# Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing

arXiv: https://arxiv.org/abs/1712.00216 · HTML: https://mlsanity.com/paper/1712.00216/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [DeltaDorsal: Enhancing Hand Pose Estimation with Dorsal Features in Egocentric Views](https://mlsanity.com/paper/2601.15516/) (2026)
- [Study and Survey on Gesture Recognition Systems](https://mlsanity.com/paper/2312.00392/) (2023)

## Abstract (arXiv)

In this paper, we propose a micro hand gesture recognition system and methods using ultrasonic active sensing. This system uses micro dynamic hand gestures for recognition to achieve human-computer interaction (HCI). The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition by machine learning. We adopt lower frequency (300 kHz) ultrasonic active sensing to obtain high resolution range-Doppler image features. Using high quality sequential range-Doppler features, we propose a state-transition-based hidden Markov model for gesture recognition. This method achieves a recognition accuracy of nearly 90% by using symbolized range-Doppler features and significantly reduces the computational complexity and power consumption. Furthermore, to achieve higher classification accuracy, we utilize an end-to-end neural network model and obtain a recognition accuracy of 96.32%. In addition to offline analysis, a real-time prototype is released to verify our method's potential for application in the real world.