A common obstacle encountered when measuring manipulative motor skills is inaccuracy in recording results, which leads to repeated data collection. This results in ineffective and inconsistent measurements. Advances in sensor and IoT technology have created opportunities for more accurate, efficient, and objective early detection of manipulative skills. Therefore, this study aims to design a sensor- and IoT-based early detection system for manipulative motor skills to improve the effectiveness and efficiency of data collection, resulting in effective and accurate detection. This research and development involved nine experts: three motor experts, three measurement test experts, and three technology experts. Fifty children aged 6-10 years participated in a field trial using a purposive sampling method. After data collection, the Pearson correlation coefficient was analyzed. The results of the study indicate that the analytical tool produced is classified as high across all aspects, and the test-retest reliability test yielded a regression (r = 0.930; P < 0.05), indicating significant performance and a high correlation coefficient. The study's findings indicate that this sensor-based and Internet of Things-based measuring tool can collect more comprehensive motor skill data than manual techniques. This integrated system reduces errors and allows for faster and more precise measurements. Furthermore, this technology is expected to provide tangible benefits for educators and coaches in monitoring children's motor skill development in a more measurable, methodical, and sustainable manner. Therefore, this discovery could be a useful way to improve the standards of motor skill teaching and assessment.
Komaini et al. (Sun,) studied this question.