Apple Inc.

Joseph Yitan Cheng

Erdrin Azemi

Kaan Emre Dogrusoz

Hanlin Goh

Ali Moin

Christoph Horst Krah

Mohammad Yeke Yazdandoost

Cuneyt Oncel Tuzel

The present application relates to apparatus, systems, and methods to perform subject-aware self-supervised learning of a machine-learning model for classification of data, such as classification of biosignals.

Subject-aware contrastive learning for biosignals

Aspects of the subject technology relate to gesture-control inputs to an electronic device for controlling one or more other devices. The electronic device can efficiently provide gesture control for multiple other devices by mapping a finite set of user gestures to a specific set of gesture-control elements for each of the multiple other devices. In this way a single gesture can be detected for potentially controlling various different functions of various different devices. Prior to gesture control, the electronic device may receive a selection of a particular one of the multiple other devices for control, and obtain the specific set of gesture-control elements for gesture control of that selected device.

Multi-device gesture control

A head-mounted device having a plurality of electrodes configured to detect optical events such as the movement of one or more eyes or coarse eye gestures is disclosed. In some examples, the one or more electrodes can be coupled to dielectric elastomer materials whose shape can be changed to vary contact between a user of the head-mounted device and the one or more electrodes to ensure sufficient contact and electrode signal quality. In some examples, the one or more electrodes can be coupled to pressure sensors and control circuitry to monitor and adjust the applied pressure. In some examples, the optical events can be used as triggers for operating the device, including transitioning between operational power modes. In some examples, the triggers can invoke higher resolution sensing capabilities of the head-mounted device. In some examples, the electrodes can be used as an on-head detector to wake-up and/or unlock the device.

Eye detection methods and devices

Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.

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Santa Clara, CA, United States of America

Joseph Yitan Cheng