解决可穿戴设备多传感器独立朝向偏移问题，实现真正旋转不变的动作识别。
Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shifts
- 将加速度计与陀螺仪数据转为三轴向量，使用共享的旋转等变主干网络处理每位置数据
- 在四个基准上对独立朝向旋转保持宏平均F1值稳定，优于依赖旋转增强的基线方法
- 无需额外校准或假设参考系，适合家庭康复、健身监测等自装设备场景
基于自管理员可穿戴设备的人体动作识别（HAR）常需在不同会话间重新佩戴惯性测量单元（IMU）。在多IMU设置中，这会导致各身体部位产生独立的朝向偏移，而传统标量型HAR模型无法结构化应对。现有方法依赖旋转增强，其鲁棒性受限于采样变换；或需额外参考帧假设与显式校准流程。本文提出真正旋转不变的HAR框架（TRI-HAR），将加速度与角速度流重塑为三轴向量，对每个IMU位置应用共享的SO(3)-等变主干与不变投影，并融合不变特征进行分类。在四个多IMU基准上，TRI-HAR在固定独立位置的SO(3)旋转下维持宏观F1值，且无需旋转增强即超越旋转增强基线。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) with self-administered wearables, such as at-home rehabilitation and exercise monitoring, often requires reattaching inertial measurement units (IMUs) across sessions. In multi-IMU settings, this can induce independent orientation offsets across body locations, a deployment shift that conventional scalar HAR models do not structurally handle. Existing remedies rely on rotation augmentation, whose robustness depends on sampled transformations, or calibration and orientationnormalization pipelines requiring additional reference-frame assumptions or explicit procedures. We present Truly Rotation-Invariant HAR (TRI-HAR), a rotation-invariant framework that makes robustness to independent per-location IMU orientation offsets a structural model property. TRI-HAR reshapes accelerometer and gyroscope streams into triaxial vectors, applies a shared SO(3)-equivariant backbone and invariant projection to each IMU location, and fuses the resulting invariant features for activity classification. Across four multi-IMU benchmarks, TRI-HAR preserves macro-F1 under fixed independent per-location SO(3) rotations and outperforms rotation-augmented baselines under this target shift without requiring rotational augmentation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。