SEGALL: A Unified Active Learning Framework for Wireless Sensing Data Segmentation
Unifying Wireless Sensing Segmentation with Active Learning
SEGALL introduces a novel framework for real-time, accurate segmentation of diverse wireless signals (IMU, Wi-Fi, mmWave) under interference. It leverages a lightweight Transformer-LSTM architecture and active learning to drastically reduce manual labeling efforts and enhance scalability for downstream AI tasks.
SEGALL's Impact on Enterprise AI
SEGALL addresses critical challenges in wireless sensing data processing, offering a robust and scalable solution for real-world IoT applications, smart homes, and human-computer interaction.
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mmWave Segmentation Accuracy
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Avg Labeling Effort Reduction
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Training Acceleration
Deep Analysis & Enterprise Applications
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Robust Segmentation in Noisy Environments
Traditional wireless sensing segmentation struggles with fine-grained activities and interference, where target and background signals can overlap. SEGALL employs a Transformer-LSTM architecture with self-attention to focus on target features and suppress interference. It formulates segmentation as a binary classification problem with Focal Loss, treating interference as a '0' label to alleviate class imbalance and enhance robustness against diverse real-world daily activities.
The system was evaluated against baseline methods, showing superior performance in distinguishing target activities from complex interference patterns across IMU, Wi-Fi, and mmWave signals.
Reducing Manual Labeling Efforts by 40.15%
Manually labeling segmentation boundaries in long time-series data is time-consuming and resource-intensive. SEGALL integrates an active learning framework with a novel Dispersion Metric query strategy. This metric quantifies the 'disorder' or randomness in model predictions, effectively identifying the most informative and heavily interfered samples for annotation.
This targeted approach not only significantly reduces the manual labeling effort by an average of 40.15% across all modalities but also improves the model's ability to learn from challenging data points, making the segmentation process more efficient and adaptable.
Integrated Training for Downstream Tasks
The ultimate goal of segmentation is to serve various downstream tasks like gesture recognition. SEGALL enhances scalability through a modular design that reuses the encoder trained during the segmentation phase for downstream tasks. This approach minimizes the need for extensive redesign and retraining, significantly accelerating the training process by 75%.
The pre-trained encoder extracts robust base features, which are then fine-tuned with lightweight, task-specific modules. This ensures adaptability to evolving user requirements and diverse applications without significant additional resources, making SEGALL highly versatile for real-world IoT deployments.
Unified Framework for Diverse Wireless Signals
SEGALL is designed as a unified framework capable of processing IMU, Wi-Fi, and mmWave signals, overcoming the limitations of signal-specific segmentation methods. Its lightweight Transformer-LSTM architecture is versatile, handling varying data dimensions and lengths while maintaining real-time processing capabilities.
Experimental results demonstrate impressive average segmentation accuracies: 95.10% for IMU, 93.23% for Wi-Fi, and 91.12% for mmWave. This cross-modality performance highlights SEGALL's robustness and adaptability, making it a powerful solution for heterogeneous sensing environments.
91.12%
mmWave Segmentation Accuracy Achieved by SEGALL, significantly outperforming previous solutions (70.50%).
Enterprise Process Flow
Long Time Series Data Input
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Transformer Encoder (Feature Extraction)
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LSTM Layer (Sequence Refinement)
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Active Learning Driven Training
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Segmentation Model Output
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Segmented Data for Downstream Tasks
| Method | Key Characteristics | Performance (mmWave example) |
|---|---|---|
| Threshold-based | Relies on fixed amplitude/phase thresholds; struggles with fine-grained and interference. | 11.21% accuracy |
| CPD-based | Identifies statistical property changes; sensitive to window size and parameter tuning. | 53.78% accuracy |
| DeepSeg (CNN-based) | Learns boundaries end-to-end; assumes clear temporal separation. | 70.50% accuracy |
| SEGALL (Transformer-LSTM + AL) | Unified, lightweight, robust to interference, active learning for efficiency, scalable for downstream tasks. | 91.12% accuracy |
40.15%
Average reduction in manual labeling effort with SEGALL's active learning.
SEGALL in Smart Home Gesture Control
Imagine a smart home where users control devices with natural gestures. Existing systems often require users to pause between gestures or struggle when gestures are mixed with daily activities (e.g., typing, drinking). SEGALL overcomes this by accurately segmenting target gestures even amidst continuous interference.
By unifying processing for Wi-Fi, IMU, and mmWave sensors, SEGALL enables robust gesture recognition regardless of the sensing modality. Its active learning significantly reduces the effort to train the system for new gestures, and its lightweight design allows deployment on edge devices like a Raspberry Pi, making smart home interaction seamless and efficient.
Calculate Your Potential AI ROI
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Annual Savings
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Your AI Implementation Roadmap
A typical timeline for integrating advanced AI capabilities into your existing enterprise infrastructure.
Phase 01: Discovery & Strategy
Initial assessment of existing systems, data infrastructure, and business objectives. Define key performance indicators and outline a phased integration strategy tailored to your enterprise.
Phase 02: Data Preparation & Model Training
Gather, preprocess, and normalize diverse wireless sensing data. Train the SEGALL model with active learning, customizing the query strategy for optimal labeling efficiency and model robustness.
Phase 03: Integration & Testing
Integrate SEGALL's segmentation and downstream task modules into your IoT platform. Conduct rigorous testing across various environments and signal modalities to ensure real-time performance and accuracy.
Phase 04: Deployment & Optimization
Deploy the fine-tuned AI model to edge devices (e.g., Raspberry Pi) or cloud infrastructure. Continuously monitor performance, gather feedback, and iterate for ongoing optimization and expanded capabilities.
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