# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 4 Experiment and Evaluation
### Dataset Construction
#### 4.1.2 Custom Dataset  
Unlike most established research fields, there is a lack of a universal, standard dataset for single-sensor home behavior monitoring using smartwatches. In this study, a custom dataset LIMU was created to obtain richer and more targeted data. The design of the LIMU dataset primarily targets home scenarios, encompassing rest data (sitting, lying down), exercise data (walking, running, jumping rope), and work data (computer office work, window cleaning), covering 14 types of behavior data. These behavior categories possess the capability to conduct detailed analysis of users' daily lives while also estimating individuals' daily health levels based on daily behavior statistics, providing users with corresponding lifestyle adjustment suggestions.  
During the data collection process, this paper simulates a smartwatch using a single IMU. By employing the WIT's WT 901 WIFI wireless IMU, sensor data can be uploaded in real time via WIFI to a UDP server running on a Raspberry Pi, with further processing of the data conducted by the perception platform running on the Raspberry Pi. According to regulations, the IMU is worn on the wrist of the dominant hand, sampling data from the three-axis accelerometer, three-axis gyroscope, and three-axis magnetometer at a sampling rate of 100 Hz.  
During the data collection process, a total of six participants were involved, including five males and one female. The participants were all university students, aged between 22 and 32 years, among whom one participant was left-handed while the others were right-handed. All participants agreed to use the recorded data for scientific research. Each participant chose to perform 14 types of behaviors (sitting, standing, walking, lying down, jumping rope, running, going upstairs, going downstairs, jumping back and forth, waving, working on a computer, opening and closing doors, cleaning windows, and washing hands), resulting in approximately 7100 s of data collected.  
The data collection scenario has a certain degree of closure, which can ensure that the data collection activities are not disturbed by pedestrians and environmental noise, thereby guaranteeing the integrity of the data to a certain extent. Figure 6 shows the correspondence between several typical human activities and waveforms in the LIMU dataset. The red, green, and blue curves depict the accelerometer, gyroscope, and magnetometer values on X-axis, Y-axis, and Z-axis. It can be seen that these activities exhibit different forms in the data from different sensors, and these implicit features can be effectively extracted through deep learning algorithms.  
Table 1 lists the basic information of the subjects involved in data collection. Figure 7 statistically analyzes the distribution of data in LIMU across different types of behaviors and users. From the statistical results, it can be observed that the dataset has a relatively even distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline
**User** & **Gender** & **Age** & **Dominant Hand** \\ \hline User 0 & Male & 24 & Right Hand \\ User 1 & Male & 24 & Right Hand \\ User 2 & Female & 32 & Right Hand \\ User 3 & Male & 23 & Right Hand \\ User 4 & Male & 22 & Right Hand \\ User 5 & Male & 23 & Left Hand \\ \hline \end{tabular}
\end{table}
Table 1: Basic information of subjects.