Abstract not found

Knowledge of how people interact is important in many disciplines, e.g. organizational behavior, social network analysis, information diffusion and knowledge management applications. We are developing methods to automatically and unobtrusively learn the social network structures that arise within human groups based on wearable sensors. At present researchers mainly have to rely on questionnaires, surveys or diaries in order to obtain data on physical interactions between people. In this paper, we show how sensor measurements from the sociometer can be used to build computational models of group interactions. We present results on how we can learn the structure of faceto-face interactions within groups, detect when members are in face-to-face proximity and also when they are having a conversation.

Mobile telephones, company ID badges, and similar common devices form a sensor network which can be used to map human activity, and especially human interactions. The most informative sensor data seem to be measurements of personto-person proximity, and statistics of vocalization and body movement measurements. Using this data to model individual behavior as a stochastic process allows prediction of future activity, with the greatest predictive power obtained by modeling the interactions between individual processes. Experiments show that between 40 % and 95 % of the variance in human behavior may be explained by such models.

Automated reasoning about human behavior is a central goal of artificial intelligence. In order to engage and intervene in a meaningful way, an intelligent system must be able to understand what humans are doing, their goals and intentions. Furthermore, as social animals, people’s interactions with each other underlie many aspects of their lives: how they learn, how they work, how they play and how they affect the broader community. Understanding people’s interactions and their social networks will play an important role in designing technology and applications that are “socially-aware”. This paper introduces some of the current approaches in activity recognition which use a variety of different sensors to collect data about users ’ activities, and probabilistic models and relational information that are used to transform the raw sensor data into higher-level descriptions of people’s behaviors and interactions. The end result of these methods is a richly structured dataset describing people’s daily patterns of activities and their evolving social networks. The potential applications of such datasets include mapping patterns of information-flow within an organization, predicting the spread of disease within a community, monitoring the health and activity-levels of elderly patients as well as healthy adults, and allowing “smart environments ” to respond proactively to the needs and intentions of their users. 1

In this paper, we describe the use of the sociometer, a wearable sensor package, for measuring face-to-face interactions between people. We develop methods for learning the structure and dynamics of human communication networks. Knowledge of how people interact is important in many disciplines, e.g. organizational behavior, social network analysis and knowledge management applications such as expert finding. At present researchers mainly have to rely on questionnaires, surveys or diaries in order to obtain data on physical interactions between people. In this paper, we show how noisy sensor measurements from the sociometer can be used to build computational models of group interactions. Using statistical pattern recognition techniques such as dynamic Bayesian network models we can automatically learn the underlying structure of the network and also analyze the dynamics of individual and group interactions. We present preliminary results on how we can learn the structure of face-to-face interactions within a group, detect when members are in face-to-face proximity and also when they are having a conversation. We also measure the duration and frequency of interactions between people and the participation level of each individual in a conversation.

The human dynamics group at the MIT Media Laboratory proposes that active pattern analysis of face-to-face interactions within the workplace can radically improve the functioning of the organization. There are several different types of information inherent in such conversations: interaction features, participants, context, and content. By aggregating this information, high-potential collaborations and expertise within the organization can be identified, and information efficiently distributed. Examples of using wearable machine perception to characterize face-to-face interactions and using the results to initiate productive connections are described, and privacy concerns are addressed.

Knowledge of how groups of people interact is important in many disciplines, e.g. organizational behavior, social network analysis, knowledge management and ubiquitous computing. Existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained, or have been forced to rely on questionnaires, or diaries to get data on face-to-face interactions. Survey-based methods are error prone and impractical to scale up. This paper describes our work in developing a computational framework to model face-to-face interactions within a community. We have integrated methods from speech processing and machine learning to demonstrate that it is possible to extract information about people's patterns of communication, without imposing any restriction on the user's interactions or environment. Furthermore, we analyze some of the conversational dynamics and present results that demonstrate distinctive and consistent turn-taking styles for individuals during conversations. Finally, we present results that show strong correlation between a person's turn-taking style during one-on-one conversations and the person's role within the network.

management

Knowledge of how groups of people interact is important in many disciplines, e.g. organizational behavior, social network analysis, knowledge management and ubiquitous computing. Existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained, or have been forced to rely on questionnaires, or diaries to get data on face-to-face interactions. Surveybased methods are error prone and impractical to scale up. This paper describes our work in developing a computational framework to model face-to-face interactions within a community. We have integrated methods from speech processing and machine learning to demonstrate that it is possible to extract information about people’s patterns of communication, without imposing any restriction on the user’s interactions or environment. Furthermore, we analyze some of the conversational dynamics and present results that demonstrate distinctive and consistent turntaking styles for individuals during conversations. Finally, we present results that show strong correlation between a person’s turn-taking style during one-on-one conversations and the person’s role within the network. Author Keywords Social network analysis, organization behavior, machine learning

In this paper we present an approach to analyzing the social behaviors that occur in a typical office space. We describe a system consisting of over 200 motion sensors connected in a wireless network observing a medium-sized office space populated with almost 100 people for a period of almost a year. We use a tracklet graph representation of the data in the sensor network, which allows us to efficiently evaluate gross patterns of office-wide social behavior of its occupants during expected seasonal changes in the workforce as well as unexpected social events that affect the entire population of the space. We present our experiments with a method based on Kullback-Leibler metric applied to the office activity modelled as a Markov process. Using this approach we detect gross deviations of short term office-wide behavior patterns from previous long-term patterns spanning various time intervals. We compare detected deviations to the company calendar and find and provide some quantitative analysis of the relative impact of those disruptions across a range of temporal scales. We also present a favorable comparison to results achieved by applying the same analysis to email logs.