Deep Learning (DL) has proved effective in a variety of application domains. However, the computational and memory demand posed by deep neural models limits the spread of DL on resource-constrained devices such as microcontrollers. An opportunity to tailor DL models to specific hardware constraints is given by Neural Architecture Search (NAS), which automatically traverses a large search space, seeking for optimal architectures both in terms of hardware and performance, based on user specifications. State of the art open-source NAS tools for microcontrollers only support 2D Convolutional Neural Network (CNN) and Multi Layer Perceptron (MLP), but do not consider 1D convolution, which is key for time series analysis and signal processing. This study focuses on enhancing the state-of-the-art μNAS framework, by adding support for 1D CNN. Preliminary tests on a dummy dataset consisting of simple gaussian-distributed waveforms, demonstrate the system ability to find appropriate architectures to satisfy the specified constraints.