Devising and evaluating wearable technology for social dynamics monitoring

The importance of studying social interactions has been proven useful in several fields. In the workplace, studies have found that allowing mixing among different groups could improve team coordination and productivity. Architectural studies have analysed how physical spaces can potentially increase unplanned interactions. Other areas such as epidemiology have also benefited from tracking face-to-face contacts to study the spread of disease. Although technology has progressed significantly, the automated and accurate measurement of human interactions with mobile devices is still lagging. The main shortcomings have to do with accuracy of the captured data and with the communication modalities considered. Additionally, non-verbal behaviours during social interactions (e.g. body posture, orientation and interaction distance) have been often neglected, with a few exceptions, even if traditional sociology has highlighted their importance. In this dissertation we address these challenges by developing two wearable research platforms to monitor different dimensions of social interactions. First, we study the extent to which Bluetooth Low Energy could detect proximity in indoor environments. We analyse all the relevant protocol parameters and measure their impact on power consumption, on custom as well as on commercial devices. We assess its accuracy with a 4-week long deployment illustrating its sustainability for social dynamics studies. With the contacts and mobility data collected during the deployment we study the relationship between social contacts and space design, focusing on a modern architectural concept, Activity-Based Working (ABW). We uncover several patterns and we show how they could be the result of the correct adoption of ABW principles. However, we also discover that the employees might not have fully embraced the ABW concepts entirely, leading to mismatches between principles and actual space usage. Given the importance of studying non-verbal behaviour during social contact we then devise a novel wearable device that, by exploiting near-infrared signals, is able to capture accurate information about distance and angle of interaction between people. We show how we design the device to be robust to ambient light changes and short occlusions by leveraging inertial measurement units. With extensive testing we evaluate its accuracy and robustness. We then explore its potential to study creative processes by deploying it to capture non-verbal cues during a creative task. We show how data about the relative orientation between people and their interpersonal distance could be used to predict the role they have during the interaction and the status of the task. The platforms developed and the insights drawn in this dissertation provide evidence to support the use of wearable technologies to monitor social interactions at an unprecedented level

Multimodal analysis of human behavior

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Towards multimodal analysis of human behavior in crowded mingling scenarios using movement cues from wearable sensors and cameras

Mingling events are scenarios where people come together to socialize, which present a high concentration of social interactions in what are called free-standing conversations groups [1]. This makes them interesting study cases for analyzing social human behavior in a natural and uncontrolled setting. Examples of such events are drink parties, networking events or conference poster sessions (see Figure 1 for examples)

Towards multimodal analysis of human behavior in crowded mingling scenarios using movement cues from wearable sensors and cameras

Mingling events are scenarios where people come together to socialize, which present a high concentration of social interactions in what are called free-standing conversations groups [1]. This makes them interesting study cases for analyzing social human behavior in a natural and uncontrolled setting. Examples of such events are drink parties, networking events or conference poster sessions (see Figure 1 for examples)