Apple Inc.

Mrinal Agarwal

Hung A Pham

Karthik Jayaraman Raghuram

Alexander Singh Alvarado

Umamahesh Srinivas

Xing Tan

Vinay R Majjigi

Bharath Narasimha Rao

Stephen P Jackson

Adeeti V Ullal

Richard Channing Moore, Iii

Ronald Keryuan Huang

Adam S Howell

Edith Merle Arnold

James P Ochs

Yann Jerome Julien Renard

Craig H Mermel

Sunny K Chow

Parisa Dehleh Hossein Zadeh

Sunny Kai Pang Chow

Daniel M Trietsch

Sriram Venkateswaran

Robin T Guers

Henry Choi

Omar Aziz

Tejal Bhamre

Tianye Sun

Alvaro J Melendez Hasbun

Richard G Liu

David Ojeda Avellaneda

Lauren Schutz

Pedro O Varangot

Rebecca L Clarkson

Aniket Aranake

Alexandru Popovici

Yi Wen Liao

Paul D Bryan

Ethan Goolish

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

The present disclosure relates to a system and method of detecting activity by a wheelchair user. In one aspect, a method comprises collecting motion data of a user device located on an appendage of the user; detecting, by a processor circuit, that one or more activities by the wheelchair user occurred based on the motion data; calculating, by a processor circuit, an energy expenditure by the user based the one or more activities by the wheelchair user occurred; and outputting, by a processor circuit, the energy expenditure estimation.

Detecting activity by a wheelchair user

The present disclosure relates to systems and methods of estimating energy expenditure of a user while swimming. A processor circuit of a user device can estimate a speed of the user based on a stroke rate and a stroke length. The processor circuit can estimate an efficiency of the user. The processor circuit can classify a swimming style of the user. The processor circuit can determine energy expenditure of the user based on the speed, the efficiency, and the style. The processor circuit can also detect glides of the user and adjust the energy expenditure.

Systems and methods of swimming calorimetry

A fitness tracking device configured to be worn by a user obtains a plurality of physical characteristics of the user including a first age and a sex of the user. The fitness tracking device maps each physical characteristic of the user to a corresponding index, wherein the first age of the user is mapped to a first age index of a first age range of a plurality of age ranges, and wherein the sex of the user is mapped to a first sex index. The fitness tracking device selects, from a memory of the fitness tracking device, a first calorimetry model of a plurality of calorimetry models, wherein the first calorimetry model is associated with each corresponding index, including the first age index and the first sex index of the user. The fitness tracking device estimates an energy expenditure rate using the first calorimetry model.

Local model for calorimetry

The present disclosure generally relates to user interfaces using tactile output. At a device, determine a first position of the device relative to a point of reference. After determining the first position, output, via one or more tactile output devices, a first tactile output, where the first tactile output is generated based on a first value of a characteristic that is selected in accordance with the first position. After outputting the first tactile output, detect a change in position of the device to a second position relative to the point of reference. In response to detecting the change in the position of the device, output a second tactile output that is different from the first tactile output, where the second tactile output is generated based on a second value of the characteristic that is selected in accordance with the second position of the device relative to the point of reference.

User interface using tactile output

In one aspect, the present disclosure relates to a method, including obtaining, by the fitness tracking device, motion data of the user over a period of time, wherein the motion data can include a first plurality motion measurements from a first motion sensor of the fitness tracking device; determining, by the fitness tracking device, using the motion data an angle of the fitness tracking device relative to a plane during the period of time; estimating by the fitness tracking device, using the motion data, a range of linear motion of the fitness tracking device through space during the period of time; and comparing, by the fitness tracking device, the angle of the fitness tracking device to a threshold angle and comparing the range of linear motion of the fitness tracking device to a threshold range of linear motion to determine whether the user is sitting or standing.

Method and system to estimate day-long calorie expenditure based on posture

Growing community of inventors

Cupertino, CA, United States of America

Mrinal Agarwal