# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 3 Data Collection and Preparation  
In order to evaluate the proposed HAR classification architectures in Sections 4 and 5, we select the datasets whose sensor data are from IMUs attached to various locations of the user body. Thereafter, we preprocess the sensor data by scaling and segmentation. The details are explained as follows. The sensor data are from the following three wearable-sensor datasets:  
* PAMAP2: The PAMAP2 dataset [36] contains the sensor data collected from nine subjects who performed 18 physical activities. However, here, only 12 activities are considered: lying, sitting, standing, ironing, vacuum cleaning, descending stairs, walking, Nordic walking, cycling, ascending stairs, running, and rope jumping. Three IMUs were attached to a wrist, the chest, and an ankle of each subject. Each IMU was equipped with two triaxial accelerometers, one triaxial gyroscope, and one triaxial magnetometer. As a result, 12 types of sensor data (\(M=12\)) were obtained from each IMU. The sampling rate was set to 100 Hz. * DaLiAc: The DaLiAc dataset [37] contains the sensor data collected from 19 subjects who performed 13 physical activities: sitting, lying, standing, washing dishes, vacuuming, sweeping, walking, ascending stairs, descending stairs, treadmill running (8.3 km/h), bicycling on ergometer (50 Watt), bicycling on ergometer (100 Watt), and rope jumping. A total of four IMUs were attached to the right hip, the right wrist, the chest, and the left ankle. Each IMU was equipped with one triaxial accelerometer and one triaxial gyroscope. As a result, six types of sensor data (\(M=6\)) were obtained from each IMU. The sampling rate was set to approximately 200 Hz. * DSAD: The DSAD dataset [38] contains the sensor data collected from eight subjects who performed 19 physical activities: sitting, standing, lying on back, lying on right side, ascending stairs, descending stairs, standing in an elevator still, moving around in an elevator, walking in a parking lot, walking on a treadmill with a speed of 4 km/h \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Ref.** & **Modality** & **Dataset** & **Classification** & **Fusion Mechanism** \\ \hline
2021 & [29] & EEG & BCI IV 2a, HGD & Motor imagery tasks & SE mechanism \\
2022 & [34] & EEG, EOG & MASS-SS3 & Sleep staging & Multimodal SE mechanism \\
2022 & [35] & RGB videos, & ETRI-Activity3D & Elderly activities & Expansion SE mechanism \\  & & skeleton sequences & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of related SE fusion. in flat, walking on a treadmill with a speed of 4 km/h at 15 degree inclined positions), running on a treadmill with a speed of 8 km/h, exercising on a stepper, exercising on a cross trainer, cycling on an exercise bike in horizontal position, cycling on an exercise bike in vertical positions, rowing, jumping, and playing basketball. Five IMUs were attached to the torso, right arm, left arm, right leg, and left leg. Each IMU was equipped with one triaxial accelerometer, one triaxial gyroscope, and one triaxial magnetometer. As a result, nine types of sensor data (\(M=9\)) were obtained from each IMU. The sampling rate was set to 25 Hz. The sensor data used to predict the current activity are obtained from different sensor types and varied within different ranges. It is a common step to apply the data scaling such that the values of these sensor data will be within the same range. In this work, the standardization method is applied to transform the sensor data such that their mean and standard deviation are zero and one, respectively. Let \(z_{t,m}^{(n)}\) be the sensor value at the \(t\)-th point obtained from the \(m\)-th sensor data of the \(n\)-th IMU. Its standardized value is obtained from:  
\[z_{t,m}^{(n)}=\frac{z_{t,m}^{(n)}-\mu_{m}^{(n)}}{\sigma_{m}^{(n)}}, \tag{1}\]  
where \(\mu_{m}^{(n)}\) and \(\sigma_{m}^{(n)}\) are the mean and standard deviation, respectively, of the values from the \(m\)-th sensor data of the \(n\)-th IMU.