# Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition

arXiv: https://arxiv.org/abs/2211.06173 · HTML: https://mlsanity.com/paper/2211.06173/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [An Unsupervised Autoregressive Model for Speech Representation Learning](https://mlsanity.com/paper/1904.03240/) (2019)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Generative Pre-Training for Speech with Autoregressive Predictive Coding](https://mlsanity.com/paper/1910.12607/) (2019)
- [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding](https://mlsanity.com/paper/2011.14097/) (2020)
- [Contrastive Predictive Coding for Human Activity Recognition](https://mlsanity.com/paper/2012.05333/) (2020)
- [Protecting Sensory Data against Sensitive Inferences](https://mlsanity.com/paper/1802.07802/) (2018)
- [Improved Speech Representations with Multi-Target Autoregressive Predictive Coding](https://mlsanity.com/paper/2004.05274/) (2020)
- [IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition](https://mlsanity.com/paper/2006.05675/) (2020)
- [ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition](https://mlsanity.com/paper/2202.00758/) (2022)
- [Sense and Learn: Self-Supervision for Omnipresent Sensors](https://mlsanity.com/paper/2009.13233/) (2020)
- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://mlsanity.com/paper/2208.00467/) (2022)
- [Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables](https://mlsanity.com/paper/1811.08127/) (2018)

## Built on by (most cited first, 6 of 6)

- [Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition](https://mlsanity.com/paper/2306.01108/) (2023)
- [RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition](https://mlsanity.com/paper/2512.08984/) (2025)
- [Don't freeze: Finetune encoders for better Self-Supervised HAR](https://mlsanity.com/paper/2307.01168/) (2023)
- [SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning](https://mlsanity.com/paper/2405.00712/) (2024)
- [RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment](https://mlsanity.com/paper/2607.26631/) (2026)
- [Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2212.13918/) (2022)

## Abstract (arXiv)

The dichotomy between the challenging nature of obtaining annotations for activities, and the more straightforward nature of data collection from wearables, has resulted in significant interest in the development of techniques that utilize large quantities of unlabeled data for learning representations. Contrastive Predictive Coding (CPC) is one such method, learning effective representations by leveraging properties of time-series data to setup a contrastive future timestep prediction task. In this work, we propose enhancements to CPC, by systematically investigating the encoder architecture, the aggregator network, and the future timestep prediction, resulting in a fully convolutional architecture, thereby improving parallelizability. Across sensor positions and activities, our method shows substantial improvements on four of six target datasets, demonstrating its ability to empower a wide range of application scenarios. Further, in the presence of very limited labeled data, our technique significantly outperforms both supervised and self-supervised baselines, positively impacting situations where collecting only a few seconds of labeled data may be possible. This is promising, as CPC does not require specialized data transformations or reconstructions for learning effective representations.