MotionQ：面向跨观测WiFi手势识别的算子条件运动商

中文摘要 AI 辅助

WiFi手势识别在固定部署中准确，但当用户朝向、可用链路或收发器位置变化时，性能常会下降。与普通域偏移不同，这些变化改变了无线观测算子，因此相同运动预期产生不同测量。现有方法仍追求域不变特征，且很大程度上忽视了变化的布局和观测配置。然而，改变观测算子也会改变哪些任务相关的运动线索在物理上可观测，而不仅仅是改变固定线索集的外观。在WiFi前向过程的局部线性化下，我们推导出一个共同任务可观测性条件，在该条件下，可从每个几何诱导算子中恢复严格的共同线性表示，同时保留手势任务。当该条件不满足时，在额外异构源算子间强制更强对齐可能会丢弃在单个算子下仍可观测的任务相关线索。因此，我们提出MotionQ，为每个候选几何生成一个算子条件的双支撑运动度量。运动商仅去除其未标记支撑的任意排序，并由置换不变的中心矩表示。不是跨算子匹配运动商，而是通过单链路保留干预鼓励每个视图保留足以进行手势识别的信息。广泛评估表明，MotionQ对外推性观测算子具有鲁棒性。

英文摘要

WiFi gesture recognition is accurate in fixed deployments but often degrades when user orientation, available links, or transceiver placement changes. Unlike ordinary domain shifts, these changes alter the wireless observation operator, so the same motion is expected to produce different measurements. Existing methods nevertheless pursue domain-invariant features and largely overlook changing layouts and observation configurations. Yet changing the observation operator also changes which task-relevant motion cues are physically observable, rather than merely altering the appearance of a fixed set of cues. Under a local linearization of the WiFi forward process, we derive a common task-observability condition under which a strict common linear representation is recoverable from every geometry-induced operator while preserving the gesture task. When the condition fails, enforcing stronger alignment across additional heterogeneous source operators may discard task-relevant cues still observable under individual operators. We therefore present MotionQ, which generates an operator-conditioned two-support motion measure for each candidate geometry. A motion quotient removes only the arbitrary ordering of its unlabeled supports and is represented by permutation-invariant central moments. Rather than matching quotients across operators, single-link-retention interventions encourage each view to retain information sufficient for gesture recognition. Extensive evaluations show that MotionQ is robust to extrapolative observation operators.

发表机构

• Tianjin University（天津大学）
• Guizhou Normal University（贵州师范大学）
• University of Science and Technology of China（中国科学技术大学）
• Guangzhou University（广州大学）
• The University of Electro-Communications（电气通信大学）
• Hefei University of Technology（合肥工业大学）

机构由 AI 辅助整理，请以论文原文为准。