Apple Inc.

Vinay R Majjigi

Syed Aon Mujtaba

Xiaowen Wang

Tarik Tabet

Christian W Mucke

Wen Zhao

Hung A Pham

Karthik Jayaraman Raghuram

Kee-Bong Song

Paul V Flynn

William Burchill

Bharath Narasimha Rao

James P Ochs

Umamahesh Srinivas

Adeeti V Ullal

Yann Jerome Julien Renard

Dawei Zhang

Isabel Mahe

Xing Tan

Stephen P Jackson

Johnson O Sebeni

Joonsuk Kim

Ajoy K Singh

Alexander Singh Alvarado

Adam S Howell

Gunes Dervisoglu

Yuchul Kim

Samy Khay-Ibbat

Youngjae Kim

Edith Merle Arnold

Ronald Keryuan Huang

Sarma V Vangala

Robert William Mayor

Christopher J Hansen

Navid Damji

Daniel Ulbricht

Jay C Blahnik

Isaac Thomas Miller

Karthik Anantharaman

Tahir Shamim

Oleg Naroditsky

Patrick James Coleman

Mohit Narang

Sreevalsan Vallath

Indranil S Sen

Jay Kriz Blahnik

Jonathan M Beard

Sunny K Chow

Craig H Mermel

Young Jae Kim

Robin T Guers

Sriram Venkateswaran

Sean P Cier

Hyojoon Bae

Parisa Dehleh Hossein Zadeh

Rami Y Hindiyeh

Christian Lipski

Telford Earl Forgety, Iii

Saurabh Godha

Kevin Sheridan

Olivier Humblet

Oliver Ruepp

Erin Paeng

Omar Aziz

Romain Trilling

Jennifer Strasser

Matthew L Semersky

Digvijay Arjunrao Jadhav

Ming Hu

Duy N Phan

Sunny Kai Pang Chow

Mrinal Agarwal

Huayu Ding

Anh N Phan

Daniel J Perry

Xiaoyue Zhang

Ryan P Kent

Henry Choi

Julia K Nichols

Suresh B Malakar

Tejal Bhamre

Pedro O Varangot

Lauren Schutz

David Ojeda Avellaneda

Richard G Liu

Tianye Sun

Alvaro J Melendez Hasbun

Ethan Goolish

Paul D Bryan

Yi Wen Liao

Alexandru Popovici

Aniket Aranake

Rebecca L Clarkson

Mariah W Whitmore

Lucie A Huet

Mark P Sena

Manjunath Shankar Rao

William S Burchill

Karthik Anatharaman

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 receives sensor data obtained by one or more sensor over a time period. The one or more sensors are worn by a user. Further, the mobile device determines a context of the user based on the sensor data, and obtains a set of rules for processing the sensor data based on the context, where the set of rules is specific to the context. The mobile device determines at least one of a likelihood that the user has fallen or a likelihood that the user requires assistance based on the sensor data and the set of rules, and generates one or more notifications based on at least one of the likelihood that the user has fallen or the likelihood that the user requires assistance.

Context aware fall detection 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

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

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

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

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

Location mapping and navigation user interfaces may be generated and presented via mobile computing devices. A mobile device may detect its location and orientation using internal systems, and may capture image data using a device camera. The mobile device also may retrieve map information from a map server corresponding to the current location of the device. Using the image data captured at the device, the current location data, and the corresponding local map information, the mobile device may determine or update a current orientation reading for the device. Location errors and updated location data also may be determined for the device, and a map user interface may be generated and displayed on the mobile device using the updated device orientation and/or location data.

Mobile device navigation system

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

This disclosure relates to TTI bundling for downlink communication. According to one embodiment, a base station and a wireless device may establish a wireless communication link. The base station may determine to enable TTI bundling for downlink communication for at least one carrier of the wireless communication link. The base station may provide an indication to the wireless device to enable TTI bundling for downlink communication for the determined carrier(s) of the wireless communication link. The base station may subsequently transmit TTI bundle downlink communications to the wireless device via the determined carrier(s).

TTI bundling for downlink communication

In an example method, a mobile device obtains a database including a plurality of data records. Each data record includes an indication of a respective impact previously experienced by a user of the mobile device, and sensor data generated by one or more first sensors worn by the user during that impact. The mobile device obtains sensor data generated by one or more second sensors worn by the user over a period of time, and determines whether the user has fallen during the period of time based on the database and the additional sensor data. The mobile device generates one or more notifications based on the determination of whether the user has fallen during the period of time.

Growing community of inventors

Sunnyvale, CA, United States of America

Vinay R Majjigi