Daniel Olguin

Wearable sensors for pervasive healthcare management

We show that it is possible to identify individual personality traits and measure group performance in a Post-anesthesia Care Unit (PACU) using wearable sensors.We instrumented a group of 67 nurses working in the PACU of a Boston area hospital with sociometric badges capable of measuring physical activity, speech activity, face-to-face interaction, and physical proximity. Using the data collected with these sensors we were able to estimate the daily average length of stay (LOS) and number of delays.

Productivity Through Coffee Breaks: Changing Social Networks by Changing Break Structure

In this paper we present a two-phase study undertaken to experimentally study in a real world setting the effects of social group strength and how to increase the strength of groups in the workplace. In the first phase of our study we measured interactions between workers at the call center of a large bank based in the United States using Sociometric Badges. We confirmed our hypothesis that the strength of an individual’s social group was positively related to productivity (average call handle time) for the employees that we studied. In the second phase of our study we show that by giving employees breaks at the same time we increased the strength of an individual’s social groups, demonstrating that low-cost management decisions can be used to act on these results.

Organizational Engineering Using Sociometric Badges

We show how a wearable computing research platform for measuring and analyzing human behavior can be used to understand social systems. Using a wearable sociometric badge capable of automatically measuring the amount of face-to-face interaction, physical proximity to other people, and relative location, we are able to construct a dynamic view of an organizations social network by viewing interactions as links between actors. Combining this with email data, where e-mail exchanges indicate a social tie, we are able to form a robust view of the social network, using proximity information to remove spurious e-mail exchanges. We attempt to use on-body sensors in large groups of people for extended periods of time in naturalistic settings for the purpose of identifying, measuring, and quantifying social interactions, information flow, and organizational dynamics. We discuss how this system can lead to an automatic intervention system that could optimize the social network in real time by facilitating the addition and removal of links based on objective metrics in a socially natural way. We deployed this research platform in a group of 22 employees working in a real organization over a period of one month, and we found that betweenness in the combined social network had a high negative correlation of r = −0.49 (p

Sensible Organizations: Changing Our Businesses and Work Styles through Sensor Data

We introduce the concept of sensor-based applications for the daily business settings of organizations and their individual workers. Wearable sensor devices were developed and deployed in a real organization, a bank, for a month in order to study the effectiveness and potential of using sensors at the organizational level. It was found that patterns of physical interaction changed dynamically while e-mail is more stable from day to day. Different patterns of behavior between people in different rooms and teams (p < 0.01), as well as correlations between communication and a workers subjective productivity, were also identified. By analyzing a fluctuation of network parameters, i.e., “betweenness centrality, ” it was also found that communication patterns of people are different: some people tend to communicate with the same people in regular frequency (which is hypothesized as a typical pattern of throughput-oriented jobs) while some others drastically changed their communication day by day (which is hypothesized as a pattern of creative jobs). Based on these hypotheses, a reorganization, such that people having similar characteristics work together, was proposed and implemented.

Studying Microscopic Peer-to-Peer Communication Patterns

This paper describes first results of an ongoing research effort using real time data collected by social badges to correlate temporal changes in social interaction patterns with performance of individual actors and groups. Towards that goal we analyzed social interaction among a team of employees at a bank in Germany, and developed a set of interventions for more efficient collaboration. In particular, we were able to identify typical meeting patterns, and to distinguish between creative and high-executing knowledge work based on the interaction pattern.

Sensor-Based Feedback Systems in Organizational Computing

Radical change is needed in todays organizations. While e-mail, instant messaging, wikis, prediction markets, and the like have proliferated across myriad sectors, the fundamental practice of management has failed to keep pace. Sensors can automatically measure social behavior occurring in physical space as well as the virtual world. Moreover sensor-based feedback is poised to help create the change necessary to improve performance and satisfaction of workers. In this paper we summarize previous work on sensor-based feedback systems and propose new systems at the individual, group, and organizational level. Our goal is to help direct future research towards these promising avenues.

Understanding Organizational Behavior with Wearable Sensing Technology

We describe how recent advances in wearable sensing technology allow for unprecedented accuracy in studies of human behavior, particularly organizational behavior. We use one such platform, the Sociometric badge, to understand organizational behavior in two studies. In the first, we describe the collection of data over a period of one month in a German banks marketing division. We found that physical proximity had a high negative correlation with e-mail activity, and by combining behavioral data and electronic communication data we were able to very accurately predict self-reports of personal and group interaction satisfaction and performance. Next we describe an experiment at a data server configuration firm, and we discovered behavioral variables that had extremely high correlations with objective productivity measures. In both studies the fine-grained behavioral variables measured by the Sociometric badge played a critical role in predicting outcomes.

Mobile Sensing Technologies and Computational Methods for Collective Intelligence

This book chapter is a review of mobile sensing technologies and computational methods for collective intelligence. We discuss the application of mobile sensing to understand collective mechanisms and phenomena in face-to-face networks at three different scales: organizations, communities and societies. We present an overview of the state-of-the art in individual behavior recognition from sensor data. We discuss related work on group behavior recognition such as face-to-face interaction, social signaling, conversation detection, and conversation dynamics. We also present a brief overview of pattern recognition methods in social network analysis for the automatic identification of groups and the study of social network evolution. We describe a sensor-based organizational design and engineering system for computational collective intelligence applications in organizations. We also provide two example applications of collective intelligence and modeling user behavior at the community scale. Finally, we investigate the impact that these new sensing technologies may have on the understanding of societies, and how these insights can assist in the design of smarter cities and countries.

Sociometric Badges: A New Tool for I.S. Research

Researchers have recently been able to understand organizations at an unprecedented level of detail using new digital records and electronic communication data. However, while digital communication is important in the modern workplace, face-to-face interaction still represents a large and important share of organizational communication, information exchange, socialization and informal coordination. Unfortunately, few techniques exist to collect face-to-face communication data at the same level of granularity as electronic communication. In this essay, we introduce and discuss a new set of research tools and methodologies collectively known as Sociometric Badges – wearable sensing devices designed to collect data on face-to-face communication and interaction in real time. To highlight opportunities and challenges for IS research, we discuss a) the design and function of the technology, b) potential opportunities for IS and management research, c) key trade-offs, challenges and research design choices, and d) important limitations of the tools and techniques. We believe this set of technologies, which will soon be publicly available for research purposes at low cost, could portend a dramatic improvement in our understanding of human behavior at unprecedented levels of granularity and therefore enable IS researchers to explore new research questions and more accurately address existing lines of research.

Sensor-based organizational engineering

We propose the use of wearable and environmental sensors to capture and model social interactions in the workplace, combined with data mining techniques and social network analysis for organizational engineering applications. By combining behavioral sensor data with other sources of information such as text-mined documents, surveys, and performance data, it is possible to optimize organizations.