| dc.description.abstract | Bluetooth is a very common technology nowadays. It can exchanging data be
tween fixed and mobile devices over short distances. This research used the low
power consumption characteristics of Bluetooth, combined with temperature and hu
midity and air quality sensors, become a thin, light and flexible circuit board, which
can get Bluetooth Signal strength, temperature, humidity, eCO2 and TVOC data.
Through Bluetooth transmission to the receiving device, it can achieve the functions
of real-time monitoring and data analysis.
In this case, a number of Bluetooth sensor were attached to the indoor space. In a
confined space, people will inevitably affect environmental factors such as air quality,
temperature, and humidity in the environment. Therefore, the Bluetooth sensor used
in this study can collect the data generated by these environmental factors, and then
use Google′s public source API-Teachable Machine as the training basis to test
whether the learning results can be combined with the indoor confined space. We will
find that there is a significant relationship between the number of people, and the
number of people in the indoor space and the distribution position of the people can
be discriminated from the strength of the Bluetooth signal and the sensor data.
The value of this research is to use de-identified analysis methods. Because the
data we used all get by sensors, no lens instruments are used, there will be no problem
of infringing on the rights of personal portraits and privacy. | en_US |