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.
Yichao Diao, Hoi Leong Lee, Gang Jin· Journal of King Saud Univers...· 0 citations
Cooperative unmanned aerial vehicle (UAV) path planning is one of the core challenges in the field of autonomous systems, requiring the simultaneous guarantee of safety, efficiency, and multi-agent collaborative consistency in continuously dynamic obstacle environments. Existing methods either rely on hand-crafted heuristic rules and thus lack adaptability, or employ deep reinforcement learning (DRL) but suffer from low sample efficiency and sparse reward problems, making it difficult to achieve high task success rates in complex dynamic scenarios. This paper proposes SkyAgent—a lightweight large language model (LLM)-driven hierarchical reinforcement learning framework specifically designed for adaptive cooperative path planning of two UAVs. SkyAgent adopts Qwen2.5-1.5B (Q4_K_M quantization, approximately 1 GB VRAM) as the high-level semantic planner, performing fully offline inference at approximately 1,900 ms model forward-pass per call on a local GPU via the Ollama framework without any external API; employs Proximal Policy Optimization (PPO) as the low-level continuous action controller; and achieves efficient decoupled coordination of the two-tier policy through an asynchronous sub-goal injection mechanism (replanning every K=20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K=20$$\end{document} steps). In a continuous two-dimensional simulation environment of 100×100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100 \times 100$$\end{document} m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document} containing 8 static and 3 dynamic obstacles, SkyAgent is systematically evaluated against 7 baseline methods (Random, Greedy, APF, A*, PPO, SAC, OraclePID) over 30 random seeds. Experimental results demonstrate that SkyAgent achieves a mission success rate of 96.7%, comprehensively outperforming all baseline methods, improving by 16.7 percentage points over the vanilla PPO baseline, reducing collisions by 83%, shortening path length by 27 m, and improving sample efficiency approximately 3.3×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} (requiring only 55k training steps to reach the 80% success rate threshold). In the Dynamic-heavy scenario (30 obstacles), the success rate still reaches 90.9%, demonstrating significant generalization robustness. Six ablation experiments quantitatively validate the indispensability of the LLM planner, sub-goal guidance, sensor observations, cooperative reward, and reward shaping components. This work provides a feasible technical pathway and a reproducible evaluation benchmark for the collaborative deployment of lightweight LLMs and RL on GPU-equipped resource-constrained edge devices; we note that the present evaluation is confined to a two-dimensional, idealized simulation and that on-board deployment presupposes a GPU-class accelerator rather than an MCU-class micro-UAV.
Yuting Cao, Zheng Zhao, Jiekai Wu et al.· Journal of King Saud Univers...· 0 citations