Temporal Structure Matters for Efficient Test-Time Adaptation in Wearable Human Activity Recognition

• URL: http://arxiv.org/abs/2605.04617v2
• Date: Mon, 11 May 2026 07:28:51 GMT
• Title: Temporal Structure Matters for Efficient Test-Time Adaptation in Wearable Human Activity Recognition
• Abstract summary: Test-time adaptation (TTA) mitigates performance degradation under real-world cross-user distribution shifts.<n>Existing methods largely inherit assumptions from vision tasks and underexploit the inherent inter-window temporal structure in WHAR streams.<n>We propose SIGHT, a lightweight and backpropagation-free TTA framework for WHAR, enabling real-time edge deployment.
• Score: 0.20391237204597365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wearable human activity recognition (WHAR) models often suffer from performance degradation under real-world cross-user distribution shifts. Test-time adaptation (TTA) mitigates this degradation by adapting models online using unlabeled test streams, yet existing methods largely inherit assumptions from vision tasks and underexploit the inherent inter-window temporal structure in WHAR streams. In this paper, we revisit such temporal structure as a feature-conditioned inference signal rather than merely an output-space smoothing prior. We derive the insight that temporal continuity and observation-induced feature deviations provide complementary cues for determining when to preserve or release temporal inertia and where to route prediction refinement during likely transitions. Building upon this insight, we propose SIGHT, a lightweight and backpropagation-free TTA framework for WHAR, enabling real-time edge deployment. SIGHT estimates predictive surprise by comparing the current feature with a prototype-based expected state, and then uses the resulting feature deviation to guide geometry-aware transition routing based on prototype alignment and stream-level marginal habit tracking. Evaluations on real-world datasets confirm that SIGHT outperforms existing TTA baselines while reducing computational and memory costs.

Related papers

• Time-warping estimation via stationarity-based learning of the de-warped signal [0.8594140167290095]
  Time-warping estimation is a fundamental problem in signal processing with applications in bioacoustics, radar, and biomedical analysis.<n>This paper introduces a Time-Warping Estimation Trainable (TWET) model for estimating timewarping functions from a single observation.
  arXiv  Detail & Related papers  (2026-09-15T08:02:58Z)
• Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting [64.2640656619583]
  Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-augmented prediction.<n>We propose a Retrieval-guided Invariant-Dynamic DEcomposition framework for time series forecasting.
  arXiv  Detail & Related papers  (2026-05-24T07:31:55Z)
• FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection [63.91351553178842]
  FACTOR is a training-free Test-time adaptation for Open-vocabulary object detection.<n>It quantifies attribute sensitivity, semantic relevance, and prediction variation to selectively suppress attribute-dependent predictions.<n>Experiments on PASCAL-C, COCO-C, and FoggyCityscapes show that FACTOR consistently outperforms prior TTA methods.
  arXiv  Detail & Related papers  (2026-05-05T02:31:18Z)
• Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding [0.0]
  We introduce the Temporal Contrastive Transformer (TCT), a representation learning framework designed to capture contextual temporal dynamics in sequences of financial transactions.<n>We show that embeddings alone achieve meaningful predictive performance (AUC 0.8644), indicating that the model captures non-trivial temporal structure.<n>While not yet production-ready, these results point to a promising direction for reducing reliance on feature engineering in financial crime detection.
  arXiv  Detail & Related papers  (2026-03-31T09:42:08Z)
• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference [18.524636088926425]
  Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams.<n>To address this, we introduce Order-Aware Test-Time Adaptation (OATTA)<n>OATTA consistently boosts established baselines, improving accuracy by up to 6.35%.
  arXiv  Detail & Related papers  (2026-01-28T20:07:40Z)
• How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning [15.102926671713668]
  We propose ST-SSDL, a Spatio-Temporal series time forecasting framework.<n>It discretizes latent space using learnable prototypes that represent typicaltemporal patterns.<n>Experiments show that ST-SSDL consistently outperforms state-of-the-art baselines across multiple metrics.
  arXiv  Detail & Related papers  (2025-10-06T15:21:13Z)
• Solving Inverse Problems with FLAIR [68.87167940623318]
  We present FLAIR, a training-free variational framework that leverages flow-based generative models as prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency [6.7236795813629]
  In streaming setups data flows are affected by factors that yield non-stationarities in the patterns (concept drift) We propose a novel approach, AiGAS-dEVL, which relies on growing neural gas to characterize the distributions of all concepts detected within the stream over time. Our approach exposes that the online analysis of the behavior of these points over time facilitates the definition of the evolution of concepts in the feature space.
  arXiv  Detail & Related papers  (2024-07-07T14:04:57Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation [1.4530711901349282]
  We propose to validate test-time adaptation methods using datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift. We enhance the well-established self-training framework by incorporating a small memory buffer to increase model stability.
  arXiv  Detail & Related papers  (2023-09-18T19:34:23Z)
• OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning [67.07363529640784]
  We propose OpenSTL to categorize prevalent approaches into recurrent-based and recurrent-free models. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and forecasting weather. We find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models.
  arXiv  Detail & Related papers  (2023-06-20T03:02:14Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.