@article {804, title = {Normalization and extraction of interpretable metrics from raw accelerometry data.}, journal = {Biostatistics}, volume = {15}, year = {2014}, month = {2014 Jan}, pages = {102-16}, abstract = {

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

}, keywords = {Acceleration, Aged, Baltimore, Cohort Studies, Data Interpretation, Statistical, Female, Humans, Male, Motor Activity}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxt029}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A and Crainiceanu, Ciprian M} } @article {Bai2014Metrics, title = {Normalization and extraction of interpretable metrics from raw accelerometry data}, journal = {Biostatistics}, volume = {15}, number = {1}, year = {2014}, pages = {102{\textendash}116}, abstract = {

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

}, keywords = {Activity intensity, Movelets, Movement, Signal processing, Time active, Tri-axial accelerometer}, issn = {14654644}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A. and Crainiceanu, Ciprian M.} } @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} }