In recent years, numerous studies have employed smartphone accelerometer data to identify human activities, leading to the development of increasingly complex and sophisticated algorithms. These evaluations acknowledge human actions. Commonly included in experimental evaluations are sitting, stair climbing, running, and walking. The topics of bicycling and driving are covered in some research. Activity recognition affects system performance and precision. The algorithm and model selection for human activity recognition during experimental evaluation is crucial. This is possible through the use of Support vector machine, k-nearest neighbours, decision tree, and deep learning algorithms such as CNNs and RNNs (RNNs). The algorithms and models will be determined by the type and complexity of the actions to be recognised and the available computational resources. CNN is used for experimentation in this paper. The accelerometer data are cleaned and transformed prior to evaluation processing. After training the system with pre-processed data, its performance is assessed using accuracy, recall, precision, and F1-score.