Hybrid Neuro-Symbolic Framework: A Technical Breakthrough in Multi-User Complex Activity Recognition

Core Overview

This project proposes a hybrid neuro-symbolic framework to address the problem of complex composite activity recognition in multi-user wearable sensing environments. It integrates four core technologies: CNN-BiLSTM temporal modeling, Graph Neural Networks (GNNs), attention mechanisms, and probabilistic reasoning. By combining the perceptual capabilities of deep learning with the logical expression of symbolic reasoning, it effectively overcomes the limitations of traditional HAR methods. The project provides a complete dataset, evaluation benchmarks, and reproducibility support, laying the foundation for research in this field.

Original Author/Source: dursunoglu (GitHub) Original Link: https://github.com/dursunoglu/Hybrid-Classification-for-Complex-and-Composite-Activity-Recognition-in-Multi-User-Environments

Research Background and Challenges

Human Activity Recognition (HAR) is a core direction in pervasive computing and health monitoring, but traditional methods have limitations:

• Focus on single-user simple activities, making it difficult to handle real-world scenarios involving simultaneous multi-user activities, composite activities, and temporal dependencies.

Key Challenges:

1. Multi-user Interaction: Sensor signals interfere with each other, and user activities influence one another;
2. Composite Activity Modeling: High-level activities are composed of low-level atomic activities in specific temporal sequences;
3. Temporal Dependencies: Causal and long-term dependencies exist between activities;
4. Sensor Heterogeneity: Effective fusion of multi-modal data is required.

Core Technical Methods

Framework Overview

The hybrid neuro-symbolic framework combines deep learning perception with symbolic reasoning: neural networks extract features and temporal patterns, while the symbolic reasoning layer uses domain knowledge to model logical relationships.

Key Components

1. CNN-BiLSTM Temporal Modeling: CNN extracts local spatial features, and BiLSTM captures bidirectional temporal dependencies, adapting to the temporal signals of wearable sensors;
2. GNN Relationship Modeling: Treats users as nodes and spatial relationships as edges, aggregates neighbor information through message passing to perceive the context of group activities;
3. Attention Mechanism for Sensor Fusion: Dynamically assigns weights to sensor channels, adaptively focusing on relevant signals;
4. Probabilistic Reasoning Layer: Uses prior knowledge (e.g., activity sequence constraints) to filter unreasonable results and improve robustness.

Dataset and Reproducibility

Dataset and Evaluation Benchmark

• Multi-user Wearable Dataset: Activity records in real-world scenarios;
• Composite Activity Annotations: Includes boundaries and compositions of atomic and composite activities;
• Baseline Methods: Provides reproducibility of comparison methods to support fair comparisons;
• Deployment Performance: Reports inference latency and resource usage across different hardware.

Reproducibility Assurance

Provides detailed experimental configurations, random seed settings, and dependency version locking to ensure other researchers can reproduce results and extend the research.

Application Scenarios and Technical Insights

Application Scenarios

• Smart Elderly Care: Monitor daily activities of the elderly and identify abnormalities;
• Health Monitoring: Track rehabilitation training and evaluate exercise effects;
• Smart Office: Understand group activities in collaborative spaces;
• Sports Analysis: Analyze team tactics and individual performance.

Technical Insights

Neuro-symbolic AI combines the advantages of data-driven and symbolic reasoning: pure data-driven methods struggle to use domain knowledge, while pure symbolic methods have difficulty handling noisy data; the hybrid architecture leverages the strengths of both, providing a reference for scenarios combining perception and reasoning.

Conclusion

The hybrid neuro-symbolic framework provides a systematic solution for multi-user complex activity recognition:

• Through the organic combination of CNN-BiLSTM, GNN, attention mechanisms, and probabilistic reasoning, it effectively captures temporal patterns, models user relationships, fuses multi-source information, and utilizes domain knowledge;
• The dataset and benchmarks provided by the project lay a solid foundation for subsequent research and promote the development of this field.