An Incremental Denoising Convolutional Autoencoder for Adaptive One-Dimensional Biosignal Reconstruction in Wearable Devices

Deep learning has significantly enhanced one-dimensional biosignal processing in wearable devices by enabling accurate and intelligent signal analysis. Wearable devices operate using optimized model parameters obtained through offline training. However, physiological variations among users necessitate periodic model updates to maintain performance. Conventional retraining requires storing both existing and newly acquired datasets, leading to increased memory consumption and computational overhead, which limits its deployment in resource-constrained wearable devices. To address this challenge, this work proposes an incremental learning framework based on an incremental denoising convolutional autoencoder (I-D-CAE) that incrementally updates the model using newly available data while preserving previously learned knowledge, thereby avoiding the need to retain the complete historical dataset. The proposed framework adapts to newly available data while retaining previously learned model parameters and maintaining a fixed model-parameter size. Experimental results demonstrate that the proposed method achieves improved reconstruction accuracy and reduced reconstruction loss, demonstrating its potential for adaptive biosignal processing in resource-constrained wearable devices.