# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
###### Abstract  
Over the last few years, human activity recognition (HAR) has drawn increasing interest from the scientific community. This attention is mainly attributable to the proliferation of wearable sensors and the expanding role of HAR in such fields as healthcare, sports, and human activity monitoring. Convolutional neural networks (CNN) are becoming a popular approach for addressing HAR problems. However, this method requires extensive training datasets to perform adequately on new data. This paper proposes a novel deep learning model pre-trained on scalograms generated using the continuous wavelet transform (CWT). Nine popular CNN architectures and different CWT configurations were considered to select the best performing combination, resulting in the training and evaluation of more than 300 deep learning models. On the source KU-HAR dataset, the selected model achieved classification accuracy and an F1 score of 97.48% and 97.52%, respectively, which outperformed contemporary state-of-the-art works where this dataset was employed. On the target UCI-HAPT dataset, the proposed model resulted in a maximum accuracy and F1-score increase of 0.21% and 0.33%, respectively, on the whole UCI-HAPT dataset and of 2.82% and 2.89%, respectively, on the UCI-HAPT subset. It was concluded that the usage of the proposed model, particularly with frozen layers, results in improved performance, faster training, and smoother gradient descent on small HAR datasets. However, the use of the pre-trained model on sufficiently large datasets may lead to negative transfer and accuracy degradation. biomedical signal processing; human activity recognition; convolutional neural networks; continuous wavelet transform; transfer learning  
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to the construction of sensor-based HAR models. The traditional feature-based approach implies the consequent execution of the following steps: signal preprocessing and noise removal (1), manual feature extraction and feature selection (2), and the use of machine learning (ML) algorithms to perform activity classification (3) [9; 10]. This approach has shown promising results in many publications [11; 12; 13; 14; 15]; however, it has certain drawbacks. Firstly, statistical signal characteristics (i.e., shallow features) are frequently insufficient to recognize complex, multi-step activities and transient states. Second, it necessitates a high level of expertise from the researchers and an individualized approach to each dataset. Moreover, extracting compelling features becomes exceedingly complicated as the number of sensors increases. Convolutional neural networks (CNN) have established the most recent state-of-the-art in speech and image recognition [16]. Nonetheless, CNN has found application in HAR problems as a powerful feature extraction mechanism and classifier. Due to the ability of CNN to automatically extract and select features, they can acquire high-level signal characteristics and often produce better results than models built using the traditional approach [17; 18; 19; 20]. However, the peculiarity of this method is that it requires large training datasets to produce adequate results on new data; otherwise, it is prone to underfitting and overfitting problems. Several methods are available to mitigate this problem, including data augmentation and regularization [21; 22]. Transfer learning (TL) is another promising technique in which a model trained on a more extensive and general source dataset is then fine-tuned on a target dataset [23]. Currently, there are numerous pre-trained models available for visual object recognition [24; 25]. However, the goal of this research is the development of a pre-trained deep CNN model specifically for the HAR classification problem.