Chengzhang Yu

Chengzhang Yu

he/him

Chengzhang Yu is a Ph.D. candidate at South China University of Technology, specializing in Large Language Models (LLMs), Multimodal Medical Artificial Intelligence, and Agent & Multi-Agent Systems (MAS). His research focuses on advancing AI-driven clinical decision-making and intelligent autonomous systems through multimodal learning and collaborative agents.

A Self-Supervised Pressure Map Human Keypoint Detection Approach: Optimizing Generalization and Computational Efficiency Across Datasets

Published in IEEE Conference, 2024

Abstract

This paper introduces a novel self-supervised learning approach for human keypoint detection using pressure map data. The proposed method addresses the challenges of limited annotated pressure map datasets and poor cross-dataset generalization in existing supervised approaches. By leveraging self-supervised learning techniques, our approach achieves superior generalization performance while maintaining computational efficiency across diverse datasets.

Key Contributions

  • Self-Supervised Learning Framework: Develops a novel self-supervised approach that eliminates the need for manual keypoint annotations in pressure map data, significantly reducing data preparation costs.

  • Cross-Dataset Generalization: Demonstrates superior generalization capabilities across different pressure map datasets, addressing a critical limitation of existing supervised methods.

  • Computational Efficiency: Optimizes the model architecture to achieve real-time performance while maintaining high accuracy, making it suitable for practical applications.

  • Comprehensive Evaluation: Extensive experiments across multiple datasets validate the effectiveness and robustness of the proposed approach.

Technical Innovation

The proposed approach combines:

  1. Self-Supervised Pretraining using contrastive learning on unlabeled pressure map data
  2. Adaptive Feature Extraction tailored for pressure map characteristics
  3. Efficient Network Architecture optimized for computational constraints
  4. Domain Adaptation Techniques for improved cross-dataset performance

Methodology

Self-Supervised Learning Strategy

  • Utilizes temporal consistency and spatial coherence in pressure map sequences
  • Employs contrastive learning to learn robust feature representations
  • Incorporates data augmentation techniques specific to pressure map modality

Network Architecture

  • Lightweight backbone network for efficient feature extraction
  • Multi-scale feature fusion for capturing both local and global patterns
  • Attention mechanisms for focusing on relevant pressure regions

Optimization Techniques

  • Progressive training strategy for stable convergence
  • Knowledge distillation for model compression
  • Adaptive learning rate scheduling for optimal performance

Experimental Results

The proposed method achieves significant improvements over existing approaches:

  • Generalization Performance: 15-20% improvement in cross-dataset evaluation compared to supervised baselines
  • Computational Efficiency: 3x faster inference time while maintaining comparable accuracy
  • Annotation Efficiency: Eliminates the need for manual keypoint annotations during training
  • Robustness: Consistent performance across different pressure sensor configurations

Dataset Evaluation

  • Comprehensive evaluation on multiple pressure map datasets
  • Cross-dataset validation demonstrating superior generalization
  • Ablation studies validating each component’s contribution

Impact and Applications

This work significantly advances the field of pressure-based human pose estimation with applications in:

  • Healthcare Monitoring: Non-intrusive patient monitoring in hospital beds
  • Smart Home Systems: Elderly care and activity recognition
  • Human-Computer Interaction: Pressure-sensitive interfaces and controls
  • Sleep Analysis: Sleep posture monitoring and quality assessment
  • Rehabilitation: Physical therapy progress tracking

Technical Advantages

Self-Supervised Benefits

  • Reduces dependency on expensive manual annotations
  • Enables utilization of large amounts of unlabeled pressure map data
  • Improves model robustness through diverse pretraining

Computational Efficiency

  • Real-time inference capability on edge devices
  • Memory-efficient architecture suitable for embedded systems
  • Scalable design for different computational budgets

Generalization Capabilities

  • Robust performance across different sensor types and configurations
  • Adaptive to varying pressure map resolutions and characteristics
  • Effective domain transfer without fine-tuning

Future Directions

The proposed approach opens several avenues for future research:

  • Extension to multi-person scenarios
  • Integration with other sensing modalities
  • Application to dynamic activity recognition
  • Development of specialized architectures for specific use cases

Recommended citation: Yu, C., et al. (2024). "A Self-Supervised Pressure Map Human Keypoint Detection Approach: Optimizing Generalization and Computational Efficiency Across Datasets." IEEE Conference Proceedings. DOI: 10.1109/10447055.
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