Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors

Understand

Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various human activity recognition (HAR) datasets.

• However, deep CNNs often require more computing resources, which limits their applications in embedded HAR.
• Although many successful methods have been proposed to reduce memory and FLOPs of CNNs, they often involve special network architectures designed for visual tasks, which are not suitable for deep HAR tasks with time series sensor signals, due to remarkable discrepancy.
• Therefore, it is necessary to develop lightweight deep models to perform HAR.