## 3 Materials and Methods
### Data Processing and Sensor Configuration  
The raw sensor data were preprocessed to enhance data quality and ensure consistency across measurements. This preprocessing involved noise reduction and normalization to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Abbreviation** & **Activities** & **Description** \\ \hline
1 & run\_wos & Running without using a smartphone & (Running) 1 around the bus stop while not using the smartphones \\ \hline
2 & stn\_wos & Stationary without using a smartphone & (Standing or Sitting) around the bus stop while not using the smartphone \\ \hline
3 & wlk\_wos & Walking without using a smartphone & (Walking at 2–3 steps per seconds) around the bus stop while not using the smartphone \\ \hline
4 & run\_ws & Running using the smartphone & (Running) 1 around the bus stop while using the smartphone \\ \hline
5 & stn\_ws & Stationary using the smartphone & (Standing or Sitting) around the bus stop \\ \hline
6 & wlk\_ws & Walking using the smartphone & (Walking at 2–3 steps per seconds) around the bus stop while using the smartphone \\ \hline \hline \end{tabular} \({}^{\dagger}\) Running speed is assumed to be higher than walking speed [30].  
\end{table} 
Table 1: Details of Experimental Activities: With and Without Smartphone Interaction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Measurement Type** & **Gravitational Effect** \\ \hline Accelerometer & Total acceleration (motion + gravity) & Included \\ \hline Gyroscope & Rotation or angular velocity around & N/ A \\  & x, y, z axes & (measures rotation) \\ \hline Linear Accelerometer & Acceleration due to movement & Excluded \\  & (gravity removed) & \\ \hline Gravity Sensor & Gravitational acceleration only & Only measures gravity \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of Smartphone Sensors and Their Role in Activity Recognition. standardize the data before further analysis. To evaluate the impact of data segmentation strategies on model performance, we divided the data into overlapping windows of 2 s with 50% overlap and non-overlapping windows. This approach allowed for the capture of continuous sequences, which are essential for training deep learning models and provided insights into how different windowing techniques affect performance. To ensure comprehensive analysis, the data were segmented into fixed windows of 2.56 s (128 data points per window). Each window was processed with two different approaches: one set with 50% overlap and another set without overlap. This segmentation strategy aimed to capture temporal patterns effectively and provide a robust comparison of preprocessing methods. Following segmentation, we applied handcrafted feature extraction to each window. This approach allowed us to capture the most relevant aspects of the sensor signals, ensuring that the models could effectively differentiate between various human activities. We extracted a variety of time-domain features commonly used in HAR. The feature set for the two-sensor configuration included 42 features, while the four-sensor configuration yielded 84 features. These features were chosen based on their relevance and effectiveness in distinguishing different activities. To provide a clear representation of the sensor measurements during a running activity, we visualize the collected data for each sensor between 80 and 85 s of the recording. This segment was selected because it captures a momentum phase of the running activity, where consistent sensor readings are observed. Figures 1-4 depict the data from different sensors during this window. The plots provide insight into the raw sensor data for the running activity. For instance, in Figure 1, the accelerometer data shows the rapid fluctuations in acceleration along the x, y, and z axes as the volunteer propels forward during running.