UASD-Edge: Uncertainty-Aware Self-Distillation for Early Human Activity Recognition on Edge Devices
Main Article Content
Abstract
Early human activity recognition (HAR) on edge devices must trade classification quality against sensing delay while avoiding unreliable supervision from incomplete observations. This paper presents UASD-Edge, a compact early-HAR framework that augments full-window-to-prefix self-distillation with per-sample teacher reliability. The reliability is computed from normalized entropy of the detached full-window prediction and weights only the distillation term, leaving hard-label supervision unchanged. A single 7,995-parameter temporal CNN is shared by all prefix lengths, and no uncertainty branch or additional inference parameters are introduced. We evaluate on the official subject-disjoint UCI Human Activity Recognition Using Smartphones split using the three dynamic locomotion classes and six inertial sequence channels. Across seeds 13, 37, and 73, UASD-Edge obtains macro-F1 of 94.51±1.18%, 97.88±1.12%, 98.22±1.07%, and 98.56±0.77% at 25%, 50%, 75%, and 100% observation. Against a same-backbone PrefixKD baseline with the identical distillation coefficient, UASD is lower by 0.22 percentage points at 25% but improves the later three checkpoints by 0.14–0.19 points, so no statistical-significance claim is made. A validation-selected global MSP gate reaches 98.05±0.75% test accuracy and 98.03±0.76% macro-F1 after observing 32.59±2.96% of the 2.56-s window on average (0.834 s). Frozen TorchScript batch-1 FP32 inference takes 0.0319 ms median at the full window on one Intel Xeon Platinum 8573C CPU thread. The results support uncertainty-weighted distillation as a low-cost regularizer for later prefixes while exposing a reproducible accuracy-earliness operating point.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
-
All published articles are open access, with copyright shared between the authors and the journal. The journal retains the exclusive right to first publication and identifies itself as original publisher.
-
Articles are made available under a Creative Commons CC BY‑NC‑ND 4.0 license, permitting users to copy and distribute the content for non‑commercial purposes only, without modification, provided proper credit is given to authors and JCPHS.
-
Redistribution or reuse must preserve the full citation details and include a link to the license. No derivatives, adaptations, or commercial uses are permitted without prior written permission.
References
1. Zhang, S. and Qiu, L. (2024). Data-driven assessment of pavement performance and resilience under extreme weather using the Long-Term Pavement Performance Program. AI and Data Science Journal, vol. 1, no. 1, pp. 57–66
2. Zhang, H. (2024). Graph-Aware Multi-Task Learning For Early Prediction Of Timing Violations And Routing Congestion In Digital Ic Physical Design. World Journal of Information Technology, 2(1): 13-20.
3. Wang, J., Tan, Y., Jiang, B., Wu, B., & Liu, W. (2025). Dynamic marketing uplift modeling: A symmetry-preserving framework integrating causal forests with deep reinforcement learning for personalized intervention strategies. Symmetry, 17(4), 610.
4. Zhao, X., Liu, J., Wang, Y., & Wang, J. (2026). CryptoMamba-SSM: Linear complexity state space models for cryptocurrency volatility prediction. IEEE Open Journal of the Computer Society, 7, 226-243.
5. Wang, J., Liu, J., Zheng, W., & Ge, Y. (2025). Temporal heterogeneous graph contrastive learning for fraud detection in credit card transactions. IEEE Access.
6. Sun, T., Yang, J., Li, J., Chen, J., Liu, M., Fan, L., & Wang, X. (2024). Enhancing auto insurance risk evaluation with transformer and SHAP. IEEE Access, 12, 116546-116557.
7. Zhang, H. (2025). Reinforcement Learning Approaches for Layout Optimization in Electronic Design Automation with Electromagnetic Compatibility Constraints. Frontiers in Robotics and Automation, 2(2), 77-93.
8. Han, X., Yang, Y., Chen, J., Wang, M., & Zhou, M. (2025). Symmetry-aware credit risk modeling: A deep learning framework exploiting financial data balance and invariance. Symmetry, 17(3), 341.
9. Chen, J., Cui, Y., Zhang, X., Yang, J., & Zhou, M. (2024). Temporal convolutional network for carbon tax projection: A data-driven approach. Applied Sciences, 14(20), 9213.
10. Sun, T., Wang, M., & Chen, J. (2025). Leveraging machine learning for tax fraud detection and risk scoring in corporate filings. Asian Business Research Journal, 10(11), 1-13.
