发表机构
South China Normal University（华南师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对单IMU活动识别的部署负担问题，提出动态影响加权（DIW）知识蒸馏方法，利用训练阶段的多IMU信息提升单IMU学生模型性能，在WEAR数据集上取得显著效果。
AI 中文摘要
位于身体多个部位的惯性传感器可提升活动识别性能，但推理阶段需使用所有传感器会增加部署负担。本研究探讨训练阶段可用的4个同步IMU是否能提升仅使用右臂IMU进行拟合与推理的学生模型性能。冻结的四IMU教师模型提供logit（对数几率）和特征目标。固定权重知识蒸馏对每个拟合样本的各目标赋予相同权重强度，然而学生模型从中获得的收益可能并不均等。我们提出动态影响加权（DIW）方法，该方法会在内部训练参与者的单独折叠上测试一步候选更新，随后为logit和特征损失分配单独的样本级门控。在WEAR数据集上，我们采用受试者不相交的五折交叉验证，评估22名受试者的19个标签及68298个完整窗口。监督学习的聚合折外出宏观F1值为0.561820，固定权重KD为0.571623，DIW达到0.638451，分别提升7.66和6.68个百分点。DIW在19个标签中的18个、22名保留受试者中的21个上优于监督学习。三种方法在推理阶段均保留相同的80915参数右臂学生模型。在此协议下，DIW将仅用于训练的多位置信息转化为更强的单IMU模型，且不改变部署的传感设备或学生前向图。
英文摘要
Inertial sensors at multiple body locations can improve activity recognition, but requiring every sensor at inference increases the deployment burden. We study whether four synchronized IMUs available during training can improve a student that uses only the right-arm IMU during fitting and inference. A frozen four-IMU teacher provides logit and feature targets. Fixed-weight knowledge distillation applies each target with the same strength to every fitting sample, although the student may not benefit equally from them. We introduce dynamic influence weighting (DIW), which tests a one-step candidate update on separate fold-internal training participants. DIW then assigns separate sample-wise gates to the logit and feature losses. On WEAR, we evaluate 19 labels and 68,298 complete windows from 22 participants using subject-disjoint five-fold cross-validation. Pooled out-of-fold macro-F1 is 0.561820 for Supervised and 0.571623 for Fixed-weight KD. DIW reaches 0.638451, gains of 7.66 and 6.68 percentage points, respectively. It exceeds Supervised for 18 of 19 labels and 21 of 22 held-out participants. All three routes retain the same 80,915-parameter right-arm student at inference. Under this protocol, DIW converts training-only multi-position information into a stronger single-IMU model without changing deployed sensing or the student forward graph.
Comments12 pages, 3 figures, 2 tables
