@article {791, title = {Predicting human movement with multiple accelerometers using movelets.}, journal = {Med Sci Sports Exerc}, volume = {46}, year = {2014}, month = {2014 Sep}, pages = {1859-66}, abstract = {

PURPOSE: The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers.

METHODS: Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg{\textperiodcentered}m (2.5 kg{\textperiodcentered}m)) performed 15 lifestyle activities in the laboratory, each wearing three accelerometers at the right hip and left and right wrists. Triaxial accelerometry data were collected at 80 Hz using ActiGraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity-specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers.

RESULTS: With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2\% to 99.9\% on the basis of the single-accelerometer movelet approach). For these activities, wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from -2.7\% to 5.8\%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers.

DISCUSSION AND CONCLUSIONS: It is possible to achieve high prediction accuracy at the second-level temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.

}, issn = {1530-0315}, doi = {10.1249/MSS.0000000000000285}, author = {He, Bing and Bai, Jiawei and Zipunnikov, Vadim V and Koster, Annemarie and Caserotti, Paolo and Lange-Maia, Brittney and Glynn, Nancy W and Harris, Tamara B and Crainiceanu, Ciprian M} } @article {805, title = {Practical recommendations for population PK studies with sampling time errors.}, journal = {Eur J Clin Pharmacol}, volume = {69}, year = {2013}, month = {2013 Dec}, pages = {2055-64}, abstract = {

PURPOSE: Population pharmacokinetic (PK) data collected from routine clinical practice offers a rich source of valuable information. However, in observational population PK data, accurate time information for blood samples is often missing, resulting in measurement errors (ME) in the sampling time variable. The goal of this study was to investigate the effects on model parameters when a scheduled time is used instead of the actual blood sampling time, and to propose ME correction methods.

METHODS: Simulation studies were conducted based on two major factors: the curvature in PK profiles and the size of ME. As ME correction methods, transform both sides (TBS) models were developed with application of Box-Cox power transformation and Taylor expansion. The TBS models were compared to a conventional population PK model using simulations.

RESULTS: The most important determinant of bias due to time ME was the degree of curvature (nonlinearity) in PK profiles; the smaller the curvature around sampling times, the smaller the associated bias. The second important determinant was the magnitude of ME; the larger the ME, the larger the bias. The proposed TBS models performed better than a conventional population PK modeling when curvature and ME were substantial.

CONCLUSIONS: Time ME in sampling time can lead to bias on the parameter estimators. The following practical recommendations are provided: 1) when the curvature of PK profiles is small, conventional population PK modeling is robust to even large ME; and 2) when the curvature is moderate or large, the proposed methodology reduces bias in parameter estimates.

}, keywords = {Humans, Models, Biological, Pharmacokinetics, Research Design, Time Factors}, issn = {1432-1041}, doi = {10.1007/s00228-013-1576-7}, author = {Choi, Leena and Crainiceanu, Ciprian M and Caffo, Brian S} }