# Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes

arXiv: https://arxiv.org/abs/2111.07089 · HTML: https://mlsanity.com/paper/2111.07089/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [A Theoretical Analysis of Contrastive Unsupervised Representation Learning](https://mlsanity.com/paper/1902.09229/) (2019)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020)
- [Adversarial Unsupervised Representation Learning for Activity Time-Series](https://mlsanity.com/paper/1811.06847/) (2018)

## Built on by (most cited first, 8 of 8)

- [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)
- [E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series](https://mlsanity.com/paper/2402.14041/) (2024)
- [LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning](https://mlsanity.com/paper/2210.07340/) (2022)
- [Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR](https://mlsanity.com/paper/2311.12674/) (2023)
- [Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes](https://mlsanity.com/paper/2406.14446/) (2024)
- [Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals](https://mlsanity.com/paper/2406.08930/) (2024)
- [Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers](https://mlsanity.com/paper/2502.17477/) (2025)

## Abstract (arXiv)

Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical. This paper discusses ways in which self-supervised approaches that use contrastive losses, such as SimCLR and BYOL, previously applied to the vision domain, can be applied to high-dimensional health signals for downstream classification tasks of various diseases spanning sleep, heart, and metabolic conditions. To this end, we adapt the data augmentation step and the overall architecture to suit the temporal nature of the data (wearable traces) and evaluate on 5 downstream tasks by comparing other state-of-the-art methods including supervised learning and an adversarial unsupervised representation learning method. We show that SimCLR outperforms the adversarial method and a fully-supervised method in the majority of the downstream evaluation tasks, and that all self-supervised methods outperform the fully-supervised methods. This work provides a comprehensive benchmark for contrastive methods applied to the wearable time-series domain, showing the promise of task-agnostic representations for downstream clinical outcomes.