# Comparison of ECG Between Gameplay and Seated Rest: Machine Learning-Based Classification
## 4 Discussion

Table 3: HRV index of the seated-rest control group. signals using wavelet transform. These studies demonstrate the feasibility and effectiveness of machine learning-based gameplay and rest classification, highlighting the importance of validating new methods against existing methods. One of the strengths of this study is that the experimental setup was limited to a seated posture, ensuring that participants exhibited minimal physical movement. This likely contributed to the relatively high classification accuracy, as postural consistency reduces motion artifacts and physiological noise that can otherwise complicate HRV analysis. Moreover, previous studies have extensively explored the identification of seated postures using body acceleration data, indicating that when posture is restricted to sitting, machine learning-based classification of gaming versus non-gaming states is feasible [33; 34; 35; 36; 37]. However, this study's approach may not generalize to gaming activities that involve significant body movement, such as virtual reality (VR) gaming or physically interactive games like those requiring motion controllers. In such cases, HRV-based classification may become less reliable due to additional physiological variations induced by movement, highlighting a key limitation of this research. Despite these limitations, our study remains relevant given the growing concern over gaming and smartphone addiction, which are increasingly recognized as modern-day public health issues. Excessive gaming has been linked to poor mental health, disrupted sleep patterns, and diminished cognitive function, making it essential to develop objective methods for identifying and monitoring gaming behavior. Although this study does not directly address addiction classification, it establishes a foundation for using physiological data to differentiate gaming behavior, which could be expanded upon in future research. Integrating HRV analysis with additional physiological and behavioral indicators--such as eye tracking, galvanic skin response, or EEG data--could enhance classification accuracy and provide deeper insights into the autonomic changes associated with excessive gaming. In conclusion, this study demonstrates the potential of HRV-based machine learning classification in distinguishing gaming states, contributing to the relatively unexplored field of gaming behavior analysis. While the study is limited to seated posture and controlled conditions, it provides a stepping stone for future research into real-world applications, where continuous physiological monitoring could be utilized for gaming addiction detection and intervention strategies. Finally, we discuss the future directions of this study. In this study, we demonstrated that machine learning can be used to classify ECG signals and distinguish gameplay individuals to some extent. However, several aspects need to be further investigated to improve the accuracy and applicability of this approach. First, the expansion of the dataset and the diversity of participants. A larger and more diverse participant pool should be incorporated to improve the generalizability of the classification model. Factors such as age, gender, gaming history, and psychological state should be considered to improve classification performance. Second, feature engineering and advanced machine learning models. In this study, we focused primarily on HRV-based classification. However, future research should investigate the analysis of ECG waveforms using pattern-matching techniques and additional physiological markers such as heart-rate acceleration patterns and non-linear HRV metrics. In addition, deep learning approaches such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can be used to extract complex temporal features from ECG signals, and the integration of real-time monitoring and wearable devices will also be necessary.