## 1 Introduction
Other exercise prescription variableswere not considered in this work. [PERSON] and [PERSON] (2020) used an IMU to implement a new type of computer interface operating system that performs hand motion tracking and click gesture recognition algorithms using Euler angles and acceleration data, respectively. [PERSON] et al. (2020) proposed a deep learning framework based on graph convolutional network (GCN) to predict the position of each joint of human lower limbs during motion in 3D space using signals collected from five IMUs (above the ankles of both feet, above the knees of both feet, and at the waist). [PERSON] et al. (2020) proposed a system of IMUs in the lower legs, IMUs in the thighs, and pressure sensors in both feet to measure the kinetics and kinematics of the lower limbs. [PERSON] et al. (2020) used an IMU on each leg and a motion camera on each calf to estimate the asymmetry of stride length and gait, and the root mean square error of stride length was 5.4 cm and asymmetry was 3.0% in healthy subjects, while the root mean square error of stride length was 9.6 cm and asymmetry was 14.7% in subjects with abnormal gait. [PERSON] et al. (2020) proposed a low-cost smart glove equipped with an inertial unit. Their system could recognize 15 sets of training programs, detect three common nonstandard behaviors: case 1-the overall speed of a repetition is too fast or too slow, case 2-the speeds of outward and backward processes are not balanced, and case 3-the repetitions are not stable with noticeable shakes. A segmentation process was used in their system to detect the start point and end point of each repetition with the help of a double adaptive thresholds-based method. Then the half dynamic time warping (Half-DTW), which is not a deep learning approach, was proposed to measure the similarity between the unrecognized activity segmentation data (accelerometer and gyroscope readings) and the activity templates. The Half-DTW requires two steps. First, it applies an outward-backward segmentation operation to divide a repetition into outward and backward parts through locating the turning point (TP, stage changing point). Second, the Half-DTW applies the corresponding matching operation and outputs the identified activity type. Several exercise quality indices were calculated as well: standard degree and execution duration for the assessment of single repetition, and smoothness and continuity for the assessment of group repetitions. The average accuracy using Half-DTW for 15 exercise types is 96.07% with 103.76 ms average time delays. [PERSON] et al. (2020) presented a wearable device using three inertial units with triaxial accelerometers, gyroscopes, and magnetometers to measure the orientation of three sections of the spine for low back pain therapy. By integrating the absolute and relative orientation from the sensors based on the so-called direction cosine matrix (DCM), it estimates the posture of the back in real-time and uses a fuzzy system to control a vibration unit to alert the user to correct the posture of his/her back. From these mentioned literature in the above, most papers focus on recognition algorithms for exercises to improve prediction accuracy. The number of sensors used for collecting data and identifying exercise types is ranging from one to five or even more. Depending on the application purpose, some sensors are attached to lower limbs, and some are attached to arms. However, to the best of the authors' knowledge, very few papers extend the results of the IMU motion tracking to produce helpful exercise prescription variables for fitness lovers. This paper integrates hardware and software to develop an attitude and heading reference system (AHRS) device using only two IMUs (one on the wrist band and the other in the sleeve near the elbow) and an intelligent algorithm that performs motion tracking, posture recognition, and calculates exercise prescription variables for weight training.