Human activity recognition (HAR) in Internet of Things (IoT) environments must cope with heterogeneous sensor settings that vary across datasets, devices, body locations, sensing modalities, and channel compositions. This heterogeneity makes conventional channel-fixed models difficult to reuse across sensing environments because their input representations are tightly coupled to predefined channel structures. To address this problem, we investigate strict channel-free HAR, in which a single shared model performs inference without assuming a fixed number, order, or semantic arrangement of input channels, and without relying on sensor-specific input layers or dataset-specific channel templates. We argue that fusion design is the central issue in this setting. Accordingly, we propose a channel-free HAR framework that combines channel-wise encoding with a shared encoder, metadata-conditioned late fusion via conditional batch normalization, and joint optimization of channel-level and fused predictions through a combination loss. The proposed model processes each channel independently to handle varying channel configurations, while sensor metadata such as body location, modality, and axis help recover structural information that channel-independent processing alone cannot retain. In addition, the joint loss encourages both the discriminability of individual channels and the consistency of the final fused prediction. Experiments on PAMAP2, together with robustness analysis on six HAR datasets, ablation studies, sensitivity analysis, efficiency evaluation, and cross-dataset transfer learning, demonstrate three main findings...

翻译：人体活动识别（HAR）在物联网（IoT）环境中必须应对异构传感器设置——这些设置因数据集、设备、身体位置、感知模态及通道构成而异。这种异构性使传统固定通道模型难以在不同传感环境中复用，因其输入表示与预定义的通道结构紧密耦合。为解决该问题，我们探究严格无通道HAR，即单一共享模型在不预设输入通道数量、顺序或语义排列的情况下执行推理，且不依赖传感器特定输入层或数据集特定通道模板。我们论证融合设计是该场景的核心问题。据此，我们提出无通道HAR框架，该框架结合了通道级编码与共享编码器、通过条件批归一化实现的元数据条件后期融合，以及通过联合损失函数实现的通道级与融合预测的联合优化。所提模型独立处理各通道以适应变化的通道配置，同时利用身体位置、模态和轴向等传感器元数据恢复仅靠通道独立处理无法保留的结构信息。此外，联合损失兼顾个体通道的可判别性与最终融合预测的一致性。在PAMAP2上的实验，连同在六个HAR数据集上的鲁棒性分析、消融研究、灵敏度分析、效率评估及跨数据集迁移学习，展示了三个主要发现……