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公开(公告)号：US20230124158A1
公开(公告)日：2023-04-20
申请号：US17832569
申请日：2022-06-03
Applicant: Apple Inc.
Inventor： Mariah W. Whitmore , Jaehyun Bae , Richard A. Fineman , Sheena Sharma , Asif Khalak , Adeeti V. Ullal
Abstract: Embodiments are disclosed for assessing walking steadiness of a mobile device user. In some embodiments, a method comprises: obtaining, with at least one processor of a mobile device, one or more mobility metrics indicative of a user's mobility, the mobility metrics obtained at least in part from a time series of sensor data output by at least one sensor of the mobile device; evaluating, with the at least one processor, the one or more mobility metrics over one or more specified time periods to derive one or more longitudinal features indicative of variability of the user's gait; and generating, with the at least one processor, at least one walking steadiness indicator for the user based on one or more walking steadiness component models and the one or more longitudinal features. Also disclosed are embodiments for training the component models.
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Patent Agency Ranking
公开(公告)号：US20230389824A1
公开(公告)日：2023-12-07
申请号：US18205472
申请日：2023-06-02
Applicant: Apple Inc.
Inventor： Allison L. Gilmore , Adeeti V. Ullal , Alexander G. Bruno , Eugene Song , Gabriel A. Blanco , James J. Dunne , João Antunes , Karthik Jayaraman Raghuram , Po An Lin , Richard A. Fineman , William R. Powers, III , Asif Khalak
CPC classification number: A61B5/112 , G16H50/20 , A61B5/681 , A61B5/7267
Abstract: Enclosed are embodiments for estimating gait time events and GCT using a wrist-worn device. In some embodiments, a method comprises: obtaining, with at least one processor of a wrist-worn device, sensor data indicative of acceleration and rotation rate; and predicting, with the at least one processor, at least one gait event time based on a machine learning (ML) model with the acceleration and rotation rate as input to the ML model.
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公开(公告)号：US20230390605A1
公开(公告)日：2023-12-07
申请号：US17952174
申请日：2022-09-23
Applicant: Apple Inc.
Inventor： Asif Khalak , Adeeti V. Ullal , Gabriel A. Blanco
CPC classification number: A63B24/0062 , G01C22/006 , G01C9/06 , A63B2220/18 , A63B2220/836
Abstract: Embodiments are disclosed for a biomechanical trigger for improved responsiveness in grade estimation. In some embodiments, a method comprises: A method comprises: obtaining, from a wearable device worn by a user, cadence data, speed data and elevation data; determining a grade of a surface on which the user is traveling based on a ratio of a change in elevation based on the elevation data and a change in speed data; determining that the grade satisfies a first condition indicative of a horizontal speed compensation by the user at a grade onset; determining that the grade satisfies a second condition indicative of a rapid elevation increase or decrease at a grade onset; and confirming that the grade is a valid estimate based on either the first condition or the second condition being satisfied.
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公开(公告)号：US20230112071A1
公开(公告)日：2023-04-13
申请号：US17832571
申请日：2022-06-03
Applicant: Apple Inc.
Inventor： Asif Khalak , Mariah W. Whitmore , Maxsim L. Gibiansky , Richard A. Fineman , Jaehyun Bae , Sheena Sharma , Carolyn R. Oliver , Mark P. Sena , Maryam Etezadi-Amoli , Allison L. Gilmore , William R. Powers, III , Edith M. Arnold , Gabriel A. Blanco , Sohum R. Thakkar , Adeeti V. Ullal
Abstract: Embodiments are disclosed for assessing fall risk of a mobile device user. In some embodiments, a method comprises: obtaining one or more mobility metrics indicative of a user’s mobility, the mobility metrics obtained at least in part from sensor data output by at least one sensor of the mobile device; evaluating the one or more mobility metrics over one or more specified time periods to derive one or more longitudinal features; estimating a plurality of walking steadiness indicators based on a plurality of component models and the one or more longitudinal features; inferring the user’s risk of falling based at least in part on the plurality of walking steadiness indicators; and initiating an action or application on the mobile device based at least in part on the user’s risk of falling.
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公开(公告)号：US20230147505A1
公开(公告)日：2023-05-11
申请号：US17985098
申请日：2022-11-10
Applicant: Apple Inc.
Inventor： Katherine Niehaus , Britni A. Crocker , Maxsim L. Gibiansky , William R. Powers, III , Allison L. Gilmore , Asif Khalak , Sheena Sharma , Richard A. Fineman , Kyle A. Reed , Karthik Jayaraman Raghuram , Adeeti V. Ullal
CPC classification number: A61B5/1118 , A61B5/4866
Abstract: Embodiments are disclosed for identifying poor cardio metabolic health using sensors of wearable devices. In an embodiment, a method comprises: obtaining estimates of maximal oxygen consumption of a user during exercise; determining at least one confidence weight based on context data; adjusting the maximal oxygen consumption estimates using the at least one confidence weight; aggregating the adjusted maximal oxygen consumption estimates to generate a summary maximal oxygen consumption estimate and corresponding confidence interval for the user; and classifying cardiorespiratory fitness of the user based on at least one of the summary maximum consumption estimate, the corresponding confidence interval, a population error model or a low cardiorespiratory fitness threshold.
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公开(公告)号：US20210393162A1
公开(公告)日：2021-12-23
申请号：US17338529
申请日：2021-06-03
Applicant: Apple Inc.
Inventor： Britni A. Crocker , Katherine Niehaus , Aditya Sarathy , Asif Khalak , Allison L. Gilmore , James P. Ochs , Bharath Narasimha Rao , Gabriel A. Quiroz , Hui Chen , Kyle A. Reed , William R. Powers, III , Maxsim L. Gibiansky , Paige N. Stanley , Umamahesh Srinivas, III , Karthik Jayaraman Raghuram , Adeeti V. Ullal
Abstract: One or more electronic device may use motion and/or activity sensors to estimate a user's maximum volumetric flow of oxygen, or VO2 max. In particular, although a correlation between heart rate and VO2 max may be linear at high heart rate levels, there is not a linear correlation at lower heart rate levels. Therefore, for users without extensive workout data, the motion sensors and activity sensors may be used to determine maximum calories burned by the user, workout data, including heart rate data, and body metric data. Based on these parameters, a personalized relationship between the user's heart rate and oxygen pulse (which is a function of VO2) may be determined, even with a lack of high intensity workout data. In this way, a maximum heart rate and therefore a VO2 max value may be approximated for the user.
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