# Contrastive Predictive Coding for Human Activity Recognition

arXiv: https://arxiv.org/abs/2012.05333 · HTML: https://mlsanity.com/paper/2012.05333/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 30 of 55)

- [Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data](https://mlsanity.com/paper/2206.02909/) (2022)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Scaling Wearable Foundation Models](https://mlsanity.com/paper/2410.13638/) (2024)
- [Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data](https://mlsanity.com/paper/2206.02353/) (2022)
- [ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition](https://mlsanity.com/paper/2202.00758/) (2022)
- [IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2402.01049/) (2024)
- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://mlsanity.com/paper/2208.00467/) (2022)
- [RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data](https://mlsanity.com/paper/2411.18822/) (2024)
- [CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking](https://mlsanity.com/paper/2307.16847/) (2023)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels](https://mlsanity.com/paper/2404.01958/) (2024)
- [Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition](https://mlsanity.com/paper/2205.10071/) (2022)
- [Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition](https://mlsanity.com/paper/2310.14390/) (2023)
- [Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensors](https://mlsanity.com/paper/2410.20034/) (2024)
- [Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition](https://mlsanity.com/paper/2109.02054/) (2021)
- [Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition](https://mlsanity.com/paper/2306.01108/) (2023)
- [Self-Supervised WiFi-Based Activity Recognition](https://mlsanity.com/paper/2104.09072/) (2021)
- [Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition](https://mlsanity.com/paper/2211.06173/) (2022)
- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition](https://mlsanity.com/paper/2203.12230/) (2022)
- [LipLearner: Customizable Silent Speech Interactions on Mobile Devices](https://mlsanity.com/paper/2302.05907/) (2023)
- [Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning](https://mlsanity.com/paper/2209.00990/) (2022)
- [Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition](https://mlsanity.com/paper/2210.03382/) (2022)
- [Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition](https://mlsanity.com/paper/2309.01262/) (2023)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [Balancing Continual Learning and Fine-tuning for Human Activity Recognition](https://mlsanity.com/paper/2401.02255/) (2024)
- [IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications](https://mlsanity.com/paper/2209.00945/) (2022)
- [Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition](https://mlsanity.com/paper/2211.04331/) (2022)
- [Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks](https://mlsanity.com/paper/2302.12636/) (2023)

## Abstract (arXiv)

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 long-term 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.