# Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data +
Footnote †: This article is a novel and expanded version of a paper entitled “Applying mixup for Time Series in Transformer-Based Human Activity Recognition”, which will be presented at The 5 th International Workshop on AI for Social Good in the Connected World at The 23 rd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Bangkok, Thailand, 9-12 December 2024. (CNNs) have been effective in capturing spatial features from sensor data, while Long Short-Term Memory (LSTM) networks model temporal dependencies [23; 24; 25; 26]. Transformer models, known for their self-attention mechanisms, have also been applied to handle long-range temporal dependencies in time-series data [27; 28]. Hybrid models combining CNNs and LSTMs have shown improved performance by leveraging both spatial and temporal features [24; 26; 29]. Despite their outstanding performance in an experimental environment, the real-world environment still poses a problem, mainly because of poor data distribution [22]. In skeleton-based HAR, Graph Neural Networks (GNNs) have gained prominence due to their ability to model the human body's skeletal structure as a graph. By representing human body joints as nodes and the connections between them as edges, GNNs can effectively capture both spatial and temporal dependencies in skeleton data, making them well-suited for HAR tasks. Moreover, GNNs can handle the non-Euclidean structure of skeleton data, which provides a significant advantage over traditional methods that assume grid-like data structures [30; 31; 32; 33; 34]. Models like the Spatial-Temporal Graph Convolutional Network (ST-GCN) capture spatial relationships between joints and temporal dynamics across frames [35]. Adaptive Graph Convolutional Networks (AGCNs) and attention mechanisms have further enhanced the capacity to focus on relevant joints and motions [36]. Despite these advancements, the models often require large, diverse datasets to perform well and may not generalize effectively to new subjects or environments due to data scarcity and variability [37]. In spite of the amount of research done in the area of Human Activity Recognition, several challenges in the field need to be addressed, such as lack of data [38], variability of data measuring hardware and techniques [39], and misalignment of activities [40; 41]. While sensors generate vast amounts of data, labeled and diverse datasets for HAR remain limited. On top of that, human motions are complex and varied, making accurate recognition challenging. In this research, we want to address the issue of HAR data scarcity and low diversity to provide a potential solution. This issue has been addressed by [PERSON] et al. [42], who discussed the issue of data scarcity in abnormal behavior detection in crowds and tried to address the problem by using generative adversarial networks to augment the dataset with artificial samples. On the other hand, [PERSON] et al. [43] proposed using semi-supervised Machine Learning to use unlabeled data alongside labeled datasets to improve activity recognition with smartphone inertial sensors. However, a significant challenge in HAR is the scarcity and limited diversity of available datasets. Collecting large-scale, annotated HAR datasets is resource-intensive due to the need for specialized equipment, privacy considerations, and the inherent variability of human movements. Traditional data augmentation techniques used in image processing do not directly apply to time-series and skeleton data, leaving a gap in effectively enhancing HAR datasets. In this research, we explore the use of mixup and cutout data augmentation [44; 45] alongside attention-based architecture, which was first proposed by [PERSON] et al. [46], and Graph Neural Networks, which have gained popularity in the HAR space [34], in order to tackle the problem of low diversity and potential scarcity when training activity recognition models.