RADBEACON X4 SPECS MANUAL
RADBEACON X4 SPECS BLUETOOTH

Using the RSSI of Bluetooth Classic radios and a 2-layer machine learning model, they classified the proximity between two devices into three interaction zones, based on the theory of Proxemics : public, social and personal. Presented a real-time system for recognizing social interactions in real-world scenarios. Note that even though the evaluation of this work is focused on the social behavior of an existing group that is interacting, Protractor could also be used to detect stationary interactions within crowds by using the estimated proximity and relative orientation between participants. The building phases of the collaborative task). Using a supervised machine learning approach based on Random Forest, they achieved 84.9% accuracy when detecting the individual’s task role ( i.e. the verbal role of each participant) and 93.2% accuracy when identifying the task timeline ( i.e. The device was evaluated in a group-collaborative task where 64 participants split into groups of four were asked to collaborate with each other to build a construction made of spaghetti and plastic tape. That uses near-infrared light to monitor the user proximity with a mean error of 2.3 – 4.9 cm, and relative body-orientation with a 6° error 95% of the time. created a wearable device named Protractor Well suited to real-life analysis of crowd behaviour and influence strategies. Using mobile phone sensors alreadyĬarried by the majority of people in a crowd makes our approach particularly Group formation that exist within the crowd. Utilizing a community detection based method, we further detected the various Predicts both 1:1 and group interactions with 77.8 We built a machine learning system based on gradient-boosted trees that Participants, where we asked them to socialize with each other for 45 minutes. To inform the development of such system, we conducted a study with 24 Mobile sensing data such as Bluetooth Smart (BLE), accelerometer and gyroscope. In this work, we present a system forĭetecting stationary social interactions inside crowds, leveraging multi-modal Mobile phones offer easier means for data collection that is easy to analyzeĪnd can preserve the user's privacy.

RADBEACON X4 SPECS MANUAL

Often performed using a variety of methods including ethnographic studies,Ĭomputer vision techniques and manual annotation-based data analysis.

Currently, detecting social interactions is Interested in their behavior in crowded situations, but also of interest to Remember these types of interactions is not only beneficial for individuals Remembering our day-to-day social interactions is challenging even if youĪren't a blue memory challenged fish.

Radbeacon x4 specs