Layered Smart Fabric for Object and Gesture Recognition
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Solution Overview
Problem
Existing wearable technologies are limited in their ability to recognize objects and gestures without explicit user interaction, particularly in ubiquitous computing environments where subtle and eyes-free inputs are needed.
Innovation Solution
A smart fabric system integrated with multiple sensors (resistive, capacitive, inductive, and NFC) that generates sensing data, processed by a data processing circuitry and machine learning module to recognize objects and gestures, enabling object recognition and gesture recognition functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of sensors (resistive, capacitive, inductive, NFC) are integrated into the smart fabric, then object recognition accuracy and gesture recognition capability are improved, but device complexity increases
Solution Approach 1:
The smart fabric is divided into multiple functional layers, with each layer containing specific sensor types. The first layer includes resistive and capacitive sensors for gesture detection, while the second layer includes inductive and NFC sensors for object recognition. This segmentation allows each sensor type to be optimized independently while working together as an integrated system.
Solution Approach 2:
The patent integrates multiple sensor technologies into a single fabric structure, creating a composite sensing system. The fabric combines conductive materials, pressure-sensitive materials, and electromagnetic sensing elements into a unified wearable device that leverages the strengths of each sensor type for comprehensive object and gesture recognition.
2Adaptability or versatility
If machine learning models are trained to recognize unseen objects and gestures, then adaptability and versatility are improved, but loss of time for data processing and model training increases
Solution Approach 1:
Machine learning models are pre-trained on diverse datasets of objects and gestures before deployment. This preliminary training enables the system to quickly recognize new objects and gestures during actual use without requiring extensive real-time processing, thus reducing operational delay while maintaining high versatility.
Solution Approach 2:
The system continuously receives feedback from sensor data during operation and uses this information to refine object and gesture recognition. The machine learning models are updated with new data over time, improving adaptability while the feedback loop ensures that processing time is optimized through iterative learning rather than exhaustive analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rich contextual interactions and subtle, eyes-free inputs by accurately identifying objects and gestures, enhancing wearable scenarios beyond current capabilities of smart watches or head-mounted displays.
Implementation Method 1
a capacitive sensor configured to generate a capacitive signal in response to an object or body part being in proximity to or in contact with the capacitive sensor
Implementation Method 2
a resistive sensor configured to generate a resistance signal in response to pressure being applied to the resistive sensor by the object or body part
Implementation Method 3
an inductive sensor configured to generate an inductive signal in response to a metallic object being in proximity to the inductive sensor
Implementation Method 4
an NFC sensor configured to generate an NFC signal in response to an NFC-enabled object being in proximity to the NFC sensor
Data Source
AI summary
A piece of smart fabric for recognizing an object or a touch gesture includes a first layer and a second layer. The first layer has multiple resistive sensors and multiple capacitive sensors. Each resistive sensor corresponds to each capacitive sensor. Each capacitive sensor includes a first piece of conductive fabric and a second piece of conductive fabric, and each resistive sensor includes a piece of pressure sensing fabric sandwiched between the first piece of conductive fabric and the second piece of conductive fabric of the corresponding capacitive sensor. The second layer has multiple inductive sensors and multiple NFC sensors. Each of the multiple inductive sensors corresponds to each of the NFC sensors. Each inductive sensor and its corresponding NFC sensor share a same coil.