# A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures

arXiv: https://arxiv.org/abs/2402.05958 · HTML: https://mlsanity.com/paper/2402.05958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2024.

## Abstract (arXiv)

The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial.