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Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

arXiv 1706.00527 ↗First posted 2 Jun 2017cs.CVcited by 130

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Abstract (arXiv)

While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson's Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54% to 86.88%.

Key facts. Cited by 130 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2017.

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Citing papers per year: 2021: 18; 2022: 24; 2023: 25; 2024: 25; 2025: 22; 2026: 16.

130 papers.

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  1. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  2. 2022Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Datacited by 71
  3. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  4. 2022Assessing the State of Self-Supervised Human Activity Recognition using Wearablescited by 63
  5. 2021Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advancescited by 54
  6. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  7. 2022What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?cited by 39
  8. 2024A Comprehensive Survey on Data Augmentationcited by 38
  9. 2021Neighborhood Contrastive Learning Applied to Online Patient Monitoringcited by 32
  10. 2021SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Datacited by 31
  11. 2023FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Spacecited by 31
  12. 2024UniMTS: Unified Pre-training for Motion Time Seriescited by 22
38 more
  1. 2023Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognitioncited by 17
  2. 2021MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classificationcited by 16
  3. 2023TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Seriescited by 16
  4. 2023Self-supervised Representation Learning From Random Data Projectorscited by 14
  5. 2024Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Taskcited by 14
  6. 2022Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Penscited by 13
  7. 2023Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approachcited by 13
  8. 2022Data Augmentation for Electrocardiogramscited by 12
  9. 2023Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learningcited by 12
  10. 2023TimeDRL: Disentangled Representation Learning for Multivariate Time-Seriescited by 10
  11. 2023A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learningcited by 9
  12. 2023Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Surveycited by 9
  13. 2023Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognitioncited by 9
  14. 2023Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognitioncited by 9
  15. 2021Adaptive Weighting Scheme for Automatic Time-Series Data Augmentationcited by 8
  16. 2022Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wildcited by 8
  17. 2021Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognitioncited by 7
  18. 2022Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networkscited by 7
  19. 2023Towards Diverse and Coherent Augmentation for Time-Series Forecastingcited by 7
  20. 2022Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognitioncited by 6
  21. 2024Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognitioncited by 6
  22. 2022Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Datacited by 5
  23. 2022Data augmentation on-the-fly and active learning in data stream classificationcited by 5
  24. 2023Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognitioncited by 5
  25. 2023Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encodercited by 5
  26. 2023SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentationcited by 5
  27. 2024Data Augmentation Policy Search for Long-Term Forecastingcited by 5
  28. 2024Large Language Model-Guided Semantic Alignment for Human Activity Recognitioncited by 5
  29. 2021The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platformscited by 4
  30. 2021DTW-Merge: A Novel Data Augmentation Technique for Time Series Classificationcited by 4
  31. 2022SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Seriescited by 4
  32. 2024Phase-driven Domain Generalizable Learning for Nonstationary Time Seriescited by 4
  33. 2024Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecastingcited by 4
  34. 2024Benchmarking Reliability of Deep Learning Models for Pathological Gait Classificationcited by 4
  35. 2025WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatchcited by 4
  36. 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Reviewcited by 4
  37. 2025RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognitioncited by 4
  38. 2021CoughTrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-efficient Sensitivity-prioritized Time Series Classifiercited by 3

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