公开(公告)号：US20240058650A1

公开(公告)日：2024-02-22

申请号：US18451798

申请日：2023-08-17

Applicant: Apple Inc.

Inventor： Matthias R. Hohmann ,  Andrea Eppy ,  Erdrin Azemi

IPC: A63B24/00 ,  A63B71/06

CPC classification number: A63B24/0062 ,  A63B71/0686 ,  A63B2024/0065 ,  A63B2024/0068 ,  A63B2071/0625

Abstract: Methods, systems and/or computer-implemented instructions are configured to perform or support actions that include: determining a time contribution for each of a set of workout effort zones for a user, wherein each of the set of workout effort zones corresponds to a range of values for a biosignal; determining a timeseries of workout target effort zones for the user based on the target time contributions for the set of workout effort zones; receiving, during a workout time period, real-time biosignal data from a sensor in a wearable electronic device being worn by the user; generating, during the workout time period, an audio, visual, or haptic stimulus based on the real-time biosignal data and a target effort zone in the time series of workout target effort zones; and outputting, during the workout time period, the audio, visual, or haptic stimulus.

公开(公告)号：US20220383189A1

公开(公告)日：2022-12-01

申请号：US17554895

申请日：2021-12-17

Applicant: Apple Inc.

Inventor： Joseph Yitan Cheng ,  Amruta Pai ,  Erdrin Azemi ,  Matthias R. Hohmann

IPC: G06N20/00 ,  G06N5/02

Abstract: Methods and systems are provided for predicting cognitive load. A computing device receives sensor measurements from sensors. The sensor measurements correspond to characteristics of a user during the performance of a task. For each sensor, the computing device derives, from the sensor measurements of the sensor, a set of features predictive of the cognitive load of the user; generates, from those features, a self-attention vector that characterizes each feature of the set of features relative to another feature; and defines a feature vector from the features and the self-attention vector. The computing device generates an input feature vector from the feature vector of at least one sensor. The computing device then uses a machine-learning model to generate an indication of the cognitive load of the user during the performance of a task from the feature vector.