Affective Sensing
The first methodology that will be used will be affective sensing. I will build an affective sensing wearable that will be able to detect physiological signals from the user on the walk and combine them with GPS location. This wearable will be based on work done by the MIT Media Lab Affective Sensing group. (need more)
User-input Emotional Index
The second methodology will be a numerical value also collected from the user during the walks. The value will be an index from 1 to 4 corresponding to a positive or negative emotional state, 1 being the most negative and 4 being the most positive. The users will be prompted by the researchers to either type this number on a keypad or speak it out loud whenever they feel that their emotional state has changed as a response to the environment. This particular index was used by Weinreb 2013 and therefore will be useful to compare the two studies and to use as a benchmark measurement for the wearable measurements. Because this index is directly representative of a state perceived by the user and it is provided by the user in real-time, it’s a really good measurement because it has a high chance of being true. Furthermore, as pointed out by Weinreb, because this index would be used spontaneously by the user and not collected at pre-specified locations, “it makes the resulting clusters, whether related to positive or negative feelings, particularly compelling because it maximizes the content validity of the overall measure relative to a technique that would have required us to request a rating at pre-selected points” (Weinreb 2013).
III. Mapping & Visualization
After collecting the two sets of data-points, the next step would be to visualize the data.