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This paper tackles the challenge of Human Activity Recognition (HAR) in heterogeneous IoT environments where sensor configurations vary significantly across datasets. They propose a channel-free HAR framework that processes each sensor channel independently using a shared encoder, incorporates sensor metadata via conditional batch normalization for late fusion, and employs a joint loss function to optimize both channel-level and fused predictions. Experiments demonstrate the framework's robustness and transferability across diverse HAR datasets, outperforming channel-fixed models.
Channel-free HAR is now possible: a single model can perform activity recognition across diverse IoT sensor setups without needing fixed channel arrangements, thanks to metadata-conditioned fusion.
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...
