EchoWrist：利用腕带上的低功耗主动声学传感实现连续手部姿态跟踪与手-物交互识别
EchoWrist: Continuous Hand Pose Tracking and Hand-Object Interaction Recognition Using Low-Power Active Acoustic Sensing On a Wristband
AI总结：
本文提出低功耗腕带 EchoWrist，通过双扬声器主动声学传感与深度学习连续重建 3D 手部关节并识别手-物交互，实验显示其低功耗且高精度。
摘要：
我们的双手是与周围世界交互的基本手段。因此，理解手部姿态和交互情境对于人机交互至关重要。我们提出 EchoWrist，这是一种低功耗腕带，利用主动声学传感连续估计 3D 手部姿态并识别手-物交互。EchoWrist 配备两个扬声器，向手部发射听不见的声波。这些声波通过反射和衍射与手部及其周围环境相互作用，携带有关手部形状及其所交互物体的丰富信息。两个麦克风捕获的信息经过深度学习推理系统，该系统恢复手部姿态并识别各种日常手部活动。两项各有 12 名参与者的用户研究结果表明，EchoWrist 在跟踪 3D 手部姿态和识别手-物交互方面既有效又高效。在 57.9mW 功耗下运行时，EchoWrist 能够连续重建 20 个 3D 手部关节，MJ EDE 为 4.81mm，并以 97.6% 的准确率识别 12 种自然手-物交互。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
