# Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition

arXiv: https://arxiv.org/abs/2210.01459 · HTML: https://mlsanity.com/paper/2210.01459/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Audio-Visual Instance Discrimination with Cross-Modal Agreement](https://mlsanity.com/paper/2004.12943/) (2020)
- [Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks](https://mlsanity.com/paper/2008.07404/) (2020)
- [Learning Representations from Audio-Visual Spatial Alignment](https://mlsanity.com/paper/2011.01819/) (2020)
- [Human Activity Recognition from Wearable Sensor Data Using Self-Attention](https://mlsanity.com/paper/2003.09018/) (2020)
- [Quo Vadis, Skeleton Action Recognition ?](https://mlsanity.com/paper/2007.02072/) (2020)
- [Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition](https://mlsanity.com/paper/2110.12163/) (2021)
- [More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors](https://mlsanity.com/paper/2202.08267/) (2022)

## Built on by (most cited first, 13 of 13)

- [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)
- [iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition](https://mlsanity.com/paper/2402.09445/) (2024)
- [M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training](https://mlsanity.com/paper/2404.17391/) (2024)
- [Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition](https://mlsanity.com/paper/2306.01108/) (2023)
- [Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2306.05285/) (2023)
- [TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines](https://mlsanity.com/paper/2408.14146/) (2024)
- [Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices](https://mlsanity.com/paper/2402.03714/) (2024)
- [Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2411.07828/) (2024)
- [IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition](https://mlsanity.com/paper/2508.01894/) (2025)
- [AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild](https://mlsanity.com/paper/2605.22715/) (2026)
- [Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2504.00438/) (2025)
- [SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models](https://mlsanity.com/paper/2507.06405/) (2025)
- [Dynamic Influence-Weighted Distillation for Single-IMU Activity Recognition](https://mlsanity.com/paper/2608.24904/) (2026)

## Abstract (arXiv)

We address the well-known wearable activity recognition problem of having to work with sensors that are non-optimal in terms of information they provide but have to be used due to wearability/usability concerns (e.g. the need to work with wrist-worn IMUs because they are embedded in most smart watches). To mitigate this problem we propose a method that facilitates the use of information from sensors that are only present during the training process and are unavailable during the later use of the system. The method transfers information from the source sensors to the latent representation of the target sensor data through contrastive loss that is combined with the classification loss during joint training. We evaluate the method on the well-known PAMAP2 and Opportunity benchmarks for different combinations of source and target sensors showing average (over all activities) F1 score improvements of between 5% and 13% with the improvement on individual activities, particularly well suited to benefit from the additional information going up to between 20% and 40%.