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Quantifying Uncertainty in Machine Learning-Based Pervasive Systems: Application to Human Activity Recognition

arXiv 2512.09775 ↗First posted 10 Dec 2025cs.SE cs.AIcited by 0

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Abstract (arXiv)

The recent convergence of pervasive computing and machine learning has given rise to numerous services, impacting almost all areas of economic and social activity. However, the use of AI techniques precludes certain standard software development practices, which emphasize rigorous testing to ensure the elimination of all bugs and adherence to well-defined specifications. ML models are trained on numerous high-dimensional examples rather than being manually coded. Consequently, the boundaries of their operating range are uncertain, and they cannot guarantee absolute error-free performance. In this paper, we propose to quantify uncertainty in ML-based systems. To achieve this, we propose to adapt and jointly utilize a set of selected techniques to evaluate the relevance of model predictions at runtime. We apply and evaluate these proposals in the highly heterogeneous and evolving domain of Human Activity Recognition (HAR). The results presented demonstrate the relevance of the approach, and we discuss in detail the assistance provided to domain experts.

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References · 11

  1. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  6. 2019Deep Evidential Regressioncited by 355
  7. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  8. 2018Inhibited Softmax for Uncertainty Estimation in Neural Networkscited by 19
  9. 2022Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognitioncited by 12
  10. 2023Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcitycited by 9
  11. 2024Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognitioncited by 4

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