发表机构
Hasso Plattner Institute, University of Potsdam（哈索·普拉特纳研究所，波茨坦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究使用Jev通用模型在三个数据集上评估无训练HAR，发现其性能远低于监督模型，更多特征反而降低识别，表明传感器到模型接口应纳入模型评估。
AI 中文摘要
通用模型有望在不训练任务特定分类器的情况下实现基于传感器的决策，这可能减少人类活动识别（HAR）对标注数据的依赖。然而，这类模型能否直接解释物理传感器信号的确定性描述，以替代或补充经过训练的HAR模型，仍不清楚。我们使用Jev（一个固定的通用概率决策模型）在来自WISDM、UCI341和PAMAP2的1,800个类别平衡的加速度计窗口上研究此问题。Jev不接收标注示例、检索上下文或HAR特定参数更新。我们评估了三种确定性传感器表示，并将5,400次Jev决策与生成式基线和三个监督HAR模型进行比较。Jev仍远低于监督识别水平，其最强表示在三个数据集上的宏F1分别为0.038、0.118和0.089，而监督模型为0.686至0.907。更多数值特征并未改善Jev；相反，它们在所有三个数据集上降低了识别性能，而将相同的数值证据与确定性语义渲染相结合可部分恢复性能，尽管该实验未将语义与伴随的序列化和冗余变化隔离。Jev查询快速且成本低，但其概率未可靠校准用于识别。事后融合分析在WISDM上发现小幅改进，但该改进未在UCI341或PAMAP2上复现。这些结果表明，无训练传感器决策不仅取决于信号中可用的信息，还取决于模型能否利用暴露该信息的表示。因此，传感器到模型的接口应被视为模型评估的一部分，而非中立的预处理步骤。
英文摘要
General-purpose models promise sensor-based decisions without training a task-specific classifier, which could reduce the dependence of Human Activity Recognition (HAR) on labeled data. Yet it remains unclear whether such models can directly interpret deterministic descriptions of physical sensor signals well enough to replace or complement trained HAR models. We study this question using Jev, a fixed general-purpose probabilistic decision model, on 1,800 class-balanced accelerometer windows from WISDM, UCI341, and PAMAP2. Jev receives no labeled examples, retrieval context, or HAR-specific parameter updates. We evaluate three deterministic sensor representations and compare 5,400 Jev decisions with a generative baseline and three supervised HAR models. Jev remains far below supervised recognition, with its strongest representation reaching macro-F1 of 0.038, 0.118, and 0.089 across the three datasets, compared with 0.686 to 0.907 for the supervised models. More numerical features do not improve Jev. Instead, they reduce recognition on all three datasets, while augmenting the same numerical evidence with a deterministic semantic rendering partially recovers performance, although the experiment does not isolate semantics from the accompanying serialization and redundancy changes. Jev is fast and inexpensive to query, but its probabilities are not reliably calibrated for recognition. A post-hoc fusion analysis finds a small improvement on WISDM that does not replicate on UCI341 or PAMAP2. These results show that training-free sensor decisions depend not only on the information available in the signal, but also on whether the model can use the representation through which that information is exposed. The sensor-to-model interface should therefore be treated as part of the model evaluation rather than as a neutral preprocessing step.
Comments15 pages, 4 figures, 8 tables
