# Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
## 1 Introduction  
Physical exercise is important for the human immune system and can help to reduce a person's risk of infection. It may also prevent bacterial infections of the lungs that result in diseases such as cold, flu, and others [1, 2]. Exercise is also one contribution in clinical implications that could help exercise as a diagnostic tool-non/invasive technique. For example, human data show that during each exercise bout, transient immune changes occurred that over time may improve immunosuppressive against pathogens, thereby reducing upper respiratory tract infection [3, 4]. In addition, [PERSON] has shown that exercise training enhances the human body, cardiovascular and metabolic outcomes in people with metabolic syndrome [5]. Some experts also pointed out that high physical exercise was associated with favorable results for most health-related quality of life scale scores, including disability, frailty, and exhaustion, in dialysis patients [6]. Dumbbells, which are popular indoor sports equipment, are widely used for resistance training to increase the strength of the skeletal muscular system; it improves bone density, enhances lean mass, and increases metabolism [7, 8]. Currently, the conventional dumbbell exercise equipment has unrecordable training information, and as it involves repetitive steps, creates a feeling of boredom. Therefore, the device has been unhelpful to people who need accurate control of exercises and want to manage their health data. [PERSON] et al. developed an intelligent dumbbell [9] that can monitor fitness activity; however, the device only obtains the raw acceleration signal from MPU6050 with no filter to denoise unwanted signals. The human interface machine recorded three-axis acceleration data and providedinformation about the angle to an observer on the Mobile Application (APP). Catching up with the trend, Xiaomi has successfully developed Smart Dumbbells and commercialized the market. However, this product only guides people to practice according to the preset program available in the application. Smart dumbbells are created by combining a traditional dumbbell and an Inertial Measurement Unit (IMU) sensor, which consists of gyroscopes and accelerometers, enabling translational movements and tracking of rotation. This sensor is an improvement in technology for developing microelectromechanical systems (MEMS) with low power and cost that can be applied to many smart tracking devices in gloves, tennis or golf. The initial idea to use an acceleration sensor to track free-weight exercises was proposed by [PERSON] and [PERSON] [10]. They used the naive Bayes and hidden Markov model methods to identify nine modes of exercise; yet, their results have a problem with imposing similar acceleration responses and the effect reaches approximately 90%. [PERSON] et al. used the same acceleration signal to identify and resolve this problem faced by previous research using a combination of a fifth order Butterworth filter to remove noise [11]. Artificial neural networks (ANNs), support vector machines (SVMs), statistical dynamic time warping (SDTW), and improved dynamic time warping (IDTW) methods were applied to compare their performances. The recognition accuracy of the ANN is not acceptable, but the IDTW method has a high efficiency of approximately 96%, which is better compared to 77% for SVMs and 81% for SDTW. In recent years, fusion sensors with artificial intelligence (AI) have contributed significantly to tracking and classifying modes of exercise [12; 13; 14]. However, there are various issues in accurately determining features from raw data of the fusion sensor: all of these methods require big data and need a lot of computer memory. So far, the identification accuracy using deep learning was 99.96% [12]; however, no real-time testing has been conducted in this research study. In addition, the fusion sensor contains a lot of noise; thus, we need to have an excellent denoising filter. This has resulted in many studies revolving around filter development.