# Left-Right Swapping and Upper-Lower Limb Pairing for Robust Multi-Wearable Workout Activity Detection

arXiv: https://arxiv.org/abs/2408.03947 · HTML: https://mlsanity.com/paper/2408.03947/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition](https://mlsanity.com/paper/2304.05088/) (2023)
- [Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors](https://mlsanity.com/paper/2007.07172/) (2020)
- [tsflex: flexible time series processing & feature extraction](https://mlsanity.com/paper/2111.12429/) (2021)
- [Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring](https://mlsanity.com/paper/2207.07753/) (2022)

## Built on by (most cited first, 1 of 1)

- [What Egocentric Video Embeddings Encode and How to Combine Them with Inertial Sensors for Human Activity Recognition](https://mlsanity.com/paper/2610.12926/) (2026)

## Abstract (arXiv)

This work presents the solution of the Signal Sleuths team for the 2024 HASCA WEAR challenge. The challenge focuses on detecting 18 workout activities (and the null class) using accelerometer data from 4 wearables - one worn on each limb. Data analysis revealed inconsistencies in wearable orientation within and across participants, leading to exploring novel multi-wearable data augmentation techniques. We investigate three models using a fixed feature set: (i) "raw": using all data as is, (ii) "left-right swapping": augmenting data by swapping left and right limb pairs, and (iii) "upper-lower limb paring": stacking data by using upper-lower limb pair combinations (2 wearables). Our experiments utilize traditional machine learning with multi-window feature extraction and temporal smoothing. Using 3-fold cross-validation, the raw model achieves a macro F1-score of 90.01%, whereas left-right swapping and upper-lower limb paring improve the scores to 91.30% and 91.87% respectively.