发表机构
Northwestern University（西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EITWatch是首个基于智能手表表背的腕部EIT系统，采用31毫米环形8电极平面阵列，通过多深度扫描提升手势识别性能，在12名参与者的宏/微手势识别中取得较高准确率，且具备一定跨会话和跨用户迁移能力。
AI 中文摘要
腕部电阻抗断层扫描（Electrical Impedance Tomography，EIT）可通过肌肉和肌腱驱动的阻抗变化感知手势，但现有腕部EIT系统所需的电极覆盖范围超出了手表背面接触区域，且需要独立的模拟前端。本文提出EITWatch，这是首个基于智能手表表背几何结构构建的腕部EIT系统，旨在探究仅依靠该接触区域是否可支持手势识别：在31毫米环形区域内设置8个平面电极，以48赫兹的频率采集35个阻抗测量值。由于平面阵列无法环绕手腕，EITWatch采用多深度扫描以采样多个源极-汇极距离和电流路径；在全部12名参与者中，其宏平均/微平均表现相较于匹配相邻注入方式分别提升了15.1/10.4个百分点。在提示式研究中，针对6种宏手势，会话内留一轮交叉验证的窗口级/试次级准确率达91.4%/92.5%，针对5种微手势加放松状态，窗口级/片段级准确率达90.1%/91.5%；窗口级跨会话及留一用户迁移准确率，宏手势为73.2%/70.4%，微手势为63.1%/55.3%。
英文摘要
Wrist Electrical Impedance Tomography (EIT) senses hand gestures from muscle- and tendon-driven impedance changes, but prior wrist-EIT systems require electrode coverage beyond the watch-back contact patch and separate analog front ends. We present EITWatch, the first wrist-EIT system built around smartwatch case-back geometry, asking whether this contact patch alone can support gesture recognition: eight planar electrodes in a 31 mm ring acquire 35 impedance measurements at 48 Hz. Because a planar array cannot encircle the wrist, EITWatch uses multi-depth scanning to sample multiple source-sink distances and current paths; it beat matched adjacent injection by 15.1/10.4 percentage points (macro/micro) across all 12 participants. In a prompted study, within-session leave-one-round-out accuracy reached 91.4%/92.5% (window/trial) for six macro-gestures, and 90.1%/91.5% (window/segment) for five micro-gestures plus relax; window-level cross-session and leave-one-user-out transfer reached 73.2%/70.4% and 63.1%/55.3% (macro/micro).
Comments7 pages, 6 figures, 2 tables. Accepted to UIST '26. Code: https://github.com/XuanyouLiu/EITWatch_Hardware
