Apple Inc.

Other

Siddharth Khullar

Nicholas Elia Apostoloff

Abhishek A Sharma

Russell Y Webb

Jerremy Holland

Tomas Jon Pfister

Tai-Peng Tian

Alexander Mark Chan

Fredrik J Sannholm

Luca Zappella

Xavier Suau Cuadros

Amruta Pai

Matt Travis Bianchi

Till M Quack

Lifeng Miao

In some examples, an individually-pruned neural network can estimate blood pressure from a seismocardiogram (SCG). In some examples, a baseline model can be constructed by training the model with SCG data and blood pressure measurement from a plurality of subjects. One or more filters (e.g., the filters in the top layer of the network) can be ranked by separability, which can be used to prune the model for each unseen user that uses the model thereafter, for example. In some examples, individuals can use individually-pruned models to calculate blood pressure using SCG data without corresponding blood pressure measurements.

Interpretable neural networks for cuffless blood pressure estimation

Sleep conditions such as moderate-to-severe sleep apnea can be assessed using a multi-night assessments. A respiration signal (e.g., acquired from a sensor strip) can be processed via a computing device. The respiration signal can be segmented and the segments can be classified to identify one or more apnea/hypopnea events. In some examples, some of the segments can be normalized such that each segment input for classification can be of the same size. The identified one or more apnea/hypopnea events can be used to estimate a nightly parameter indicative of a severity of (or presence of) sleep apnea. The nightly parameters from a multi-night period can be used to estimate a multi-night parameter indicative of the severity of (or presence of) sleep apnea. In some examples, quality checks can be performed to filter out some data (e.g., to exclude data from entire nights or exclude a portion of data from individual nights).

Methods for assessing sleep conditions

A system for selecting different aspects of data objects to be matched with similar aspects of other data objects. A user inputs a search data object and a value. A neural network computes features for the search object at multiple layers that correspond to different aspects of the object. A descriptor is generated for the search object from features output at a layer position of the neural network determined from the value. The descriptor is compared to corresponding descriptors for objects in a collection to select objects that include aspects similar to an aspect of the search object. The user can change the value to view different objects that include aspects similar to other aspects of the search object. Thus, the user can explore different aspects of an object to find objects that include aspects similar to the aspect of the object that the user is interested in.

Data retrieval system

Modifying operation of an intelligent agent in response to facial expressions and/or emotions.

Intelligent software agent

Growing community of inventors

Santa Clara, CA, United States of America

Siddharth Khullar