# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 3 TestBed Materials and Machine Learning Model  
### System Model of the Proposed Testbed  
This section gives a brief and concise description of the materials used in the testbed. The major objective of this research as already stated is to develop a dataset comprising vibration, breathing, movement, and location depicting human activities in a smart factory shop floor. These data can assist in identifying the need for an emergency for the health need of a shop floor worker. For instance, the ultra-wide band (UWB) sensor is capable of giving readings of normal and abnormal breathing status of a person within its recognition range (typical values range from 2.5 m to 8 m placement of the sensor on a roof surface from the floor) [49].  
If there are more than one person at the floor, the UWB sensor gives out a motion or movement value ranging from 100 to 4800, depending on the number of persons and type of movement. In such a case, the UWB gives zero (0) breathing value. Moreover, the UWB sensor gives breathing values ranging from 6 to 40 only when there is one worker at the floor. The values represent respiration rate (breaths per minute). So, to detect emergency, this dataset considers the situation of an isolated factory floor worker in need of attention. His/her breathing rate is captured by the UWB sensor. His/her exact location is captured by the LIDAR, while the vibration sensor detects the vibration when a human(worker) falls or when it is an object that falls. The system model was made up of three prominent sensors, which were used for the collection of data. These sensors include: G-link vibration, RP-LIDAR A1, and ultra-wide band (UWB) sensor. Thereafter, the collected data was used as input to various implemented ML algorithms. To create the smart factory environment for data collection, we leveraged on the facilities of Daegu Mechatronics and Materials Institute in Korea, as shown in Figure 1. The dataset is available on demand via the corresponding author.  
The breathing and movement data were collected in three situations. The first situation is in cases where two or more persons were located at the smart factory shop floor indicated by movement readings of the UWB. The second is in situations where only one person is at the smart factory floor and without a case for any emergency attention indicated by normal breathing value. And the third is where a fallen person(worker) seems not to have breathing value in the acceptable range. The vibration sensor was used to collect dataset for cases of falling person, fallen heavy objects, and cases of no vibration at all, thus suggesting a zero need for attention. On the other hand, the RP-LIDAR gave the location distance and angle of the persons in the smart factory floor. All these data were collected and saved in the host computer prior to processing detection and classification, as shown in the system model in Figure 1. The dataset collected is of binary nature having labels of \"normal and abnormal human activities\" judged by the movement, breathing, LIDAR, and vibration values obtained from the factory floor.