Digital Glove Sensor Mapping for Non-Digital Object Control
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Solution Overview
Problem
Traditional user input devices are often expensive, power-dependent, and one-size-fits-all, leading to discomfort and potential malfunctions, limiting customization and reliability in human-computer interaction.
Innovation Solution
A digital glove equipped with sensors such as flex, pressure, and inertial measurement units is used to enable control of host computers using non-digital objects like cups or writing implements, allowing for customizable and power-efficient interaction through machine learning models that map sensor data to virtual user input device data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional digital user input devices are used, then computing systems can be controlled, but they are expensive, require power, and are prone to malfunctioning
Solution Approach 1:
The patent replaces traditional mechanical/digital input devices with a digital glove that uses flex sensors to detect finger movements. The glove converts physical finger gestures into digital signals through capacitive sensing, eliminating the need for batteries and mechanical components while maintaining input functionality.
Solution Approach 2:
The digital glove utilizes the user's own hand movements as the input mechanism, requiring no external power source. The flex sensors in the glove fingers detect the natural flexing and extending of user fingers, converting these biological movements directly into control signals for the computing system.
2Reliability
If traditional digital user input devices are used, then computing systems can be controlled, but they are expensive
Solution Approach 1:
The patent employs inexpensive flex sensors made from conductive fabric or thread that can be easily manufactured and replaced. These sensors use simple capacitive sensing principles that require minimal materials, dramatically reducing production costs compared to traditional digital input devices while maintaining functional reliability.
3Ease of operation
If traditional digital user input devices are used, then computing systems can be controlled, but they are one size fits all and not comfortable for users with large or small hands
Solution Approach 1:
The digital glove is designed to be wearable and conformable, adapting to different hand sizes and shapes. The flexible sensor array in each glove finger detects individual finger movements regardless of the user's hand dimensions, providing a customized fitting experience for each wearer without requiring multiple device sizes.
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
This solution enhances human-computer interaction by enabling the use of inexpensive, customizable non-digital objects to control computers, reducing the risk of device failure and providing tailored user experiences through software updates, thereby improving overall computing system operation.
Implementation Method 1
Some flex sensors utilize capacitive/piezoresistive sensing
Implementation Method 2
Some flex sensors utilize capacitive/piezoresistive sensing
Implementation Method 3
Tactile pressure sensors (which might be referred to herein as 'pressure sensors'), which might also utilize capacitive/piezoresistive sensing
Implementation Method 4
Tactile pressure sensors (which might be referred to herein as 'pressure sensors'), which might also utilize capacitive/piezoresistive sensing
Data Source
AI summary
The disclosed technologies address technical problems, including improving human-computer interaction, by augmenting the functionality provided by non-digital objects using a digital glove. To provide this functionality, a machine learning model is trained using sensor data generated by sensors in a digital glove and data generated by a user input device while the digital glove is utilized to manipulate an object like a user input device. Once trained, the machine learning model can take sensor data generated by a digital glove while manipulating a non-digital object and generate virtual user input device data that can be utilized to control a host computer. A digital glove can also be utilized to perform selection operations using non-digital objects when pressure data generated by one or more of the pressure sensors in the digital glove indicates that pressure was exerted at a finger of the digital glove in excess of a threshold value.