Boxing has seen limited advancements in way of quantification of the sport during live professional fights. The PunchR system is an automated measurement system to collect information about a fight in realtime with fine-grained temporal resolution. The system employs nonintrusive accelerometers placed on the insides of boxers' wrists beneath their gloves to wirelessly relay acceleration data to a ringside laptop. Statistical models process the data for realtime estimates of fight metrics to quantify the action taking place in the ring including punch count, punch speed, punch force, and punch type. There are no new risks provided to the boxers during the fight nor do these accelerometers affect either of the boxers' physical performances.

The statistical methods used to construct these statistical models are non-parametric in a sense that the data is really driving the modeling procedure without any notion of a structure prior to model construction. Data for the models is collected in a controlled experimental boxing setting or through video review of live fights. Model features are derived from the acceleration profiles of punches combined with boxer physical measurements to create a complete feature set to train the models.

Primarily, the boxing metrics are largely tangible quantities that already are commonplace in boxing discussions (count, speed, force, and type) but have no prior method of measure. Methods are also developed to measure number of counterpunches and flurries using the timing of certain punches. Newly proposed metrics not in typical boxing terminology are either combinations of the common punch metrics or other measures taken from the acceleration data. These metrics attempt to measure a boxer's aggression and different measures of overall punch quality. The groundwork for these new contributions to boxing metrics and resulting visualizations for these metrics arise through the study of 65 professional boxing matches. Two complete fight summaries illustrate how a fight narrative can be told through use of the new metrics and visualizations. The PunchR system has been developed to begin the higher level quantification of boxing.

Mobile technology has become increasingly popular in the past decade through the combination of device portability and advances in network and internet technology. Smart phones, in particular, are at the forefront of these technologies, enabling users to remotely track and further involve themselves in the management of personal health through participatory mobile health applications. Thus far, the majority of participatory applications currently provide users with data visualizations displaying information germane to the user's medical condition, however, there is still a need for in-depth exploratory and inference-based data analysis using advanced statistical methods to maximize the discernment of potential implications carried in these data streams. This paper provides an overview of the structure of participatory data using an asthma management application as an initial platform and discusses several directions for statistical analysis motivated by three usage cases: individuals using the application, creators of the application, and the scientific community. Methods include functional and semi-parametric data analysis, mixed modeling, and clustering methods to model variables representing asthma wellness as a function of subject-specific, population level, and latent spatiotemporal factors. Societal implications are also discussed.

Advancements in technology and the changes in how people interact with data in recent years have given rise to online applications that allow people to collect data about themselves. For most applications, such as Twitter and Facebook, the collection is indirect. The primary purpose of the services is to share information with others. However, this regularly-updating online culture also provides a medium for personal data collection where people actively log data about themselves and their surroundings. This dissertation describes the development of your.flowingdata (YFD), an application that allows people to collect data via Twitter and to explore their data with a set of online visualization tools. Usage of the collection mechanism and visualizations is then described. Whereas most related work describes usage over a period of a week or less for in-lab users, YFD is a publicly available application and usage was studied over several months and for thousands of users. This provides a wider view into how general users, who are not necessarily "data professionals," collect and interact with their data. Study of YFD usage also provides insights for presentation of data to a wide audience and how to help them understand data.

Simple visible-light digital cameras offer a potential for expanded forms of plant ecological research. The moss Tortula princeps undergoes changes in reflected visible light during cycles of drying and hydrating in the field, and the MossCam project has collected digital images of T. princeps at least daily since 2003. Laboratory studies can be used to calibrate these images to indicate field physiological conditions. Drying the moss 6 d in the laboratory resulted in a decrease of net CO2 uptake to near 0; recovery after rewetting occurred within 10 min. The difference in reflectance between hydrated and dry T. princeps was maximal ca. 550 nm, and maximal net CO2 uptake was linearly related to the green : red ratio of laboratory images when net CO2 uptake was positive. Using the green : red ratio of field images and otherwise assuming ideal conditions, the total carbon gain for a 6-d period around a 1.3-mm rain event was ca. 208 mmol CO2 /m2, equivalent to 69 d of respiration under dry conditions. Using a visible-light digital camera with micrometeorological data and laboratory-based gas exchange measurements, T. princeps can be used as a model species for simple field estimations of photosynthesis, carbon gain, and phenological events.

Graham, E.A., M.P. Hamilton, B.D. Mishler, P.W. Rundel & M.H. Hansen. 2006. Use of a networked digital camera to estimate net CO2 uptake of a desiccation tolerant moss. International Journal of Plant Sciences 167:751-758.