# Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data +
## 2 Background Literature  
Human Activity Recognition (HAR) is a field within Artificial Intelligence and Machine Learning that focuses on the automatic detection and classification of human activities based on data collected from various methods, including both sensor-based and skeleton-based approaches [1; 38]. Sensor-based HAR typically involves accelerometers, gyroscopes, or other wearable devices to capture movement and orientation data. At the same time, skeleton-based HAR uses data derived from visual sensors such as RGB cameras or depth sensors, which track the positions of skeleton joints over time. HAR has significant applications in diverse areas, including healthcare, where it can monitor patient activities and detect falls, sports science for performance analysis, and smart home environments to enhance automation and security [4; 11]. Understanding and recognizing human activities is crucial for developing systems that provide personalized feedback, improve safety, and support independent living. Traditional approaches to HAR often involve classical Machine Learning techniques such as k-Nearest Neighbors (k-NN) [47; 48], Support Vector Machines (SVM), and decision trees [29; 49]. These methods rely heavily on manual feature extraction and selection processes, where domain experts identify relevant features from the raw sensor data. While these approaches have been effective to some extent, they face challenges, such as the labor-intensive nature of feature engineering and the difficulty in capturing complex patterns in the data. Additionally, these models may struggle to scale and generalize across different datasets and environments due to their reliance on handcrafted features [38]. The advancement towards deep learning has significantly improved HAR by enabling models to learn features from the raw data automatically [11]. Convolutional Neural Networks (CNNs) have been used to capture spatial dependencies in the data [50], while Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are adept at modeling temporal dependencies [51]. These deep learning models have shown superior performance compared to traditional methods, as they can handle large volumes of data and extract hierarchical features that represent complex patterns in human activities. Similarly, attention-based models have revolutionized the fields of Computer Vision (CV) and Natural Language Processing (NLP) by introducing the transformer architecture that relies on self-attention mechanisms [46]. Unlike traditional sequence models, transformers can capture long-range dependencies and complex patterns in data without the need for recurrent connections. This capability has led to the development of state-of-the-art models in NLP, such as BERT and GPT-3 [52; 53], and in Computer Vision, such as Vision Transformers (ViTs) [54]. The scalability and efficiency of transformers make them promising candidates for applications in time-series data like HAR, where capturing temporal dependencies is crucial. On the other hand, Graph Neural Networks (GNNs), first proposed by [PERSON] et al. in 2005 [33] and later developed by [PERSON] et al. in 2009 [32], have also shown great promise when it comes to activity recognition tasks. For skeleton data, typically represented as sequences of joints or key-points in a graph structure, GNNs are a natural fit due to their ability to capture complex relationships between body joints over time. Their usability was shown in one of the initial works of [PERSON] et al. [55], who implemented GNNs on top of skeleton sequences and proposed a model they named the Spatial-Temporal Graph Convolutional Neural Network (ST-GCN). GNNs can model both spatial and temporal dependencies by treating each joint as a node and the connections between them as edges, allowing for more precise activity recognition. Studies have demonstrated the effectiveness of GNNs in extracting both local and global body movement patterns, which are essential for recognizing complex human activities.