Apple Inc.

Other

Karthik Jayaraman Raghuram

Hung A Pham

Alexander Singh Alvarado

Xing Tan

Umamahesh Srinivas

Vinay R Majjigi

Adeeti V Ullal

James P Ochs

Yann Jerome Julien Renard

Bharath Narasimha Rao

Craig H Mermel

Richard Channing Moore, Iii

Adam S Howell

Gunes Dervisoglu

Edith Merle Arnold

Ronald Keryuan Huang

Stephen P Jackson

Hengliang Zhang

Huayu Ding

Sheena Sharma

Xiaoyuan Tu

Mrinal Agarwal

Parisa Dehleh Hossein Zadeh

Rami Y Hindiyeh

Vivek Garg

Jay C Blahnik

Jay Kriz Blahnik

Sunny K Chow

Geoffrey Louis Chi-Johnston

Harshavardhan Mylapilli

Jonathan M Beard

Telford Earl Forgety, Iii

Daniel M Trietsch

Robin T Guers

Kevin Sheridan

Xiaoyue Zhang

Olivier Humblet

Sriram Venkateswaran

Akifumi Kobashi

Erin Paeng

Omar Aziz

Jennifer Strasser

Alexander Kanaris

Kenneth Waters

Sunny Kai Pang Chow

Daniel J Perry

Anh N Phan

James Michael Silva

Barker N Bhaskaran

Henry Choi

Allison L Gilmore

Julia K Nichols

Suresh B Malakar

Katherine E Niehaus

Tejal Bhamre

Tianye Sun

Pedro O Varangot

Aniket Aranake

Alexandru Popovici

Yi Wen Liao

Paul D Bryan

Ethan Goolish

Alvaro J Melendez Hasbun

Sumayah F Rahman

Richard G Liu

Lauren Schutz

Vinay R Majigi

David Ojeda Avellaneda

Rebecca L Clarkson

Kyle A Reed

Britni A Crocker

Mariah W Whitmore

Maxsim L Gibiansky

Irida Mance

Lucie A Huet

Maryam Etezadi-Amoli

Mark P Sena

William R Powers, Iii

Paige N Stanley

Di Wu

Dan Marvin Trietsch

Parisa Deleh Hossein Zadeh

Manjunath Shankar Rao

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

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

In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

Wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models

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

Embodiments are disclosed for estimating caloric expenditure using a heart rate model specific to a motion class. In an embodiment, a method comprises: obtaining acceleration and rotation rate from motion sensors of a wearable device; determining a vertical component of inertial acceleration and a vertical component of rotational acceleration from the acceleration and rotation rate, respectively; determining a magnitude of the rotation rate; determining a correlation between the inertial vertical acceleration component and rotational acceleration; determining a percentage of motion outside a dominant plane of motion; predicting a motion class based on a motion classification model that takes as input the motions, correlation and percentage; determining a likelihood the user is walking; in accordance with determining that the user is likely not walking, configuring a heart rate model based on the predicted motion class; and estimating, using the configured heart rate model, a caloric expenditure of the user.

Estimating caloric expenditure using heart rate model specific to motion class

Growing community of inventors

Santa Clara, CA, United States of America

Karthik Jayaraman Raghuram