Personalized Motion Prediction for Home Appliance Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for predicting user behavior and controlling home appliances rely on pre-trained AI models that do not account for individual user behavior, leading to ineffective operation control due to the lack of personalized training data.
Innovation Solution
A system that collects and generates training data after distribution to users, trains a motion prediction model using this data, and controls home appliances based on the obtained prediction information, allowing for personalized operation control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-trained AI models are used to predict user behavior and control home appliances, then the system can operate immediately without data collection, but the control effectiveness is poor because the models do not account for individual user behavior patterns
Solution Approach 1:
The system performs preliminary data collection and model training during a designated training period before full operation begins. Sensor data is gathered and stored in advance to build personalized motion prediction models, ensuring the system is prepared to provide effective personalized control from the start of normal operation.
Solution Approach 2:
The system automatically collects sensor data, generates training data, trains motion prediction models, and updates control strategies without requiring manual intervention. The system serves itself by continuously learning from user behavior patterns and improving its personalized control capabilities over time.
2Measurement precision
If personalized motion prediction models are trained using collected sensor data, then individual user behavior patterns are captured improving control precision, but the system complexity increases due to data collection and model training requirements
Solution Approach 1:
The server performs multiple functions including data collection, training data generation, motion prediction model training, and control strategy update. This multi-functional approach consolidates complex operations into a single centralized system rather than distributing complexity across multiple components.
Solution Approach 2:
The server acts as an intermediary between the sensors and the home appliances. It collects raw sensor data, processes it into training data, trains personalized models, and generates control commands, thereby simplifying the overall system architecture by centralizing the intelligence layer.
3Manufacturing precision
If training data is collected for a predetermined period after system distribution, then personalized models can be trained accurately, but the time required before effective operation begins is extended
Solution Approach 1:
The system is designed to collect sensor data and train motion prediction models during a predetermined training period immediately after installation. This preliminary action ensures that personalized models are trained with actual user behavior data before the system transitions to normal operational mode, achieving both high model quality and timely deployment.
Data Source
AI summary
Proposed is a method for training a motion prediction model. The method may include obtaining sensor data including a plurality of frame data, and obtaining home appliance data including operation time information for a first operation of a home appliance. The method may also include selecting a frame data group on the basis of the operation time information for a first operation of the home appliance included in the home appliance data and time information for each of the plurality of frame data. The method may further include generating training input data for the first operation of the home appliance on the basis of the selected frame data group, and training the motion prediction system using at least the training input data.