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DSHformer: Locality-Sensitive Hash Attention and Prototype Alignment for Sensor-Based Human Activity Recognition.

Xiaofeng Zhang1, Muzi Ding1, Tangzhi Teng1

  • 1School of Artificial Intelligence and Computer Science, Nantong University, Seyuan Campus, Nantong 226019, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
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DSHformer enhances human activity recognition (HAR) by combining efficient attention mechanisms with prototype learning. This framework improves accuracy and generalization for sensor-based HAR systems on wearable devices.

Area of Science:

  • Computer Science
  • Machine Learning
  • Signal Processing

Background:

  • Sensor-based human activity recognition (HAR) is crucial for healthcare and wearables.
  • Deep learning advances HAR, but distribution shifts and high computational complexity limit real-world use.
  • Existing methods struggle with generalization across users/sensors and efficient long-sequence processing.

Purpose of the Study:

  • To propose DSHformer, an accuracy-oriented HAR framework addressing generalization and efficiency.
  • To combine compact channel-temporal encoding with locality-sensitive hashing (LSH)-based attention.
  • To enable efficient and accurate HAR on resource-constrained wearable devices.

Main Methods:

  • A low-parameter patch-based graph-attention encoder models sensor channel-temporal dynamics.
Keywords:
Transformerdomain shifthuman activity recognitionlocality-sensitive hashingprototype alignmentwearable sensors

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  • A trainable prototype pool and decomposition network improve class separability via prototype alignment.
  • A decomposition-stable LSH-based attention mechanism with O(LlogL) complexity is introduced for HAR.

    • Main Results:

    • DSHformer achieved high accuracies on five benchmarks: WISDM (98.6%), UCI-HAR (93.7%), PAMAP2 (98.4%), Opportunity (88.5%), and UniMiB-SHAR (96.6%).
    • The framework demonstrated competitive or superior performance against Transformer variants and HAR-specific baselines.
    • Ablation studies confirmed the significant contribution of each DSHformer component.

    Conclusions:

    • DSHformer effectively addresses distribution shift and computational complexity in sensor-based HAR.
    • The proposed LSH-based attention mechanism enables efficient long-sequence modeling.
    • DSHformer offers a promising solution for real-world, resource-constrained HAR applications.