Apple Inc.

Other

Hung A Pham

Karthik Jayaraman Raghuram

Alexander Singh Alvarado

Xing Tan

Umamahesh Srinivas

Vinay R Majjigi

Adam S Howell

Adeeti V Ullal

Gunes Dervisoglu

Yann Jerome Julien Renard

Xiaoyuan Tu

Hengliang Zhang

Craig H Mermel

Ronald Keryuan Huang

Bharath Narasimha Rao

Stephen P Jackson

James P Ochs

Richard Channing Moore, Iii

Edith Merle Arnold

Sunny K Chow

Jonathan M Beard

Parisa Dehleh Hossein Zadeh

Mrinal Agarwal

Sheena Sharma

Huayu Ding

Rami Y Hindiyeh

Anh N Phan

Harshavardhan Mylapilli

Geoffrey Louis Chi-Johnston

Vivek Garg

Sunny Kai Pang Chow

Telford Earl Forgety, Iii

Robin T Guers

John B Morrell

Brian T Gleeson

Ben-Heng Juang

Anil K Kandangath

Thayne M Miller

Hue Duc Tran

Daniel M Trietsch

Xiaoyue Zhang

Sriram Venkateswaran

Shameek P Ganguly

Akifumi Kobashi

Omar Aziz

Erin Paeng

Srinivasan Nimmala

Steven Porter Hotelling

Tang Yew Tan

Cheng Kai Chen

Colin M Ely

Erik G De Jong

Erik L Wang

Tyler S Bushnell

Emily Clark Schubert

Indranil S Sen

Venkateswara Rao Manepalli

Brad G Boozer

Jiaying Wu

Parin Patel

Robert Caston Curtis

Lu Zhang

Richard B Warren

Vijay Kumar Ramamurthi

Andrew M Wadycki

Paul V Johnson

Digvijay Arjunrao Jadhav

Venu Madhav Duggineni

Duy N Phan

Andre M Boule

Kenneth Waters

Jonathan C King

Wesley Scott Smith

Maulik V Choksi

Daniel J Perry

Christopher D Guichet

See-Ho Tsang

Xiao Xiao

Henry Choi

Christopher E Balcells

John D Blackwell

Martin M Menzel

Alexander Joshua Wiens

Tejal Bhamre

Allison L Gilmore

Julia K Nichols

Halil Ibrahim Basturk

Katherine E Niehaus

Suresh B Malakar

Parisa Deleh Hossein Zadeh

Manjunath Shankar Rao

Bor-rong Chen

Dan Marvin Trietsch

Theodore Lao

Chintan S Parikh

William R Powers, Iii

Mark P Sena

Maryam Etezadi-Amoli

Lucie A Huet

Irida Mance

Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone). In some embodiments, a method comprises: detecting, with at least one processor, a crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.

Crash detection on mobile device

Disclosed embodiments include wearable devices and techniques for detecting the end of hiking activities. By accurately and promptly detecting the end of hiking activities automatically, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording a hiking activity.

Detecting the end of hiking activities on a wearable device

In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

Detecting falls using a mobile device

The present disclosure relates to methods and systems of determining swimming metrics of a user during a swimming session. The method can include receiving, by a processor circuit of a user device, motion information from one or more motion sensors of the user device; determining, by the processor circuit using the motion information, a first set of rotational data of the user device, wherein the first set of rotational data is expressed in a first frame of reference; converting, by the processor circuit, the first set of rotational data into a second set of rotational data, wherein the second set of rotational data is expressed in a second frame of reference; determining, by the processor circuit, one or more swimming metrics of the user; and outputting the one or more swimming metrics.

Systems and methods for determining swimming metrics

In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Disclosed embodiments include wearable devices and techniques for detecting walking workouts. By accurately and promptly detecting the start of walking workouts activities and automatically distinguishing between walking workout and causal walking activities, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording walking workouts.

Detecting outdoor walking workouts on a wearable device

Embodiments are disclosed for head dimension estimation for spatial audio applications. In an embodiment, a method comprises: obtaining, using one or more processors of an audio headset worn on a user's head, acceleration samples and rotation rate samples over a specified time window while the user rotates their head, the acceleration samples and rotation rate samples measured using motion sensors in the headset; determining a function that relates the acceleration samples to the rotation rate samples; comparing the function to a plurality of reference functions, where each reference function corresponds to a different head dimension in a nominal range of head dimensions; and estimating a dimension of the user's head based on the comparing.

Head dimension estimation for spatial audio applications

A system may have electronic devices with wireless communications circuitry. Devices may transmit short-range wireless signals such as advertising packets. Each advertising packet may contain information on the motion context of the transmitting device. By comparing motion context information for received packets with local motion context information, an electronic device may analyze received signal strength indicator measurements with enhanced accuracy. Presence-in-vehicle state information and/or other device context may also be obtained from nearby devices by comparing local and received motion context.

Electronic devices with motion-based proximity sensing

One or more electronic device may use motion and/or activity sensors to estimate a user's 6 minute walking distance. In particular, because users typically walk at less than their maximum output and in imperfect conditions, control circuitry within the device(s) may rely on walks of shorter distances to estimate the 6 minute walking distance. For example, the control circuitry may gather activity information for the user, such as heart rate, calories burned, and step count, and analyze a distance component and a speed component for periods in which the user has walked. Individual 6 minute walk distance estimates may be generated based on each of the activity information, distance component, and speed component. The distance and speed estimates may be corrected for walking behaviors that deviate from an ideal testing environment, and may then be fused with the activity estimate to generate a final 6 minute walk distance estimate.

Electronic devices with 6 minute walk distance estimates

Disclosed embodiments include wearable devices and techniques for detecting cardio machine activities, estimating user direction of travel, and monitoring performance during cardio machine activities. By accurately and promptly detecting cardio machine activities and automatically distinguishing between activities performed on different types of cardio machines, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording activities on a wide variety of cardio machines. In various embodiments, cardio machine activity detection techniques may use magnetic field data from a magnetic field sensor to improve the accuracy of orientation data and device heading measurements used to detect the end of a cardio machine activity.

Detecting the end of cardio machine activities on a wearable device

Ear buds may have optical proximity sensors and accelerometers. Control circuitry may analyze output from the optical proximity sensors and the accelerometers to identify a current operational state for the ear buds. The control circuitry may also analyze the accelerometer output to identify tap input such as double taps made by a user on ear bud housings. Samples in the accelerometer output may be analyzed to determine whether the samples associated with a tap have been clipped. If the samples have been clipped, a curve may be fit to the samples. Optical sensor data may be analyzed in conjunction with potential tap input data from the accelerometer. If the optical sensor data is ordered, a tap input may be confirmed. If the optical sensor data is disordered, the control circuitry can conclude that accelerometer data corresponds to false tap input associated with unintentional contact with the housing.

Wireless ear buds

Growing community of inventors

Oakland, CA, United States of America

Hung A Pham