D
Donald J. Patterson
Researcher at University of California, Irvine
Publications - 74
Citations - 6195
Donald J. Patterson is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Ubiquitous computing & Context (language use). The author has an hindex of 24, co-authored 71 publications receiving 5902 citations. Previous affiliations of Donald J. Patterson include University of Washington & Westmont College.
Papers
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Journal ArticleDOI
Serum Phosphate Levels and Mortality Risk among People with Chronic Kidney Disease
Bryan Kestenbaum,Joshua N. Sampson,Kyle Rudser,Donald J. Patterson,Stephen L. Seliger,Bessie A. Young,Bessie A. Young,Donald J. Sherrard,Dennis L. Andress +8 more
TL;DR: Elevated serum phosphate levels were independently associated with increased mortality risk among this population of patients with chronic kidney disease and were associated with a significantly increased risk for death.
Journal ArticleDOI
Inferring activities from interactions with objects
Matthai Philipose,Kenneth P. Fishkin,Mike Perkowitz,Donald J. Patterson,Dieter Fox,Henry Kautz,Dirk Hähnel +6 more
TL;DR: The key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution.
Journal ArticleDOI
Learning and inferring transportation routines
TL;DR: In this paper, a hierarchical Markov model is proposed to infer a user's daily movements through an urban community using multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as user's destination and mode of transportation.
Book ChapterDOI
Inferring High-Level Behavior from Low-Level Sensors
TL;DR: In this paper, a method of learning a Bayesian model of a traveler moving through an urban environment is presented, which simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner.
Journal ArticleDOI
Efficiently Scaling up Crowdsourced Video Annotation
TL;DR: It is argued that video annotation requires specialized skill; most workers are poor annotators, mandating robust quality control protocols and an inherent trade-off between the mix of human and cloud computing used vs. the accuracy and cost of the labeling.