Grip Detection Algorithm for Touch-Sensitive Surface Inputs
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Solution Overview
Problem
Touch-sensitive devices struggle to differentiate between intentional and unintentional touch inputs, such as those caused by a user gripping the device, leading to potential misinterpretation of user interactions and unintended device responses.
Innovation Solution
Implementing a grip detection algorithm that analyzes touch input characteristics, including location, size, shape, inversion, density, and duration, to identify and filter out unintentional grip inputs, thereby processing only valid touch inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the touch-sensitive surface detects all touch inputs, then no intentional touches are missed, but unintentional grip inputs are misinterpreted as valid touches
Solution Approach 1:
The system changes multiple parameters of the touch input simultaneously (location proximity to edge, elongated shape aspect ratio, contact duration, signal density) to distinguish grip inputs from intentional touches. By evaluating combinations of these parameters rather than relying on a single threshold, the system accurately identifies and filters unintentional grip inputs while maintaining sensitivity to valid touches.
2Object-affected harmful factors
If the system filters out edge touches to prevent grip misinterpretation, then unintentional inputs are reduced, but intentional touches near the edge are also missed
Solution Approach 1:
Instead of filtering based solely on location, the system evaluates multiple parameters including the aspect ratio of the touch patch (intentional touches typically have different dimensional proportions than grip contacts), signal density distribution, and contact duration. This multi-parameter approach allows intentional touches near the edge to be distinguished from grip inputs based on their unique characteristic patterns.
Solution Approach 2:
The system dynamically adjusts its interpretation of touch inputs based on the combination of parameters observed. Rather than applying a static filter, it evaluates whether the pattern of parameters (location, shape, duration, density) collectively indicates an intentional touch or a grip input, allowing flexible and accurate classification in varying contexts.
3Device complexity
If simple touch detection is used, then the system remains simple and fast, but it cannot differentiate between intentional and unintentional touches
Solution Approach 1:
The touch processing system is segmented into distinct functional stages: initial touch detection, parameter extraction (location, shape, duration, density), grip criteria evaluation, and classification decision. This segmentation allows the system to maintain simplicity in each individual stage while achieving sophisticated classification through the coordinated sequence of stages, avoiding the need for a single complex algorithm.
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
Effectively prevents unintended device responses by accurately distinguishing between intentional and unintentional touch inputs, enhancing user experience by ensuring accurate interpretation of user interactions.
Implementation Method 1
In some capacitive-type touch sensing systems, fringing electrical fields used to detect touch can extend beyond the surface of the display, and objects approaching near the surface may be detected near the surface without actually touching the surface.
Data Source
AI summary
Grip detection can be beneficial for an electronic device to ignore unintended contacts on a touch sensitive surface. Examples of the disclosure provide various ways for identifying an input patch as a grip. In some examples, identifying an input patch as a grip comprises determining whether the input patch satisfies one or more grip identification criteria. In some examples, identified grips are saved in a grip database. In some examples, the identified grips are filtered out of touch images. In some examples, when baseline touch data for a touch-sensitive is updated, the touch processor can forgo updating the baseline for portions of the touch sensitive surface associated with the identified grips.