Hand State Prediction System for Digital Pen Interaction
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Solution Overview
Problem
Existing digital pen technologies face limitations in efficiently switching modes and performing complex interactions on small screens due to cumbersome menu systems and physical buttons, which are aesthetically unpleasing and increase hardware complexity.
Innovation Solution
A hand state prediction system that combines inertial motion data and surface contact data using a machine learning-based model, such as a neural network, to automatically detect and classify hand gestures and postures, enabling improved user interaction by transforming hand actions into command actions for digital devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional menu systems and physical buttons are used for mode switching and command input, then device functionality is maintained, but device complexity and hardware requirements increase
Solution Approach 1:
The patent replaces physical buttons and mechanical menu systems with a machine learning-based hand state prediction system that processes inertial motion data and contact surface signals. The neural network model converts hand gestures and postures into command actions, eliminating the need for physical hardware components while maintaining full functionality.
Solution Approach 2:
The patent introduces an intermediary layer consisting of sensors and machine learning models that mediate between the user's hand actions and the digital device commands. The inertial motion sensors and contact surface sensors act as intermediaries to capture hand state, which is then processed by the neural network to generate appropriate commands.
2Adaptability or versatility
If physical buttons are added to digital pens for mode switching, then interaction capability is improved, but aesthetic appearance and hardware simplicity deteriorate
Solution Approach 1:
The patent eliminates physical buttons from the digital pen by substituting them with sensor-based detection. The system uses inertial motion sensors and contact surface sensors to detect hand actions, which are then processed by machine learning models to determine user intent, maintaining versatility without physical components.
Solution Approach 2:
The patent makes the digital pen universally interactive by enabling it to recognize multiple types of hand actions (gestures, postures, contact patterns) through a single integrated sensor system and machine learning model, replacing the need for multiple physical buttons for different functions.
3Measurement precision
If multiple sensors are integrated into digital pen, then hand state detection accuracy is improved, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent segments the sensing function into two independent components: inertial motion sensors for capturing hand movement and contact surface sensors for detecting hand placement and pressure. This segmentation allows each sensor type to be optimized independently while providing complementary information to the machine learning model.
Solution Approach 2:
The patent merges the data from inertial motion sensors and contact surface sensors into a unified hand state representation processed by the machine learning model. The fusion of these sensor modalities provides comprehensive hand state information that improves detection accuracy while managing manufacturing complexity through integrated processing.
Data Source
AI summary
In various examples, the present disclosure describes methods and systems for generating hand state predictions. A hand state prediction system includes a machine learning-based model, such as a neural network model, that is trained to convert inertial motion measurements and surface contact data into predictions of a corresponding hand gesture or gripping posture. For each window of data sampled, motion data and contact data are obtained, processed and fused to generate a fused prediction. The hand state prediction system can operate in continuous mode to automatically detect a start and an end of a hand action, or a user can designate a start and an end of a hand action. A hand state prediction is generated by a multimodal classifier by processing the fused prediction. Instructions represented by a hand action can be acted upon through a command action performed by a computing device or a computer application.