可穿戴IMU职业活动识别研究：LOSO评估下个体准确率差异达27个百分点
Towards Field-Deployable Wearable IMU Systems for Occupational Activity Recognition
如果你在做可穿戴传感器或活动识别，这篇把 LOSO 评估的水分挤得很实在：同样模型有人 88.7% 有人 61.9%，还给出了什么时候该用个性化校准、什么时候别碰 CTGAN 的实用结论。
一项基于 VTT-ConIoT 基准的研究指出，建筑工人人体活动识别（HAR）常报告的 95% 以上总准确率，掩盖了真实部署中的个体差异。在 LOSO（留一受试者）协议下，六种分类器、五种传感器位置、三种窗口大小的系统测试显示，多传感器 16 类配置的单人准确率从 61.9% 到 88.7%，均值 77.5%，差距达 27 个百分点。多传感器融合和更长窗口均能提升准确率，标准时序增强带来小幅稳定收益，而 CTGAN 表格增强反而损害准确率。个性化校准只用 weakest 参与者 30% 的自身数据，就能为其挽回 16.8 个百分点。
Towards Field-Deployable Wearable IMU Systems for Occupational Activity Recognition
Wearable inertial measurement unit (IMU) systems for construction worker human activity recognition (HAR) routinely report aggregate accuracies above 95 %, rarely under genuine subject-independent (leave-one-subject-out (LOSO)) evaluation, masking the per-individual variance that determines real-world deployment performance. Using the VTT-ConIoT benchmark, we conduct, to our knowledge, the first systematic configuration sensitivity analysis of this dataset: six classifiers across five sensor placements and three window sizes under a single LOSO protocol, a personalised-calibration pilot, and a validated comparison of tabular GAN-based and standard time-series augmentation. Per-subject LOSO accuracy for the main multi-sensor, 16-class configuration ranges from 61.9 % to 88.7 % (mean 77.5 %), a 27 pp spread, though all data are laboratory-collected. Multi-sensor fusion and longer windows both improve accuracy; standard augmentation gives small, consistent gains, while Conditional Tabular GAN (CTGAN) augmentation actively harms accuracy with a demonstrated distributional-fidelity mechanism; and personalised calibration recovers 16.8 pp for the weakest participants from just 30 % of their own data. These findings provide evidence-grounded configuration guidance for scaling occupational body sensor networks towards field deployment
