Apple Inc.

Umamahesh Srinivas

Karthik Jayaraman Raghuram

Hung A Pham

Alexander Singh Alvarado

Xing Tan

Bharath Narasimha Rao

Xiaoyuan Tu

Adeeti V Ullal

Vinay R Majjigi

Ronald Keryuan Huang

Yann Jerome Julien Renard

James P Ochs

Richard Channing Moore, Iii

Sheena Sharma

Gunes Dervisoglu

Hengliang Zhang

Craig H Mermel

Mrinal Agarwal

Huayu Ding

Harshavardhan Mylapilli

Geoffrey Louis Chi-Johnston

Vivek Garg

Kevin Will Chen

Kevin M Lynch

Adam S Howell

Reed E Olsen

Edith Merle Arnold

Sunny K Chow

Parisa Dehleh Hossein Zadeh

Aditya Sarathy

Robin T Guers

Jay C Blahnik

Rami Y Hindiyeh

Kevin Sheridan

Indranil S Sen

Jay Kriz Blahnik

Olivier Humblet

Jennifer Strasser

Anil K Kandangath

Stephen P Jackson

Digvijay Arjunrao Jadhav

Jonathan M Beard

Jonathan C King

Sunny Kai Pang Chow

Daniel M Trietsch

Xiaoyue Zhang

John-Peter E Cafaro

Parisa Deleh Hossein Zadeh

Dan Marvin Trietsch

Seyyedeh Mahsa Mirzargar

Keyu Chen

Yongyang Nie

Rene F Aguirre Ramos

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

Embodiments are disclosed for posture and motion monitoring using mobile devices. In an embodiment, a method comprises: obtaining motion data obtained from at least one motion sensor worn by a user; obtaining at least one frame of skeletal data of the user based on at least one of camera data or depth data; calibrating, with the at least one processor, the motion data and skeletal data to determine a calibration offset; synchronizing the motion data and skeletal data; generating, with the at least one processor, a body pose of the user by: aligning reference frames of the synchronized motion data and the synchronized skeletal data using the calibration offset; estimating the body pose of the user based on the aligned motion data and skeletal data; and classifying, using a machine learning model, the estimated body pose of the user.

Posture and motion monitoring using mobile devices

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.

Detecting falls using a mobile 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 disabling/re-enabling head tracking for spatial audio applications. In an embodiment, a method comprises: obtaining, using one or more processors of an auxiliary device worn by a user, motion data; tracking, using the one or more processors, the user's head based at least in part on the motion data; determining, using the one or more processors, whether or not the user is walking based at least in part on the motion data; in accordance with determining that the user is walking, determining if a source device configured to deliver spatial audio to the auxiliary device is static for a specified period of time; and in accordance with determining that the user is walking and the source device is static for the specified period of time, disabling the head tracking.

Disabling/re-enabling head tracking for distracted user of spatial audio application

In an example method, a mobile device receives motion data obtained by one or more sensors over a time period, where the one or more sensors are worn by a user, The mobile device determines, based on the motion data, an impact experienced by the user during the time of period, and determines one or more of characteristics of the user. The mobile device determines, based on the motion data and the one or more characteristics of the user, a likelihood that the user requires assistance subsequent to the impact, and generates one or more notifications based on likelihood.

In an example method, a mobile device receives motion data obtained by one or more sensors worn by a user. The mobile device determines, based on the motion data, that the user has fallen at a first time and whether the user has moved between a second time and a third time subsequent to the first time. Upon determining that the user has not moved between the second time and the third time, the mobile device initiates a communication to an emergency response service at a fourth time after the third time. The communication includes an indication that the user has fallen and a location of the user.

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.

Growing community of inventors

Milpitas, CA, United States of America

Umamahesh Srinivas