Gesture-Controlled Measurement Device Using Machine Learning
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Solution Overview
Problem
Modern measurement application devices, such as oscilloscopes, present a complex user interface with numerous menu options, making it difficult for users to access and configure various functions efficiently.
Innovation Solution
The integration of a signal processing module, a user interface capable of acquiring gesture-based input, and a processor executing a machine learning algorithm that analyzes the gesture-based input to output control or configuration information for the signal processing module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex menu trees are provided via user interface to access all functions, then device functionality and versatility are improved, but ease of operation deteriorates
Solution Approach 1:
The machine learning algorithm enables the device to automatically interpret and respond to user gestures without requiring manual navigation through menu structures. The system learns from gesture patterns and autonomously configures measurement parameters, allowing the device to serve itself in interpreting user intent and executing appropriate functions.
Solution Approach 2:
The patent replaces the mechanical interaction model (manual menu navigation through layered interfaces) with an intelligent system that uses machine learning to interpret gestures and automatically configure device settings. This substitution eliminates the need for users to physically navigate complex menu trees while preserving full device functionality.
2Adaptability or versatility
If multiple processing and configuration options are provided, then device versatility is improved, but device complexity increases
Solution Approach 1:
The machine learning algorithm acts as an intermediary layer between the user's simple gesture input and the complex device configuration options. It translates intuitive gestures into appropriate processing and measurement functions, managing the complexity of multiple configuration options without exposing it to the user.
Solution Approach 2:
The system dynamically changes operational parameters based on learned gesture patterns. Instead of presenting users with static complex menus, the machine learning model adapts parameter configurations in real-time based on gesture recognition, effectively managing device complexity through dynamic parameter adjustment rather than static interface complexity.
3Adaptability or versatility
If comprehensive menu structures are implemented to cover all functions, then adaptability is improved, but ease of operation deteriorates
Solution Approach 1:
The machine learning algorithm performs preliminary action by pre-learning the relationship between gesture patterns and device functions. It anticipates user intent based on gesture recognition and proactively configures appropriate measurement parameters, eliminating the need for users to search through comprehensive menu structures to find functions.
Data Source
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AI summary
The present disclosure provides a measurement application device comprising a signal processing module configured to at least one of generate measurement signals, and acquire measurement signals, a user interface configured to acquire gesture-based user input, and a processor coupled to the user interface, and executing a machine learning algorithm, wherein the machine learning algorithm is configured to analyze received gesture-based user input, and to output respective control information for controlling the signal processing module or configuration information for configuration of the signal processing module. Further, a respective computer implemented method is provided.