## 1 Introduction  
The early childhood years represent a critical period for establishing healthy physical activity patterns that can influence lifelong health outcomes. Physical activity is crucial for children's health and development, affecting their physical growth, cognitive development, and social skills [1]. Furthermore, research has identified physical activity as a significant protective factor against mental health challenges, including depressive symptoms and suicidal behaviour in later developmental stages [2]. Understanding their activity level can provide valuable information for designing public health interventions, school physical activity programmes, and disease prevention strategies [3]. By improving the accuracy of these assessments, we can obtain a clearer and more detailed picture of children's physical activity, which can lead to more effective interventions to promote healthy lifestyles from an early age [4].  
Historical approaches to measuring children's physical activity have relied heavily on subjective methods. Traditionally, studies aimed at measuring children's physical activity were based on questionnaires or parental reports [5]. These methods lack objective measurement capabilities and introduce significant reporting biases, as parents may unconsciously project their behavioural patterns onto their assessments. This limitation has driven the need for more objective measurement tools.  
In response to these methodological challenges, the use of accelerometry has become increasingly prevalent for collecting objective data in physical activity and sport research, both for adults and children. This technology provides continuous, unbiased measurement of movement patterns throughout the day. In the case of children, accelerometry has proven particularly valuable as it offers an objective method to validate questionnaire responses and provide accurate activity measurements [6].  
The data collected from accelerometers enables the application of advanced analytical approaches, particularly Machine Learning (ML) or Deep Learning (DL) algorithms, which offer several key advantages: (1) real-time monitoring of children's physical activity with automated alert systems, (2) personalised evaluation frameworks that account for age and gender differences, (3) identification of sedentary patterns to inform intervention strategies, and (4) detection of complex movement patterns that might be missed by traditional analysis methods [7; 8; 9].  
Significant progress has been made in applying various ML algorithms (such as Random Forest and Support Vector Machine) and DL approaches (including Artificial Neural Networks and ensemble models) to accelerometer data. Several reviews have examined different aspects of this field, specifically focusing on wearable technology for measuring physical activity in children [10; 11]. [PERSON] et at. [12] have contributed valuable insights on physical activity measurement in children with neuromotor disorders, and the broader application of artificial intelligence in children's education has been systematically reviewed [12].  
This review aims to critically evaluate the effectiveness of machine learning applications in improving physical activity measurements in preschool children, specifically focusing on accelerometer-based data collection methods. By synthesising current evidence and identifying key trends, this review seeks to provide a comprehensive understanding of how machine learning can enhance our ability to measure and analyse physical activity patterns in young children.