Children’s fine motor skills are linked not only to drawing ability but also to cognitive, social-emotional, self-regulatory, and academic development Suggate et al. Journal of Research in Reading, 41(1), 1–19 (2018), Benedetti et al. (2014), Liew et al. Early Education & Development, 22(4), 549–573 (2011), Liew (2012) and Xie et al. (2014). Current educators are assessing children’s fine motor skills by either determining their shape drawing correctness Meisels et al. (1997) or measuring their drawing time duration Kochanska et al. (1997) and Liew et al. (2011) through paper-based assessments. However, these methods involve human experts manually analyzing children’s fine motor skills, which can be time consuming and prone to human error or bias Kim et al. (2013) and Lotz et al. (2005). With many children using sketch-based applications on mobile devices like smartphones or tablets Anthony et al. (2012), computer-based fine motor skill assessment has the potential to address limitations of paper-based assessment by using automated measurements. In this work, we introduce a machine learning approach for analyzing aspects of children’s fine motor skill development. We performed a study with 60 young children (aged 3 to 8 years old), and we implemented classifiers that determine children’s age category based on features related to fine motor skill, predominantly for curvature- and corner-based drawing skills, surpassing the performance of our previous work Kim et al. (2013) and of human evaluators. We also present dedicated discussion and statistical testing of sketch recognition features which will further enhance automated fine motor assessment.

Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machine-learning algorithm was able to classify all activities at .935 of an F-measure. Finally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% ac- curacy in real-time on a mobile device.

Accidental drowning stands as the third leading cause of global mortality, according to the World Health Organization. While suggestions like increased supervision and physical barriers aim to reduce drowning risk, their effectiveness is hindered by the widespread accessibility of water bodies. In contrast, swimming education has demonstrated a remarkable potential to decrease the risk of drowning by up to 90%. However, research on technology-supported swimming education is limited, mainly focusing on drowning detection and support for competitive swimmers. This paper addresses this gap by presenting outcomes from a user study, particularly focusing on a vulnerable demographic: children without basic swimming skills in survival-oriented beginner courses. Data from 45 participants aged 4 to 13 were collected through wearable sensors and cameras. The study’s results offer valuable insights into integrating technology into swimming education for non-swimming children, contributing to the advancement of drowning prevention strategies.