11. Chen, J., Wang, M., & Sun, T. (2025). Intelligent tax systems and the role of natural language processing in regulatory interpretation. American Journal of Machine Learning, 6(4), 74-94.
12. Chen, Z., Liu, J., & Chen, J. (2025). Machine Learning Methods for Financial Forecasting in Enterprise Planning: Transitioning from Rule-Based Models to Predictive Analytics. Frontiers in Artificial Intelligence Research, 2(3), 541-564.
13. Chen, J., Liu, J., Liang, Y., & Zhou, M. (2026). KE-MLLM: A knowledge-enhanced multi-sensor learning framework for explainable fake review detection. Applied Sciences, 16(6), 2909.
14. Yue, X., Yang, J., & Liu, W. (2026). FraudDebate-Agent: A Multi-Agent LLM Framework with an Evidence-Based Debate Mechanism for Financial Statement Fraud Detection. Mathematics, 14(15), 2695.
15. Zhu, R. & Yue, X. (2024). An Explainable Machine Learning Framework For Predicting Dividend Sustainability And Financial Risk Of U.s. Reits Under Interest-Rate Shocks. Journal of Trends in Finance and Economics, 1(2): 48-57.
16. Yue, X. & Zhu, R. (2024). An Explainable Hybrid Machine Learning Framework For Cash-Flow-Conditioned Multi-Horizon Liquidity Risk Early Warning: An Sme-Oriented Benchmark Study. World Journal of Management Science, 2(1): 63-72.
17. Cui, Y., Han, X., Chen, J., Zhang, X., Yang, J., & Zhang, X. (2025). FraudGNN-RL: a graph neural network with reinforcement learning for adaptive financial fraud detection. IEEE Open Journal of the Computer Society, 6, 426-437.
18. Zi, B. (2024). Cloud-native distributed systems for real-time payment intelligence. AI and Data Science Journal, 1(1), 51-56.
19. Zhang, S. & Qiu, L. (2023). An Interpretable, Uncertainty-Aware Framework For Bridge Deterioration Prediction And Risk-Based Maintenance Prioritization. Academic Journal of Architecture and Civil Engineering, 1(2): 21-28.
20. Jin, J., Xing, S., Ji, E., & Liu, W. (2025). Xgate: explainable reinforcement learning for transparent and trustworthy api traffic management in iot sensor networks. Sensors, 25(7), 2183.
21. Xing, S., Wang, Y., & Liu, W. (2025). Multi-dimensional anomaly detection and fault localization in microservice architectures: A dual-channel deep learning approach with causal inference for intelligent sensing. Sensors, 25(11), 3396.
22. Xing, S., & Wang, Y. (2025). Proactive data placement in heterogeneous storage systems via predictive multi-objective reinforcement learning. IEEE Access, 13, 117986-117998.
23. Ji, E., Wang, Y., Xing, S., & Jin, J. (2025). Hierarchical reinforcement learning for energy-efficient API traffic optimization in large-scale advertising systems. IEEE Access.
24. Xing, S., & Wang, Y. (2025). Cross-modal attention networks for multi-modal anomaly detection in system software. IEEE Open Journal of the Computer Society.
25. Liu, Y., Ren, S., Wang, X., & Zhou, M. (2024). Temporal logical attention network for log-based anomaly detection in distributed systems. Sensors, 24(24), 7949.
26. Ren, S., Jin, J., Niu, G., & Liu, Y. (2025). ARCS: Adaptive reinforcement learning framework for automated cybersecurity incident response strategy optimization. Applied Sciences, 15(2), 951.
27. Li, P., Ren, S., Zhang, Q., Wang, X., & Liu, Y. (2024). Think4SCND: Reinforcement learning with thinking model for dynamic supply chain network design. IEEE Access, 12, 195974-195985.
28. Hu, X., Zhao, X., Wang, J., & Yang, Y. (2025). Information-theoretic multi-scale geometric pre-training for enhanced molecular property prediction. Plos one, 20(10), e0332640.
29. Wang, M., Zhang, X., Yang, Y., & Wang, J. (2025). Explainable machine learning in risk management: Balancing accuracy and interpretability. Journal of Financial Risk Management, 14(3), 185-198.
30. Yang, J., Li, P., Cui, Y., Han, X., & Zhou, M. (2025). Multi-sensor temporal fusion transformer for stock performance prediction: An adaptive Sharpe ratio approach. Sensors, 25(3), 976.