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公开(公告)号：US20230389821A1
公开(公告)日：2023-12-07
申请号：US18205476
申请日：2023-06-02
Applicant: Apple Inc.
Inventor： Richard A. Fineman , Adeeti V. Ullal , Allison L. Gilmore , Gabriel A. Blanco , Karthik Jayaraman Raghuram , Mark P. Sena , Maryam Etezadi-Amoli , James J. Dunne , Po An Lin
CPC classification number: A61B5/1114 , A61B5/681 , A61B5/7264 , A61B2562/0219
Abstract: Enclosed are embodiments for estimating vertical oscillation (VO) at the wrist. In some embodiments, a method comprises: obtaining, with a wearable device worn on a wrist of a user, sensor data indicative of the user's acceleration and rotation rate; estimating centripetal acceleration based on the user's acceleration and rotation rate; calculating a modified user's acceleration by subtracting the estimated centripetal acceleration from the user's acceleration; estimating center of mass (CoM) acceleration by decoupling an arm swing component of the user's acceleration from the modified user's acceleration; and computing vertical oscillation of the user's CoM using a machine learning model with at least the CoM acceleration as input to the machine learning model, or by integrating vertical acceleration derived from the CoM acceleration and a gravity vector.
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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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Patent Agency Ranking
公开(公告)号：US11961332B1
公开(公告)日：2024-04-16
申请号：US17338499
申请日：2021-06-03
Applicant: Apple Inc.
Inventor： William R. Powers, III , Maryam Etezadi-Amoli , Britni A. Crocker , Allison L. Gilmore , Edith M. Arnold , Hung A. Pham , Irida Mance , Sumayah F. Rahman , Katherine Niehaus , Kyle A. Reed , Maxsim L. Gibiansky , Karthik Jayaraman Raghuram , Adeeti V. Ullal
CPC classification number: G06V40/25 , A61B5/1118 , A61B5/1123 , A61B5/6802 , A61B2562/0219
Abstract: One or more electronic device may use motion and/or activity sensors to estimate a user's 6 minute walking distance. In particular, because users typically walk at less than their maximum output and in imperfect conditions, control circuitry within the device(s) may rely on walks of shorter distances to estimate the 6 minute walking distance. For example, the control circuitry may gather activity information for the user, such as heart rate, calories burned, and step count, and analyze a distance component and a speed component for periods in which the user has walked. Individual 6 minute walk distance estimates may be generated based on each of the activity information, distance component, and speed component. The distance and speed estimates may be corrected for walking behaviors that deviate from an ideal testing environment, and may then be fused with the activity estimate to generate a final 6 minute walk distance estimate.
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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：US20230392953A1
公开(公告)日：2023-12-07
申请号：US18205478
申请日：2023-06-02
Applicant: Apple Inc.
Inventor： Lucie A. Huet , Adeeti V. Ullal , Allison L. Gilmore , Gabriel A. Blanco , Karthik Jayaraman Raghuram , Maryam Etezadi-Amoli , Richard A. Fineman
CPC classification number: G01C22/006 , A61B5/112 , A61B5/681 , G01C25/00 , A61B2562/0219
Abstract: Embodiments are disclosed for stride length estimation and calibration at the wrist. In some embodiments, a method comprises: obtaining sensor data from a wearable device worn on a wrist of a user; deriving features from the sensor data; estimating a form-based stride length using an estimation model that takes the features and user height as input; and calibrating the form-based stride length. In other embodiments, user cadence and speed are used to estimate speed-based stride length which, upon certain conditions, is blended with the form-based stride length to get a final estimated stride length of the user.
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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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