes of an accelerometer, installed in a vehicle at an arbitrary orientation, may be realigned to the coordinate frame of the vehicle. In one implementation, a method may include determining, based on acceleration measurements from the accelerometer that likely corresponds to stopping, a dominant orientation of the accelerometer in relation to gravity, including calculating a first transformation angle and a second transformation angle as parameters to perform coordinate realignment of a coordinate frame of the accelerometer to a coordinate frame of the vehicle. The method may further include identifying, based on the acceleration measurements, an occurrence of acceleration events of the vehicle; determining, based on an analysis of the acceleration events, a third transformation angle; and storing the first, second, and third transformation angles.

Automatic alignment of a vehicle three-axes accelerometer

A system and method is disclosed that facilitates the processing of a sound signal. In embodiments, an input sound signal can be processed according to a computational model using predetermined parameters. A sound signal originating from a musical instrument can be processed according to coefficients that are generated using a learning model.

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Atlanta, GA, United States of America

Stephen Christopher Welch