Place Sensing Workshop

I participated in a workshop in Singapore with 12 other Georgia Tech HCI students, Industrial Design students from Taiwan, and students from NUS in Singapore. The workshop was about creating a wearable to sense a place.

There exist a variety of waiting spaces in Singapore, such as waiting at bus stops, on escalators, in lifts, and train stations. Our focus in this workshop was narrowed down to spaces that are ideal for waiting for others in busy places. This situation requires an open space to be seen, but also a comfortable environment to wait as our users may or may not know how long they will have to be there.



Our concept of spaces to wait for someone in high traffic areas includes many characteristics to sense, both for the human and the environment. Additionally, comfort is very subjective and required some means of having a variable to compare to. We operationalized comfort to several environmental variables such as ambient temperature, proximity to other people, and audio levels. We intended to compare these quantitative environment attributes to the qualitative perception of comfort that users recorded during the testing.


We considered various wearable forms to design and build, and chose to focus on a hat prototype so that the sensors could be high enough to capture crowd proximity. We used the Adafruit Feather microcontroller due to its thin profile and light weight. A real time clock was stacked on top to record time data. The ultrasonic sensor was placed at the front of a hat and managed to capture up to 20 meters. An accelerometer on the top of the hat was used to collect qualitative data about the waiting space. This is done through nodding for “yes, this is a good place to wait” or tilting the head to the side for “no, this is not a good place to wait.” We originally wanted to use a head shake for “no”, but the data from the accelerometer was not accurate enough.

prototype testing2

On the brim is a microphone that picks up noise levels. Finally, a temperature sensor was placed next to the ultrasonic. All the sensors are integrated into a felt cap fitted to the interior of the hat. It also acts as a barrier to protect the head and was removable to adjust electronics as needed.



After debugging and testing the wearable for any issues, the the prototype was deployed in Singapore shopping centers and train stations to collect and record attributes of the environment.

Our hypothesis was that lower temperature, sparse human density, and constant ambient audio would in fact present the comfortable spaces to wait for another person. Once the data was collected, we compared and analyzed our results with the qualitative user responses.

Our data visualization showed unique results between the shopping area and the train station. We found that overall proximity to others changed between the two spaces. Shopping environments provided more comfortable room to wait, while train stations showed to have more human density, with more frequent close contact traffic.



We concluded that while temperature and noise were useful attributes to define a comfortable space to wait, proximity to other people was the strongest variable. Some future thoughts with this project include a mobile app to use on the go, further prototype refinement to include more ultrasonic sensors, and collection of other variables such as human stress, safety, and abrupt change in environment.