Neural HMI Input Prediction for Low-Latency User Control
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Solution Overview
Problem
Current human-machine interface systems face challenges in reducing latency, particularly in gaming and cloud gaming, due to delays in processing user inputs, which affect the responsiveness and overall user experience.
Innovation Solution
A human-machine interface system that utilizes a sensor to generate data associated with human movement, a measurement unit to measure user movement over time, a processing unit to process this data using a trained neural network to predict user actions, and a control unit to compare these predictions with actual user actions, allowing for the early use of predicted actions to reduce latency in information processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user input processing is performed in real-time waiting for confirmation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary processing of predicted user actions before they are fully confirmed. The neural network predicts user actions in advance, and the system begins processing these predictions while they are still being validated against actual measured actions. This allows the system to prepare responses proactively rather than waiting for complete confirmation, thereby reducing latency while maintaining accuracy through subsequent validation.
2Loss of time
If neural network prediction is used to predict user actions, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The system introduces a neural network as an intermediary component that bridges the gap between raw user input and system response. The neural network acts as a predictive mediator that processes sensor data and generates predicted user actions, which are then validated against actual measurements. This intermediary layer enables proactive processing and latency reduction while keeping the overall system architecture manageable through modular design.
Data Source
AI summary
A human-machine interface system comprises a sensor configured to generate data associated with a human movement, such as measured electrical signals or data from an accelerometer. A measurement unit of the human-machine interface measures user movement over time to generate a sequence of measured user movement data. A processor processes the data associated with a human movement from the sensor using a trained neural network to determine one or more predicted user actions. A comparison unit compares the one or more predicted user actions with one or more user actions obtained from the sequence of measured user movement data. A control unit uses the predicted user actions to control a process in an information processing apparatus in dependence upon the comparison performed by the comparison unit.