# More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors

arXiv: https://arxiv.org/abs/2202.08267 · HTML: https://mlsanity.com/paper/2202.08267/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [What Makes Multi-modal Learning Better than Single (Provably)](https://mlsanity.com/paper/2106.04538/) (2021)
- [Self-supervised transfer learning of physiological representations from free-living wearable data](https://mlsanity.com/paper/2011.12121/) (2020)
- [TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers](https://mlsanity.com/paper/2003.08474/) (2020)
- [Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images](https://mlsanity.com/paper/1611.08321/) (2016)
- [Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild](https://mlsanity.com/paper/2107.09510/) (2021)

## Built on by (most cited first, 3 of 3)

- [Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition](https://mlsanity.com/paper/2210.01459/) (2022)
- [iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition](https://mlsanity.com/paper/2402.09445/) (2024)
- [Classification of Stress via Ambulatory ECG and GSR Data](https://mlsanity.com/paper/2208.04705/) (2022)

## Abstract (arXiv)

Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common scenario in many applications, but may not always be feasible in real-world scenarios. For example, although combining bio-signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework to improve testing performance with reduced sensors through leveraging the complementary information of multiple modalities during training. More specifically, different sensors may carry different but complementary information, and our model is designed to enforce collaborations among different modalities, where positive knowledge transfer is encouraged and negative knowledge transfer is suppressed, so that better representation is learned for individual modalities. Our experimental results show that our framework achieves comparable performance when compared with the full modalities. Our code and results will be available at https://github.com/compwell-org/More2Less.git.