The classification of human motions into their corresponding activity classes is an integral challenge for Human Activity Recognition (HAR) since the data is available in multiple modalities; hence, a single framework or approach is not enough to handle this. In this paper, we have applied Continuous Wavelet Transformation (CWT) to convert the raw time-series sensor data into 3D matrices of n-channels analogous to multi-channel images. These matrices are fed into our proposed hybrid model named HybridHAR-Net, which comprises a two-dimensional Convolutional Neural Network (CNN) architecture and a multi-layered Gated Recurrent Unit (GRU) network. The CNN architecture of HybridHAR-Net extracts structural features from the n-channel inputs while the layered GRU network processes the sequential features, producing a better HAR evaluation result. The proposed HybridHAR-Net model is evaluated on two publicly available benchmark datasets- WISDM and UCI-HAR and achieved classification accuracies of 97.43% and 97.15%, respectively. The experiment results show that our proposed method of utilizing CWT on raw sensor data and applying the proposed HybridHAR-Net model for predicting daily-life human activities outperforms recent multiple HAR methods to which our method is compared.