MST-HDQ: Multi-Scale temporal hash discriminative quantization for wearable sensor sequence recognition

Abstract

Sensor-based human activity recognition (HAR) from wearable devices is fundamental to applications such as mobile health monitoring, smart sports training, rehabilitation assessment, and context-aware computing. Wearable sensor sequence recognition requires representations that are temporally expressive, compact, and robust to class imbalance. Existing sensing systems typically treat temporal modeling and compact representation learning as separate design problems: temporal architectures often terminate in dense classifier logits, whereas hash-based objectives operate on features that have already been formed. This decoupled design limits the discriminability of learned activity codes, particularly for minority classes in imbalanced datasets. We propose MST-HDQ, a compact sequence-code learning framework that co-designs a sensor-aware large-kernel temporal encoder, wearable-sequence hierarchical attentive aggregation, a hash-discriminative quantization head, and a class-robust training objective. The temporal encoder combines multi-scale local branches with depthwise large-kernel temporal blocks to capture both short transient motions and longer activity phases, while the aggregation module fuses attentive temporal pooling with multi-depth feature summaries to select informative temporal moments across encoder depths. The quantization head learns compact prototype-discriminative sequence codes through scalar codebook discretization and normalized prototype classification. Experiments on OPPORTUNITY, PAMAP2, WISDM, and UniMiB-SHAR under the Hash-MMDC protocol show consistent macro-F1 improvements over reproduced baselines, with paired 50-seed Wilcoxon tests showing p<0.001\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}. Controlled comparisons with additional re-implemented baselines, grouped ablations, hash-space diagnostics, sensitivity studies, and stricter segment-grouped and subject-independent splits further support the robustness of the gains. MST-HDQ preserves compact 64-dimensional codes while requiring only 0.622M–0.813M trainable parameters, making it suitable for efficient wearable sensing deployments. The results demonstrate that jointly optimizing temporal evidence extraction and code-space geometry yields more class-complete hash structures than treating these objectives independently.