# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 2 Related Works  
Various classification algorithms, such as logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and XGBoost, have been used to classify activities using features extracted from HAR datasets. For example, the authors of [26] used manual feature extraction with the LR, SVM, and KNN classifiers and reached the maximum accuracy of 83.9%, 88.9%, and 95.3%, respectively, while recognizing seven activities of daily living (ADL). In [15; 27], the KNN algorithm was declared the optimal method for classifying sensor-based HAR features due to its high accuracy and low statistical error rate. However, like other models constructed using the traditional approach, it was unable to effectively distinguish similar activities. In recent works that employ manual feature engineering, the XGBoost classifier [11; 28; 29; 30] and classifier stacking [31; 32] are gaining popularity due to their high efficacy. With the proliferation and development of neural networks, deep learning (DL) methods began supplanting traditional methods for addressing HAR problems. In [17; 18; 19; 20], the authors used CNN for sensor-based HAR activity classification tasks and obtained superior results compared to state-of-the-art models built using the traditional approach. The authors of [19] compared the performance of 1D and 2D sequential CNN models for HAR signal classification, concluding that 2D CNNs produce better results, outperforming traditionally constructed models. In [33], the authors used various DL models, including CNNs, recurrent neural networks (RNNs), and long short-term memory (LSTM), to classify activities based on smartphone accelerometer signals and determined that LSTM is the model with the best overall performance. With the proliferation of HAR problems, various DL models that were constructed specifically for HAR classification tasks emerged. In [34], the authors proposed the InnoHAR model based on the combination of an Inception neural network (INN) and an RNN. The iSPILnception [35] was inspired by Google's Inception-ResNet architecture and achieved high predictive accuracy while requiring fewer device resources to address signal-based HAR problems. In the most recent research, hybrid models based on LSTM and bi-directional LSTM (BiLSTM) [36; 37; 38; 39] are gaining popularity for human activity classification due to their ability to effectively extract spatial and temporal characteristics. The main challenges faced by DL-based models are the need for extensive training datasets, intra-class variations and similarity issues, and imbalanced training datasets. Thus, training DL models on small datasets typically results in overfitting or underfitting problems that result in poor performance on new data. Transfer learning (TL) is a promising solution to the problems mentioned. Currently, several papers propose pre-trained models specifically for the sensor-based HAR classification problem. The authors of [40] proposed a knowledge transfer approach called SA-GAN, which employs the generative adversarial network (GAN) to perform cross-subject TL. This method outperformed other state-of-the-art works in 66% of experiments; in the other 25%, it came in second. Another approach was used in [41]. The authors measured the data distribution distance between the source and target subjects using the Wasserstein distance metric and then employed the DeepTransHHAR model for feature extraction and activity recognition. The authors of [42] present a stratified TL framework for source domain selection and activity transfer based on stratified distance and capture of the local attribute of the domains. In the HAR domain, there are methods besides TL for mitigating the overfitting and underfitting issues. In [43; 44], the authors used self-supervised learning techniques that utilize unlabeled data collected from wearable sensors, which resulted in an auspicious increase in classification performance.