Beyond 1 × 1 Convolutions: A Dynamic Select-and-Fuse Channel Sampling Strategy for On-Device Human Activity Recognition

The proliferation of low-cost, portable sensors has made wearable human activity recognition (HAR) a cornerstone for real-time health monitoring and behavior analysis. However, deploying accurate yet lightweight deep learning models on resource-constrained wearable devices poses a significant challenge for on-device activity recognition. While channel pruning is a common solution to accelerate deep Convolutional Neural Networks (CNNs), existing works often require specialized implementations or pre-trained models, which potentially degrade performance by simply removing an entire channel, limiting their ability to handle complex multimodal sensor inputs. Moreover, lightweight CNN design, particularly the heavy use of 1×1 convolution layers for channel squeezing, remain inefficient for sensor-based HAR, which consume resources without expanding the receptive field due to their pointwise nature. To address these issues, we propose a novel dynamic channel sampling module, Select-and-Fuse (SaF), specifically designed for sensor-based HAR. SaF divides channels into subsets and performs a dynamic, input-dependent selection from them, with the picking decision being made per-time-step based on the input sensor signal activations, allowing for fine-grained feature adaptation to multi-modal sensor signals. While integrated into compact backbones, SaF significantly reduces model size and inference latency while maintaining high accuracy. Extensive evaluations on public UCI-HAR, OPPORTUNITY, WISDM, and UniMiB-SHAR benchmarks confirm a favorable performance-cost trade-off. Crucially, we measure actual inference latency on a Raspberry Pi, proving its practicality for resource-constrained HAR applications. Code will be released.