# CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control

arXiv: https://arxiv.org/abs/2306.04319 · HTML: https://mlsanity.com/paper/2306.04319/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Hand Gesture Controlled Drones: An Open Source Library](https://mlsanity.com/paper/1803.10344/) (2018)

## Built on by (most cited first, 6 of 6)

- [TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines](https://mlsanity.com/paper/2408.14146/) (2024)
- [Initial Findings on Sensor based Open Vocabulary Activity Recognition via Text Embedding Inversion](https://mlsanity.com/paper/2501.07408/) (2025)
- [Unimodal and Multimodal Sensor Fusion for Wearable Activity Recognition](https://mlsanity.com/paper/2404.16005/) (2024)
- [TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition](https://mlsanity.com/paper/2505.02052/) (2025)
- [VersaPants: A Loose-Fitting Textile Capacitive Sensing System for Lower-Body Motion Capture](https://mlsanity.com/paper/2511.16346/) (2025)
- [Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusion](https://mlsanity.com/paper/2602.23694/) (2026)

## Abstract (arXiv)

We present CaptAinGlove, a textile-based, low-power (1.15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control. We employ lightweight convolutional neural networks as the backbone models and a hierarchical multimodal fusion to reduce power consumption and improve accuracy. The system yields an F1-score of 80% for the offline evaluation of nine classes; eight hand gesture commands and null activity. For the RTE, we obtained an F1-score of 67% (one user).