Wrist-wearable devices such as smartwatch have gained popularity as they provide quick access to the various information and easy access to multiple applications. Among various applications of the smartwatch, user verification based on the handwriting has been recently investigated. In this paper, we present a novel technique for user verification using a smartwatch based on writing pattern or signing pattern. The proposed technique leverages accelerometer data captured from the smartwatch that are further represented using 2D Continuous Wavelet Transform (CWT) and deep features extracted using the pre-trained ResNet50. The comparison is performed using the ensemble of the classifier. Extensive experiments are carried out on the newly captured dataset using two different smartwatches with three different writing scenarios (or activities). The article provides key insights and analysis of the results in such a verification scenario.