公开(公告)号：US20250104861A1

公开(公告)日：2025-03-27

申请号：US18665282

申请日：2024-05-15

Applicant: Apple Inc.

Inventor： Hamidreza ABBASPOURAZAD ,  Udhyakumar NALLASAMY ,  Oussama ELACHQAR ,  Saba EMRANI ,  Andrew MILLER ,  Ian R. SHAPIRO

IPC: G16H50/20 ,  G16H10/60

Abstract: The subject technology provides for large-scale training of foundation models for physiological signals from wearable electronic devices. An apparatus receives receive input data having a plurality of physiological signal information segments associated with a user. The apparatus applies one or more augmentation functions to the plurality of physiological signal information segments to generate an augmented version of the plurality of physiological signal information segments. The apparatus trains a neural network to produce a trained machine learning model by generating, via an encoder, an embedding of the augmented version having a first number of dimensions in an embedding space. The apparatus maps, via a multilayer perceptron projection, the embedding into a representation having a second number of dimensions. The apparatus determines mutual information between a pair of representations of the augmented version. The apparatus can deploy the trained machine learning model to predict a physiological state of the user.

公开(公告)号：US20240079112A1

公开(公告)日：2024-03-07

申请号：US18076367

申请日：2022-12-06

Applicant: Apple Inc.

Inventor： Andrew MILLER ,  Gregory DARNELL ,  Guillermo R. SAPIRO ,  Seyedeh Sana TONEKABONI ,  You REN ,  Achille NAZARET

IPC: G16H20/30

CPC classification number: G16H20/30

Abstract: The subject technology provides a framework for generating physiological predictions for a user of an electronic device. The physiological predictions may include user-specific predictions of a heartrate, a heartrate range, a number of steps, a number of calories, or other physiological conditions or aspects that may occur if the user engages in a future activity, such as a future workout. The physiological predictions may be generated by a machine learning model that incorporates a physiological state equation, and that generates, and utilizes, a user-specific embedding, along with user-agnostic parameters of the future activity, to make the predictions.