Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches

• URL: http://arxiv.org/abs/2512.19713v1
• Date: Mon, 15 Dec 2025 08:03:46 GMT
• Title: Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches
• Authors: Taoran Sheng, Manfred Huber,
• Abstract summary: Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms.<n>This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR.<n>We develop and empirically compare: (1) traditional fully supervised learning, (2) basic unsupervised learning, (3) a weakly supervised learning approach with constraints, (4) a multi-task learning approach with knowledge sharing, (5) a self-supervised approach based on domain expertise, and (6) a novel weakly self-supervised learning framework that leverages domain knowledge and minimal labeled data.
• Score: 1.1816942730023885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labeling needs but often deliver suboptimal performance. This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR, with particular focus on novel approaches that minimize labeling requirements while maintaining competitive accuracy. We develop and empirically compare: (1) traditional fully supervised learning, (2) basic unsupervised learning, (3) a weakly supervised learning approach with constraints, (4) a multi-task learning approach with knowledge sharing, (5) a self-supervised approach based on domain expertise, and (6) a novel weakly self-supervised learning framework that leverages domain knowledge and minimal labeled data. Experiments across benchmark datasets demonstrate that: (i) our weakly supervised methods achieve performance comparable to fully supervised approaches while significantly reducing supervision requirements; (ii) the proposed multi-task framework enhances performance through knowledge sharing between related tasks; (iii) our weakly self-supervised approach demonstrates remarkable efficiency with just 10\% of labeled data. These results not only highlight the complementary strengths of different learning paradigms, offering insights into tailoring HAR solutions based on the availability of labeled data, but also establish that our novel weakly self-supervised framework offers a promising solution for practical HAR applications where labeled data are limited.

Related papers

• Reduction of Supervision for Biomedical Knowledge Discovery [28.68816381566995]
  It is essential to employ automated methods for knowledge extraction and processing.<n>Finding the right balance between the level of supervision and the effectiveness of models poses a significant challenge.<n>Our study addresses the challenge of identifying semantic relationships between biomedical entities in unstructured text.
  arXiv  Detail & Related papers  (2025-04-13T14:05:40Z)
• From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning [71.41062111470414]
  Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities.<n>We present a novel framework that expands the capability of standard object detectors beyond mere object recognition to complex event understanding.<n>Our key innovation lies in bridging the semantic gap between object detection and event understanding without requiring expensive task-specific training.
  arXiv  Detail & Related papers  (2025-02-09T10:30:54Z)
• Towards Effective Evaluations and Comparisons for LLM Unlearning Methods [97.2995389188179]
  This paper seeks to refine the evaluation of machine unlearning for large language models.<n>It addresses two key challenges -- the robustness of evaluation metrics and the trade-offs between competing goals.
  arXiv  Detail & Related papers  (2024-06-13T14:41:00Z)
• Incremental Self-training for Semi-supervised Learning [56.57057576885672]
  IST is simple yet effective and fits existing self-training-based semi-supervised learning methods. We verify the proposed IST on five datasets and two types of backbone, effectively improving the recognition accuracy and learning speed.
  arXiv  Detail & Related papers  (2024-04-14T05:02:00Z)
• Composite Learning for Robust and Effective Dense Predictions [81.2055761433725]
  Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task. We find that jointly training a dense prediction (target) task with a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks.
  arXiv  Detail & Related papers  (2022-10-13T17:59:16Z)
• Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition [1.869225486385596]
  We explore the hypothesis that leveraging multiple modalities can lead to better recognition. We extend a number of recent contrastive self-supervised approaches for the task of Human Activity Recognition. We propose a flexible, general-purpose framework for performing multimodal self-supervised learning.
  arXiv  Detail & Related papers  (2022-05-20T10:39:16Z)
• Boosting Supervised Learning Performance with Co-training [15.986635379046602]
  We propose a new light-weight self-supervised learning framework that could boost supervised learning performance with minimum additional cost. Our results show that both self-supervised tasks can improve the accuracy of the supervised task and, at the same time, demonstrates strong domain adaption capability.
  arXiv  Detail & Related papers  (2021-11-18T17:01:17Z)
• Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables [9.399840807973545]
  generative adversarial network (GAN) paradigms to learn unsupervised feature representations from wearable sensor data. Geometrically-Guided GAN or Guided-GAN for the task. Result: Guided-GAN to outperform existing unsupervised approaches whilst closely approaching the performance with fully supervised representations.
  arXiv  Detail & Related papers  (2021-10-12T04:29:21Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Self-supervised driven consistency training for annotation efficient histopathology image analysis [13.005873872821066]
  Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. We propose a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning. We also propose a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data.
  arXiv  Detail & Related papers  (2021-02-07T19:46:21Z)
• Can Semantic Labels Assist Self-Supervised Visual Representation Learning? [194.1681088693248]
  We present a new algorithm named Supervised Contrastive Adjustment in Neighborhood (SCAN) In a series of downstream tasks, SCAN achieves superior performance compared to previous fully-supervised and self-supervised methods. Our study reveals that semantic labels are useful in assisting self-supervised methods, opening a new direction for the community.
  arXiv  Detail & Related papers  (2020-11-17T13:25:00Z)
• On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake? [31.563514432259897]
  We present a novel framework integrating self-supervised pretraining, active learning, and consistency-regularized self-training. Our experiments reveal two key insights: (i) Self-supervised pre-training significantly improves semi-supervised learning, especially in the few-label regime. We fail to observe any additional benefit of state-of-the-art active learning algorithms when combined with state-of-the-art S4L techniques.
  arXiv  Detail & Related papers  (2020-11-16T17:34:55Z)
• Spectrum-Guided Adversarial Disparity Learning [52.293230153385124]
  We propose a novel end-to-end knowledge directed adversarial learning framework. It portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art.
  arXiv  Detail & Related papers  (2020-07-14T05:46:27Z)

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.