research-article

Contrastive Predictive Coding for Human Activity Recognition

Authors:

Harish Haresamudram,

Thomas PlötzAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 2

Article No.: 65, Pages 1 - 26

https://doi.org/10.1145/3463506

Published: 24 June 2021 Publication History

Abstract

Feature extraction is crucial for human activity recognition (HAR) using body-worn movement sensors. Recently, learned representations have been used successfully, offering promising alternatives to manually engineered features. Our work focuses on effective use of small amounts of labeled data and the opportunistic exploitation of unlabeled data that are straightforward to collect in mobile and ubiquitous computing scenarios. We hypothesize and demonstrate that explicitly considering the temporality of sensor data at representation level plays an important role for effective HAR in challenging scenarios. We introduce the Contrastive Predictive Coding (CPC) framework to human activity recognition, which captures the temporal structure of sensor data streams. Through a range of experimental evaluations on real-life recognition tasks, we demonstrate its effectiveness for improved HAR. CPC-based pre-training is self-supervised, and the resulting learned representations can be integrated into standard activity chains. It leads to significantly improved recognition performance when only small amounts of labeled training data are available, thereby demonstrating the practical value of our approach. Through a series of experiments, we also develop guidelines to help practitioners adapt and modify the framework towards other mobile and ubiquitous computing scenarios.

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Cited By

Chen XZhou XSun MWang H(2025)Temporal Contrastive Learning for Sensor-Based Human Activity Recognition: A Self-Supervised ApproachIEEE Sensors Journal10.1109/JSEN.2024.349193325:1(1839-1850)Online publication date: 1-Jan-2025
https://doi.org/10.1109/JSEN.2024.3491933
DeSmet CGreeley CCook D(2025)Hydra-TS: Enhancing Human Activity Recognition With Multiobjective Synthetic Time-Series Data GenerationIEEE Sensors Journal10.1109/JSEN.2024.348310825:1(763-772)Online publication date: 1-Jan-2025
https://doi.org/10.1109/JSEN.2024.3483108
sedaghati Nardebili SGhaffari A(2025)Application of human activity/action recognition: a reviewMultimedia Tools and Applications10.1007/s11042-024-20576-2Online publication date: 8-Jan-2025
https://doi.org/10.1007/s11042-024-20576-2
Show More Cited By

Index Terms

Contrastive Predictive Coding for Human Activity Recognition
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 2

June 2021

932 pages

EISSN:2474-9567

DOI:10.1145/3472726

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Published: 24 June 2021

Published in IMWUT Volume 5, Issue 2

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Cited By

Chen XZhou XSun MWang H(2025)Temporal Contrastive Learning for Sensor-Based Human Activity Recognition: A Self-Supervised ApproachIEEE Sensors Journal10.1109/JSEN.2024.349193325:1(1839-1850)Online publication date: 1-Jan-2025
https://doi.org/10.1109/JSEN.2024.3491933
DeSmet CGreeley CCook D(2025)Hydra-TS: Enhancing Human Activity Recognition With Multiobjective Synthetic Time-Series Data GenerationIEEE Sensors Journal10.1109/JSEN.2024.348310825:1(763-772)Online publication date: 1-Jan-2025
https://doi.org/10.1109/JSEN.2024.3483108
sedaghati Nardebili SGhaffari A(2025)Application of human activity/action recognition: a reviewMultimedia Tools and Applications10.1007/s11042-024-20576-2Online publication date: 8-Jan-2025
https://doi.org/10.1007/s11042-024-20576-2
Hecker NNapoli ODelgado JRocha ABoccato LBorin E(2025)An Analysis of Time-Frequency Consistency in Human Activity RecognitionIntelligent Systems10.1007/978-3-031-79035-5_5(66-81)Online publication date: 30-Jan-2025
https://doi.org/10.1007/978-3-031-79035-5_5
da Silva BNapoli ODelgado JRocha ABoccato LBorin E(2025)Impact of Pre-training Datasets on Human Activity Recognition with Contrastive Predictive CodingIntelligent Systems10.1007/978-3-031-79035-5_21(306-320)Online publication date: 30-Jan-2025
https://doi.org/10.1007/978-3-031-79035-5_21
Haresamudram HEssa IPlötz T(2024)Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity RecognitionSensors10.3390/s2404123824:4(1238)Online publication date: 15-Feb-2024
https://doi.org/10.3390/s24041238
Li ZLiu HHuan ZLiang J(2024)Active contrastive coding reducing label effort for sensor-based human activity recognitionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23480446:2(3987-3999)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-234804
Thukral MHaresamudram HPlötz T(2024)Cross-Domain HAR: Few-Shot Transfer Learning for Human Activity RecognitionACM Transactions on Intelligent Systems and Technology10.1145/370492116:1(1-35)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3704921
Chen YHu CKimura TLi QLiu SWu FChen G(2024)SemiCMT: Contrastive Cross-Modal Knowledge Transfer for IoT Sensing with Semi-Paired Multi-Modal SignalsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997798:4(1-30)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699779
Logacjov A(2024)Self-supervised Learning for Accelerometer-based Human Activity Recognition: A SurveyProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997678:4(1-42)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699767
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