# Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1610.07031 · HTML: https://mlsanity.com/paper/1610.07031/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2016.

## Built on by (most cited first, 2 of 2)

- [Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data](https://mlsanity.com/paper/2211.01342/) (2022)
- [Video-based Exercise Classification and Activated Muscle Group Prediction with Hybrid X3D-SlowFast Network](https://mlsanity.com/paper/2406.06703/) (2024)

## Abstract (arXiv)

The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.