Our goal is to lend transparency to which time/location may be triggering their anxiety, as well as patterns that they can be better prepared for. The device will take pulse/heart rate, as well as noise, temperature, humidity, brightness, and heat index into account.
- Quantified Self component: User could potentially make informative decisions in his/her everyday life in order to mitigate stress or anxiety related to specific location. As a result he/she can be less stressed or anxious.
- Smart City component: Scaling the project among NYC users can possibly help city administration to identify areas and patterns that negatively contributes overall emotional health of the city. It can provide an evidence for introducing new solutions and policies to make city and its residents’ interactions much more pleasant.
In “Listening To Your Heart: How Interoception Shapes Emotion Experience and Intuitive Decision Making,” the authors write:
"Theories proposing that how one thinks and feels is influenced by feedback from the body remain controversial. A central but untested prediction of many of these proposals is that how well individuals can perceive subtle bodily changes (interoception) determines the strength of the relationship between bodily reactions and cognitive-affective processing. (Dunn et al. 1835-1844))"
By better understanding our environment, it seems we can make better decisions. If each citizen better understands how they react to their city, the healthier a city’s residents’ decisions might be.
“Noise interferes in complex task performance, modifies social behaviour and causes annoyance. “ ("British Medical Bulletin")"
The British Medical Bulletin cites a study done that researches the effects that noise has on social behavior and decision making. We decided to take this urban stimulus, along with factors that can act as powerful triggers in an urban environment like brightness, temperature, and humidity, to see which ones might have an effect on heart rate.
The core concept of our project should be affective computing, it is a popular area with increasingly attention and has been researched around the world for several years. Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. Detecting emotional information begins with passive sensors which capture data about the user's physical state or behavior without interpreting the input. The data gathered is analogous to the cues humans use to perceive emotions in others. The emotional information we focused in the project should be skin condition and pulse/heart rate, also the environmental factors will be considered in the implementation. As for some prior works of the affective computing, there are typical examples. The Cognition and Affect Project (University of Birmingham), Blue Eyes (IBM) and Affect Sensitive Human-Robot Collaboration (Vanderbilt University), these prior projects are all focused on affective computing and extract crucial information to support the emotional management and decision making.
Instrument Deployment and Device Setup
- Arduino Uno and bread board
- Electret Microphone Breakout (for environmental noise)
- Humidity and Temperature Sensor
- Photoresistor (for ambient light levels)
- Pulse Sensor (for heartbeat rate)
- Adafruit Ultimate GPS Logger Shield (for location and microSD storage)
The diagram for the sensors system of Pulse Map:
Data Collection and Visulization
2. The Contrast between BPM and Other Factors
1.A commuter wears the device to determine how transportation affects their anxiety. After collecting data, they now know which subway lines to avoid or target.
We like this one because it speaks to the individual’s use within a larger urban context. This is very close to what we had in mind when we originally conceived on the project.
2. An advertiser wants to advertise a relaxing vacation package. They use the device to know which spots in the city are the most stressed out.
This was more interesting to us. We had not yet considered the commercial appeal of a device like our’s, but after seeing this user persona it seems obvious. It would be interesting to see how we could organize the data to most useful for an API or for advertisers.
- Dunn, B. D., H. C. Galton, R. Morgan, D. Evans, C. Oliver, M. Meyer, R. Cusack, A. D.
- "British Medical Bulletin." Noise Pollution: Non-auditory Effects on Health. N.p., n.d. Web. 28