Apple Inc.

Bharath Narasimha Rao

Umamahesh Srinivas

Karthik Jayaraman Raghuram

Vinay R Majjigi

Hung A Pham

James P Ochs

Madhusudan Chaudhary

Karthik Anantharaman

Alexander Singh Alvarado

Prashant H Vashi

Xing Tan

Gunes Dervisoglu

Cherif Jazra

Leonardo A Soto Matamala

Kevin Will Chen

Kevin M Lynch

Vikram Bhaskara Yerrabommanahalli

Reed E Olsen

Adeeti V Ullal

Craig H Mermel

Jonathan M Beard

Sunny K Chow

Robin T Guers

Ronald Keryuan Huang

Sree Ram Kodali

Yannick L Sierra

Jay C Blahnik

Sachin J Sane

Adam S Howell

Srinivas Pasupuleti

Abhishek Sen

Edith Merle Arnold

Mrinal Agarwal

Rami Y Hindiyeh

Brian H Cassidy

Kevin Sheridan

Olivier Humblet

Jennifer Strasser

Syed Aon Mujtaba

Kee-Bong Song

Xiaoyuan Tu

Xiaowen Wang

Jay Kriz Blahnik

Naveen Rawat

Aravind Radhakrishnan

Youngjae Kim

Stephen P Jackson

Richard Channing Moore, Iii

Anh N Phan

Aditya Sarathy

John-Peter E Cafaro

Erin Paeng

Suresh B Malakar

Vikram B Yerrabommenahalli

Lucie A Huet

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

Embodiments are disclosed for user posture transition detection and classification. In an embodiment, a method comprises: obtaining, using one or more processors, motion data from a headset worn by a user; determining, using the one or more processors, one or more windows of motion data that indicate biomechanics of one or more phases of a user's postural transition; and classifying, using the one or more processors, as the user's postural transition based on the one or more windows of data.

User posture transition detection and classification

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

Techniques are disclosed for facilitating disabling an alarm in response to particular types of activity-indicative data. More specifically, activity-indicative data (e.g., sensor data or input(s) can be detected prior to a preset alarm time. Upon determining, based on the activity-indicative data, that a wakefulness condition is satisfied (e.g., that the activity-indicative data corresponds to one or more predefined characteristics), a disablement query can be displayed that includes an option to disable the alarm. In response to detecting a selection of the option, the alarm can be disabled such that the alarm stimuli is not to be presented at the preset alarm time.

Methods and systems for disabling sleep alarm based on automated wake detection

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

Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models. In an embodiment, a method comprises: establishing, by a processor of a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining, by the processor using the communication connection, 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, by the processor, a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining, by the processor, a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining, by the processor, 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, by the processor to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

Embodiments are disclosed for indoor/outdoor detection using a mobile device, such as wearable computer (e.g., smartwatch). In an embodiment, a method comprises: receiving, by one or more processors of a wearable computer, wireless access point (AP) scan data, global navigation satellite system (GNSS) data and inertial sensor data; determining, by the one or more processors, a first state of the wearable computer based on the wireless AP scan data; determining, by the one or more processors, a second state of the wearable computer based on a comparison of the GNSS data and the inertial sensor data; and outputting, by the one or more processors, an indoor/outdoor signal indicating that the wearable computer is indoors or outdoors based on the first and second states.

Indoor/outdoor detection using a wearable computer

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 digitally stored map can indicate the signal quality for each of the map's regions. A device can determine its location, speed, and direction using global positioning system (GPS) and other sensors. Based on this information, the mobile device can predict a field of locations within which the device will probably be located within a specified future time frame. Based on both the information indicating signal quality and the probable future field of locations, the device can estimate a moment at which the device will probably begin to suffer from low-quality or absent signal. Using this prediction, the device can proactively perform a variety of anticipatory remedial actions. For example, the device can begin allocating a greater portion of currently available wireless network communication bandwidth to the reception of data packets that represent content that is being streamed to the device, so that the device can proactively buffer those packets.

Location-assisted service capability monitoring

Growing community of inventors

Sunnyvale, CA, United States of America

Bharath Narasimha Rao