Jonathan Westhues

The MERL motion detector dataset

Looking into the future of residential and office building Mitsubishi Electric Research Labs (MERL) has been collecting motion sensor data from a network of over 200 sensors for a year. The data is the residual traces of year in the life of a research laboratory. It contains interesting spatio-temporal structure ranging all the way from the seconds of individuals walking down hallways, the minutes in lobbies chatting with colleagues, the hours of dozens of people attending talks and meetings, the days and weeks that drive the patterns of life, to the months and seasons with their ebb and flow of visiting employees. This document describes that dataset, which contains well over 30 million raw motion records, spanning a calendar year and two floors of our research laboratory, as well as calender, weather, and some intermediate analytic results. The dataset was originally released as part of the 2007 Workshop on Massive Datasets. The dataset can be obtained from http://www.merl.com/wmd.

Submerging technologies

Fountains, reflecting pools, and other water displays have a long history in art and architecture. In the fountain industry, an “interactive fountain” is one that guests can touch or walk inside of to get wet. This is counter to our typical definition of an interactive system which requires sensors to detect user actions, and an output that changes in response to these actions. In this work, techniques for making truly interactive water displays are presented.

Socialmotion: measuring the hidden social life of a building

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.

Buzz: measuring and visualizing conference crowds

This exhibition explores the idea of using technology to understand the movement of people. Not just on a small stage, but in an expansive environment. Not the fine details of movement of individuals, but the gross patterns of a population. Not the identifying biometrics, but patterns of group behavior that evolve from the structure of the environment and the points of interest embedded in that structure. In this instance: a marketplace, and in particular, the marketplace of ideas called SIGGRAPH 2007 Emerging Technologies (ETech).