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Motion or inertial sensors such as gyroscope and accelerometer commonly found in smartwatches and smartphones can measure characteristics such as acceleration and angular velocity of movements in the human body and use them to learn models capable of identifying human activities, that has applicability in various fields such as biometrics, remote patient health monitoring, etc. Recently deep learning-based methods have become popular for human activity recognition because they use representation learning techniques that can automatically generate optimal features from raw input data generated from sensors without any human intervention and can identify hidden patterns in data. This work proposes a novel hybrid deep neural network model, CNN-GRU that combines convolutional and gated recurrent units for human activity recognition. This model was successfully validated on WISDM dataset and produced accuracy that is suggestively better than other state-of-the-art deep neural network models such as Inception Time and DeepConvLSTM created using AutoML.