Test time domain adaptation has come to the forefront as a challenging scenario in recent times. Although single domain test-time adaptation has been well studied and shown impressive performance, this can be limiting when the model is deployed in a dynamic test environment. We explore this continual domain test time adaptation problem here. Specifically, we question if we can translate the effectiveness of single domain adaptation methods to continuous test-time adaptation scenario. We propose to use the given source domain trained model to continually measure the similarity between the feature representations of the consecutive batches. A domain shift is detected when this measure falls below a certain threshold, which we use as a trigger to reset the model back to source and continue test-time adaptation. We demonstrate the effectiveness of our method by performing experiments across datasets, batch sizes and different single domain test-time adaptation baselines. This can have a significant impact in a variety of applications, from healthcare and agriculture to transportation and finance. As a result, this research has the potential to greatly benefit developing countries by providing new tools and techniques for building more effective and efficient machine learning systems.