Apple inc. (20240103633). HOLD GESTURE RECOGNITION USING MACHINE LEARNING simplified abstract

HOLD GESTURE RECOGNITION USING MACHINE LEARNING

Organization Name

apple inc.

Inventor(s)

Bongsoo Suh of San Jose CA (US)

Behrooz Shahsavari of Hayward CA (US)

Charles Maalouf of Seattle WA (US)

Hojjat Seyed Mousavi of San Jose CA (US)

Laurence Lindsey of Portland OR (US)

Shivam Kumar Gupta of Fremont CA (US)

HOLD GESTURE RECOGNITION USING MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240103633 titled 'HOLD GESTURE RECOGNITION USING MACHINE LEARNING

Simplified Explanation

Embodiments are disclosed for hold gesture recognition using machine learning (ML). In an embodiment, a method comprises: receiving sensor signals indicative of a hand gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn by the user; generating a first embedding of first features extracted from the sensor signals; predicting a first part of a hold gesture based on a first ML gesture classifier and the first embedding; generating a second embedding of second features extracted from the sensor signals; predicting a second part of the hold gesture based on a second ML gesture classifier and the second embedding; predicting a hold gesture based at least in part on outputs of the first and second ML gesture classifiers and a prediction policy; and performing an action on the wearable device or other device based on the predicted hold gesture.

• Hand gesture recognition using machine learning
• Sensor signals from wearable devices used for gesture recognition
• Prediction of hold gestures based on ML classifiers and sensor data
• Actions performed based on predicted hold gestures

Potential Applications

This technology can be applied in:

• Virtual reality and augmented reality systems for intuitive user interactions
• Gaming controllers for more immersive gameplay experiences
• Smart home devices for hands-free control and automation

Problems Solved

This technology helps in:

• Improving user experience by accurately recognizing hand gestures
• Enabling seamless interaction with wearable devices
• Enhancing accessibility for users with mobility impairments

Benefits

The benefits of this technology include:

• Increased efficiency in controlling devices through gestures
• Enhanced user engagement and satisfaction
• Potential for new and innovative applications in various industries

Potential Commercial Applications

This technology has potential commercial applications in:

• Consumer electronics industry for developing advanced wearable devices
• Entertainment industry for creating interactive gaming experiences
• Healthcare industry for assistive technologies for individuals with disabilities

Possible Prior Art

One possible prior art in this field is the use of machine learning algorithms for gesture recognition in various applications such as robotics, computer vision, and human-computer interaction.

How does this technology handle variations in hand gestures among different users?

The technology may utilize a training dataset with diverse hand gestures to improve the accuracy of recognition across different users.

What is the latency involved in predicting and performing actions based on hold gestures?

The latency can vary based on the complexity of the gestures, the efficiency of the ML algorithms, and the processing power of the devices involved.

Original Abstract Submitted

embodiments are disclosed for hold gesture recognition using machine learning (ml). in an embodiment, a method comprises: receiving sensor signals indicative of a hand gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn by the user; generating a first embedding of first features extracted from the sensor signals; predicting a first part of a hold gesture based on a first ml gesture classifier and the first embedding; generating a second embedding of second features extracted from the sensor signals; predicting a second part of the hold gesture based on a second ml gesture classifier and the second embedding; predicting a hold gesture based at least in part on outputs of the first and second ml gesture classifiers and a prediction policy; and performing an action on the wearable device or other device based on the predicted hold gesture.