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Your search keyword '"Ciprian, M."' showing total 13 results
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13 results on '"Ciprian, M."'

6. Association of Spectral-Domain OCT With Long-term Disability Worsening in Multiple Sclerosis.

Author

Lambe, Jeffrey, Fitzgerald, Kathryn C., Murphy, Olwen C., Filippatou, Angeliki G., Sotirchos, Elias S., Kalaitzidis, Grigorios, Vasileiou, Elena, Pellegrini, Nicole, Ogbuokiri, Esther, Toliver, Brandon, Luciano, Nicholas J., Davis, Simidele, Fioravante, Nicholas, Kwakyi, Ohemaa, Risher, Hunter, Crainiceanu, Ciprian M., Prince, Jerry L., Newsome, Scott D., Mowry, Ellen M., and Saidha, Shiv

Published
2021
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7. Predicting Human Movement With Multiple Accelerometers Using Movelets

Author

Bing He, Brittney S. Lange-Maia, Paolo Caserotti, Vadim V Zipunnikov, Tamara B. Harris, Ciprian M. Crainiceanu, Jiawei Bai, Annemarie Koster, Nancy W. Glynn, Sociale Geneeskunde, RS: CAPHRI School for Public Health and Primary Care, and RS: CAPHRI - Social participation and health

Subjects
Male, Computer science, Posture, Physical activity, Time Series, Physical Therapy, Sports Therapy and Rehabilitation, Walking, Pattern Recognition, Motor Activity, Accelerometer, Article, Predictive Value of Tests, Accelerometry, Activities of Daily Living, Humans, Orthopedics and Sports Medicine, Motor activity, Aged, Aged, 80 and over, Signal processing, Training set, business.industry, Pattern recognition, Physical Activity, Prediction algorithms, Temporal resolution, Signal Processing, Pattern recognition (psychology), Female, Artificial intelligence, business, Algorithms
Abstract

AB 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[middle dot]m-2 (2.5 kg[middle dot]m-2)) 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.

Published
2014
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11. Abstract TMP18.

Author

Leigh, Richard, Jen, Shyian S, Sweeney, Elizabeth M, Crainiceanu, Ciprian M, Hillis, Argye E, Krakauer, John W, and Barker, Peter B

Published
2013

12. Abstract WMP13.

Author

Jen, Shyian S, Sweeney, Elizabeth M, Hillis, Argye E, Crainiceanu, Ciprian M, Krakauer, John W, Barker, Peter B, and Leigh, Richard

Published
2013

13. E-007.

Author

Gribble, Matthew O., Howard, Barbara V., Umans, Jason G., Francesconi, Kevin A., Goessler, Walter, Crainiceanu, Ciprian M., Silbergeld, Ellen K, Guallar, Eliseo, and Navas-Acien, Ana

Published
2012
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