# EchoWrist: Continuous Hand Pose Tracking and Hand-Object Interaction Recognition Using Low-Power Active Acoustic Sensing On a Wristband

arXiv: https://arxiv.org/abs/2401.17409 · HTML: https://mlsanity.com/paper/2401.17409/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2024.

## Built on by (most cited first, 8 of 8)

- [On the Utility of 3D Hand Poses for Action Recognition](https://mlsanity.com/paper/2403.09805/) (2024)
- [WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch](https://mlsanity.com/paper/2502.05783/) (2025)
- [SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Tracking](https://mlsanity.com/paper/2406.11645/) (2024)
- [SensPS: Sensing Personal Space Comfortable Distance between Human-Human Using Multimodal Sensors](https://mlsanity.com/paper/2502.07441/) (2025)
- [Visuo-Acoustic Hand Pose and Contact Estimation](https://mlsanity.com/paper/2508.00852/) (2025)
- [Proactive Conversational Assistant for a Procedural Manual Task based on Audio and IMU](https://mlsanity.com/paper/2602.15707/) (2026)
- [RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals](https://mlsanity.com/paper/2608.00289/) (2026)
- [SoniSpeech: A Large-Scale Open-Vocabulary Tri-Modal Dataset for Wearable Silent Speech Interfaces](https://mlsanity.com/paper/2608.00803/) (2026)

## Abstract (arXiv)

Our hands serve as a fundamental means of interaction with the world around us. Therefore, understanding hand poses and interaction context is critical for human-computer interaction. We present EchoWrist, a low-power wristband that continuously estimates 3D hand pose and recognizes hand-object interactions using active acoustic sensing. EchoWrist is equipped with two speakers emitting inaudible sound waves toward the hand. These sound waves interact with the hand and its surroundings through reflections and diffractions, carrying rich information about the hand's shape and the objects it interacts with. The information captured by the two microphones goes through a deep learning inference system that recovers hand poses and identifies various everyday hand activities. Results from the two 12-participant user studies show that EchoWrist is effective and efficient at tracking 3D hand poses and recognizing hand-object interactions. Operating at 57.9mW, EchoWrist is able to continuously reconstruct 20 3D hand joints with MJEDE of 4.81mm and recognize 12 naturalistic hand-object interactions with 97.6% accuracy.