## 3 Methodology
### Data Acquisition  
The dataset utilized in this research, referred to as the Myogym dataset [29], was derived from a sample group of 10 individuals participating in 30 unique gym exercises, each performed ten times, as detailed in Table 1. An additional NULL class denotes periods when no activity was undertaken. The Myogym collection represents an exhaustive assemblage of raw sensor readings captured by the Myo armband during exercise sessions conducted in a gymnasium setting. This dataset encompasses measurements from IMU and EMG. The dimensions of the data are as follows:  
* The information obtained from the IMUs encompasses measurements from a three-dimensional accelerometer and a three-dimensional gyroscope. Together, these form a feature domain spanning six dimensions in total. Every data point in this collection represents the instantaneous values for acceleration and angular velocity. These values were recorded at a sampling frequency of 50 measurements per second. Figure 2 illustrates a subset of IMU time-series recordings obtained from the multi-sensor wearable dataset. Panel (a) showcases readings from the triaxial accelerometer, while panel (b) presents corresponding gyroscope rotation signals. * EMG data: The EMG data were acquired through an array of eight distinct electromyography sensors, generating an eight-dimensional feature space. Each channel recorded the electrical impulses the underlying muscular tissues produced throughout the gymnasium-based exercise routines. The electromyographic signals were acquired at a sampling rate of 50 measurements per second, synchronized with the data collected from the inertial measurement units. As stated by [PERSON] et al. [30], the Myo armband employed a WEMG-8 commercial electromyography sensor manufactured by Lavtha Co., Ltd. (Daejeon, Republic of Korea). This sensor was equipped with a wireless transmitter operating at a frequency of 2.4 GHz and incorporated an analog bandpass filter within the electromyography electrode unit itself. The frequency range of this filter spanned from 13 Hz to 430 Hz. The Myogym dataset's sampling frequency for collecting electromyographic data was set at 50 Hz. Figure 3 depicts a set of EMG time-series streams recorded from arm muscles during various resistance training sessions. The Myogym dataset is a comprehensive compilation of multi-modal time-series data incorporating inertial and EMG sensors to capture information throughout gym sessions. The rationale behind employing a multi-sensor, multi-subject dataset is  
Figure 1: The proposed framework of S-HAR for gym exercise activity recognition. to facilitate the advancement and evaluation of algorithms for activity recognition and sensor fusion capable of autonomously identifying and classifying typical strength training and cardiovascular exercises. Figure 2: Some samples of IMU data from the Myogym dataset: (**a**) accelerometer data; (**b**) gyroscope data. Figure 3: Some samples of EMG data from the Myogym dataset: (**a**) seated cable rows; (**b**) one-arm dumbbell row; (**c**) bench press; (**d**) pushups. The unprocessed sensor data are complemented by appropriate activity labels, which indicate the precise physical exercise done at each time interval in the gym. The Myogym dataset provides a comprehensive and complex description of sensor information, allowing for the creation and assessment of machine learning models for the recognition of gym exercises. The choice of this dataset was driven by its importance and widespread adoption in the realm of HAR using wearable sensors. It has been utilized in numerous studies as an ideal benchmark for comparing different methodologies and assessing the achieved accuracy levels across various implementations. The designers of the dataset study meticulously supervised the data collection process to ensure data quality and movement consistency. Consequently, this dataset is well-suited for conducting thorough analyses of its various activities. It also proves effective in identifying symmetric movements.