Human activity recognition (HAR) is a rapidly growing field of research that aims to automatically identify and classify human motions and activities from different tracking devices, such as cameras and sensors. One of the most widely used sensor modalities for HAR is the smartphone, which has various sensors, such as gyroscopes, accelerometers, and GPS, that can provide rich information about a person’s movements and actions. HAR applications are essential for the Internet of Things (IoT) and smart home industries. We used the recent advances in deep learning techniques to develop a new HAR model for wearable sensors. The proposed model, MLCNNwav, relies on residual convolutional neural networks and 1-D trainable discrete wavelet transform. The multilevel CNN is designed to capture global features, whereas the wavelet transformation enhances the representation and generalization by learning activity-related features. Several deep learning approaches are compared to assess the superiority of the developed model. Four public benchmarks HAR data sets were used for the evaluation. The outcomes confirmed that the developed MLCNNwav recorded high-accuracy rates on all data sets.