# Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals

arXiv: https://arxiv.org/abs/2407.10874 · HTML: https://mlsanity.com/paper/2407.10874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation](https://mlsanity.com/paper/1911.09272/) (2019)
- [Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training](https://mlsanity.com/paper/1812.06145/) (2018)
- [Surface Electromyography as a Natural Human-Machine Interface: A Review](https://mlsanity.com/paper/2101.04658/) (2021)
- [Simultaneous Estimation of Hand Configurations and Finger Joint Angles using Forearm Ultrasound](https://mlsanity.com/paper/2211.15871/) (2022)
- [Prediction of Metacarpophalangeal joint angles and Classification of Hand configurations based on Ultrasound Imaging of the Forearm](https://mlsanity.com/paper/2109.11093/) (2021)

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

- [Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images](https://mlsanity.com/paper/2502.00275/) (2025)

## Abstract (arXiv)

Biosignal-based hand gesture classification is an important component of effective human-machine interaction. For multimodal biosignal sensing, the modalities often face data loss due to missing channels in the data which can adversely affect the gesture classification performance. To make the classifiers robust to missing channels in the data, this paper proposes using Random Channel Ablation (RChA) during the training process. Ultrasound and force myography (FMG) data were acquired from the forearm for 12 hand gestures over 2 subjects. The resulting multimodal data had 16 total channels, 8 for each modality. The proposed method was applied to convolutional neural network architecture, and compared with baseline, imputation, and oracle methods. Using 5-fold cross-validation for the two subjects, on average, 12.2% and 24.5% improvement was observed for gesture classification with up to 4 and 8 missing channels respectively compared to the baseline. Notably, the proposed method is also robust to an increase in the number of missing channels compared to other methods. These results show the efficacy of using random channel ablation to improve classifier robustness for multimodal and multi-channel biosignal-based hand gesture classification.