Automated CSI Labeling via AI and Ground Truth Sensors
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Solution Overview
Problem
The labor-intensive and costly process of manually labeling Channel State Information (CSI) data for training AI models in wireless localization systems, which is necessary for accurate location and gesture recognition, hinders efficient development and deployment of these systems.
Innovation Solution
An automated labeling system that includes an automated labeling module (ALM) and a training database, coupled with ground truth providing devices such as cameras, IMU sensors, and Bluetooth beacons, which automatically generate labeled training data by interpreting user gestures and environmental data, reducing the need for manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of CSI data is used, then labeling accuracy is ensured, but labeling time and cost increase significantly
Solution Approach 1:
The system uses AI models to automatically label CSI data without human intervention. The AI model processes raw CSI data and generates labels directly, enabling the system to serve itself rather than requiring manual labeling operations.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated AI-based system. The AI model uses machine learning algorithms to interpret CSI data and generate labels automatically, substituting human labor with computational processing.
2Reliability
If manual labeling of CSI data is used, then data quality is maintained, but development efficiency decreases
Solution Approach 1:
The system incorporates feedback mechanisms where the AI model continuously learns from labeled data and refines its labeling capabilities. This feedback loop ensures data quality while enabling rapid scaling of the labeling process without linear increases in manual effort.
Solution Approach 2:
The system performs preliminary actions by pre-processing and automatically labeling large portions of CSI data before final model training. This preliminary automated labeling reduces the burden on manual annotators and accelerates the overall development timeline.
3Productivity
If automated AI labeling is implemented, then labeling speed increases, but system complexity increases
Solution Approach 1:
The AI labeling system is designed as a universal platform that can handle multiple types of wireless signals and localization scenarios. The same core AI model architecture can be applied to different CSI data types, reducing the need for separate specialized labeling systems for each application.
4Measurement precision
If large amounts of labeled data are collected, then AI model accuracy improves, but data collection cost increases
Solution Approach 1:
The automated AI labeling system enables the system to generate its own training data by processing raw CSI signals and creating labeled datasets automatically. This self-service capability allows extensive data collection without proportionally increasing human resource costs.
Solution Approach 2:
The system changes the parameters of data collection by focusing on capturing raw CSI signals rather than pre-labeled data. By collecting unlabeled raw data and using AI to generate labels post-collection, the system reduces the immediate cost of data acquisition while maintaining the ability to create large-scale training datasets.
Data Source
AI summary
Aspects of the present disclosure provide an automated labeling system. For example, the automated labeling system can include an automated labeling module (ALM) configured to receive wireless signals and ground truth of learning object and label the wireless signals with the ground truth when receiving the ground truth to generate labeled training data. The automated labeling system can also include a training database coupled to the ALM. The training database can be configured to store the labeled training data.