Exercise is critical to children's mental and physical health. However, improper exercise can be counterproductive. [1] Research shows that excessive exercise can cause stress fractures, ligamentous injuries, and knee articular cartilage damage in children, while lack of exercise can lead to obesity and even depression. [2] Therefore, it is necessary to provide children with professional coaches and health advisers. However, professional guidance is expensive and impractical in rural or underdeveloped areas where health infrastructure is weak and professional coaches are not available. [3] Recently, the issue became more obvious because the coronavirus pandemic quarantined children from their schools' athletic facilities, leaving them with less professional guidance from their teachers. [4] Fortunately, wearable devices (e.g., smartwatches or smart-bands) can provide motion monitoring and exercise advice to people who do not have access to professional coaches and health advisers at a low cost and with good accessibility. [5,6] However, children are often ignored in the design of these devices. Wearable devices and algorithms developed for children lack diversity and function, forcing them to use commercial adult devices or algorithms to get high-quality remote exercise guidance. [7] Utilizing adult devices or algorithms on children faces two severe problems. First, human physical characteristics show variability in different activities. It is hard to build an age group recognition model robust to multiple activities. Second, due to the physiological differences between children and adults, human activity recognition algorithms developed on adults work poorly on children, making motion monitoring inaccurate. [8] Previous research reports a 10.8%-27.1% recognition decrease when using a model trained by adult data to recognize child activity. [9] Providing exercise advice through wearable fitness devices requires three steps: 1) recognizing the user's current activity; 2) monitoring duration or intensity; and 3) providing advice based on the user's age group characteristics and data collected in step 1 and 2. Therefore, incorrect activity recognition and exercise standard misuse will lead to improper exercise advice and can cause excessive exercise or lack of exercise, which would finally lead to harm for the children. [10] In addition, lots of