公开(公告)号：US20240099627A1

公开(公告)日：2024-03-28

申请号：US18369835

申请日：2023-09-18

Applicant: Apple Inc.

Inventor： Matthias R. HOHMANN ,  Ellen L. ZIPPI ,  Kaan E. DOGRUSOZ

IPC: A61B5/22 ,  A61B5/00 ,  A61B5/389

CPC classification number: A61B5/224 ,  A61B5/389 ,  A61B5/681 ,  A61B5/7225

Abstract: Aspects of the subject technology provide improved techniques for estimating muscular force. The improved techniques may include single-channel or multiple-channel surface electromyography (EMG), such as via a measurement device worn on a wrist. A muscular force estimate may be based on one or more measurements of variation between adjacent voltage measurements and estimates of spectral properties of the voltage measurements. The resulting muscular force estimate may for a basis for improved hand gesture recognition and/or heath metrics of the user.

公开(公告)号：US20230342583A1

公开(公告)日：2023-10-26

申请号：US18080736

申请日：2022-12-13

Applicant: Apple Inc.

Inventor： Joseph Y. CHENG ,  Bradley W. GRIFFIN ,  Hanlin GOH ,  Helen Y. WENG ,  Matthias R. HOHMANN

IPC: G06N3/02

CPC classification number: G06N3/02 ,  G06T2207/20084

Abstract: A method is provided that includes receiving biosignal data measured from a user, encoding the biosignal data into a vector, and generating, using a generative model, an image based on the vector. The generated image is provided for display.

公开(公告)号：US20240103632A1

公开(公告)日：2024-03-28

申请号：US18369833

申请日：2023-09-18

Applicant: Apple Inc.

Inventor： Matthias R. HOHMANN ,  Anna SEDLACKOVA ,  Bradley W. GRIFFIN ,  Christopher M. SANDINO ,  Darius A. SATONGAR ,  Erdrin AZEMI ,  Kaan E. DOGRUSOZ ,  Paul G. PUSKARICH ,  Gergo PALKOVICS

IPC: G06F3/01 ,  G06F3/0346

CPC classification number: G06F3/017 ,  G06F3/0346 ,  G06F3/011 ,  G06F3/016

Abstract: Aspects of the subject technology relate to providing gesture-based control of electronic devices. Providing gesture-based control may include determining, with a machine learning system that includes multiple machine learning models, a prediction of one or more gestures and their corresponding probabilities of being performed. A likelihood of the user's intent to actually perform that gesture may then be generated, based on the prediction and a gesture detection factor. The likelihood may be dynamically updated over time, and a visual, auditory, and/or haptic indicator of the likelihood may be provided as user feedback. The visual, auditory, and/or haptic indicator may be helpful to guide the user to the correct gesture if the gesture is intended, or to stop performing an action similar to the gesture if the gesture is not intended.