Google llc (20240201792). FULL HAND KINEMATICS RECONSTRUCTION USING ELECTRICAL IMPEDANCE TOMOGRAPHY WEARABLE simplified abstract

FULL HAND KINEMATICS RECONSTRUCTION USING ELECTRICAL IMPEDANCE TOMOGRAPHY WEARABLE

Organization Name

google llc

Inventor(s)

Stiven Guillaume Francois Morvan of New York NY (US)

Dongeek Shin of San Jose CA (US)

Andrea Colaco of Los Altos CA (US)

Sambuddha Basu of San Jose CA (US)

Sean Kyungmok Bae of San Francisco CA (US)

Junyi Zhu of Cambridge MA (US)

FULL HAND KINEMATICS RECONSTRUCTION USING ELECTRICAL IMPEDANCE TOMOGRAPHY WEARABLE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240201792 titled 'FULL HAND KINEMATICS RECONSTRUCTION USING ELECTRICAL IMPEDANCE TOMOGRAPHY WEARABLE

The patent application describes a technique for determining hand gestures formed by a user using an electrical impedance tomograph of the wrist.

• A user wears a flexible wristband with electrodes that induce an electric field through the wrist, allowing for the measurement of electrical impedance.
• By applying a current to a subset of electrodes and measuring the induced voltage, the electrical impedance throughout the wrist can be determined using electrical impedance tomography (EIT).
• A neural network can then map the electrical impedance tomograph to a specific hand gesture.

Potential Applications: - Gesture recognition technology for human-computer interaction - Rehabilitation and physical therapy applications - Virtual reality and gaming interfaces

Problems Solved: - Accurate and non-invasive hand gesture recognition - Real-time monitoring of wrist movements - Enhanced user experience in various applications

Benefits: - Improved accuracy and efficiency in gesture recognition - Enhanced user interaction with devices - Potential for new applications in healthcare and entertainment industries

Commercial Applications: Title: "Innovative Gesture Recognition Technology for Enhanced User Interaction" This technology can be utilized in smart devices, gaming consoles, virtual reality systems, and healthcare devices, enhancing user experience and expanding market opportunities.

Prior Art: Further research can be conducted in the fields of electrical impedance tomography, gesture recognition technology, and neural network mapping for hand gestures.

Frequently Updated Research: Stay updated on advancements in electrical impedance tomography, neural network algorithms for gesture recognition, and applications of wearable technology in human-computer interaction.

Questions about Gesture Recognition Technology: 1. How does this technology compare to traditional methods of gesture recognition? 2. What are the potential limitations of using electrical impedance tomography for hand gesture recognition?

Original Abstract Submitted

techniques include determining hand gestures formed by a user based on an electrical impedance tomograph of the wrist. for example, a user may be outfitted with a flexible wristband that fits snugly around the wrist and contains a plurality of electrodes, e.g., 32 electrodes. when a current is applied to a first subset of the electrodes, e.g., two of 32 electrodes, the electric field induced through at least one cross-section of the wrist will in turn induce a voltage across adjacent pairs of a second subset of the electrodes (e.g., the other 30 of 32 electrodes). from this current and induced voltage, one may use techniques of electrical impedance tomography (eit) to determine the electrical impedance throughout the at least one cross-section of the wrist, e.g., in an electrical impedance tomograph. one may use a neural network to map the electrical impedance tomograph to a hand gesture.