Identifying interaction groups using the bluetooth proximity data of the Conflab dataset

Abstract

Detecting social interactions through wireless wearable Bluetooth devices is increasing in popularity. Devices use the signal strength to other detected devices to estimate the proximity between people and group them together based on the Dominant set algorithm. Dominant sets are a maximal clique of nodes with an edge-weight based on the affinity between the nodes. Nevertheless, the signal is heavily influenced by external factors, which increase in an crowded environment. This paper introduces three different noise reduction filters that try to detect the kind of noise and therefore improve the detection of surrounded devices. Further, this paper looks at the overall impact of proximity on the resulting RSSI values. Knowing this relationship helps to normalize the values and therefore eliminates the need to apply noise reduction. Using a dataset of 48 sensors recorded in a conference setting with a specific designed sensor the low frequency pass filter gets an accuracy score of 81.8 % with a cut-off frequency of 0.07 Hz. It performs best when considering a conversation window of 20 seconds. Here, only 2/3 of the detected groups has to coincide with the actual formed group at a specific timestamp. Furthermore, the orientation of the participants to each other has heavy influence on the resulting RSSI values and therefore a normalization based on only proximity cannot be done.