## 1 Introduction  
Water plays a key role in many of the human body's functions, including maintaining normal temperature, getting rid of wastes and preventing kidney damage. Typically, an adult male body contains approximately 59% water, while a female body contains roughly 50% water. Even when sedentary, a healthy adult loses 1.05 to 3.1 L of water every day through breathing, urine, feces, or skin diffusion [1]. Therefore, people need to stay hydrated by eating food and drinking fluids. According to the National Academy of Medicine, sufficient water intake is at least 3.7 L/day for men and 2.7 L/day for women [1]. However, the U.S.A. National Health and Nutrition Examination Survey shows that from 2011 to 2016, around 60% of people did not consume the recommended amount of water [2]. In addition, the Youth Risk Behavior Survey found that 48.7% of senior high school students drank water less than three times a day, and 24.6% of them drank water less than once a day in 2017 [3]. Once the fluid intake is lower than the consumption, it is difficult to metabolize waste. The accumulation of waste in the body increases the burden on the kidneys and even leads to chronic kidney disease [4]. Furthermore, insufficient fluid intake can also cause headaches, dizziness and fatigue [5]. Hence, fluid intake monitoring can help people understand their fluid consumption and remind individuals to hydrate appropriately [6]. The traditional methods of monitoring and assessing water intake are manually filling out questionnaires or using mobile applications to record the number of times and amount of water consumed each day [7]. Nevertheless, the manual methods described above still have disadvantages, such as being time-consuming, making personal errors, and being subjective [8; 9]. With the rapid development of technology, more and more professionals have begun to dive into research on intelligent activity identification of fluid intake. Scholars use different approaches to automatically identify complete episodes of drinking activities. Common sensing devices can be divided into two categories according to their characteristics: ambient-based and wearable-based systems. Ambient-based systems monitor personal drinking behavior through environmental sensing devices, such as cameras [10; 11], smart containers with built-in sensors or wireless communication modules [12; 13; 14; 15; 16]. Wearable-based systems extract characteristics of drinking information by placing sensors on the body. Typical wearable devices include wrist-worn inertial measurement units [17] and microphones placed near the neck [18; 19]. The ambient-based systems mentioned above are easy to use and convenient. Nonetheless, the user must perform the activity within the cramped space when using the camera to capture drinking behavior. This method not only restricts the scope of the monitoring but may also interfere with the privacy of users or other people. Although wireless communication modules can solve privacy issues, they still have problems, such as monitoring space limitations and radiofrequency shielding. The environment does not restrict the smart containers with built-in sensors, but people may forget to use them because they are not used to carrying these devices. Compared with ambient-based devices, wearable-based devices are not affected by the environment. In addition, with the maturity of microelectromechanical systems (MEMS) technology, sensors are gradually miniaturized and integrated into devices such as smart watches, mobile phones, and earphones. Wearable-based approaches have the advantages of being small, lightweight, and mobility-friendly. Therefore, many previous studies [20; 21; 22; 23; 24; 25; 26] have demonstrated the feasibility and effectiveness of wearable devices for fluid intake assessment and monitoring. [PERSON] et al. [21] recorded motion signals and detected drinking activities with the wrist-worn inertial measurement unit (IMU).