322 related articles for article (PubMed ID: 22875251)
1. A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation.
Lin CW; Yang YT; Wang JS; Yang YC
IEEE Trans Inf Technol Biomed; 2012 Sep; 16(5):991-8. PubMed ID: 22875251
[TBL] [Abstract][Full Text] [Related]
2. SoM: a smart sensor for human activity monitoring and assisted healthy ageing.
Naranjo-Hernández D; Roa LM; Reina-Tosina J; Estudillo-Valderrama MÁ
IEEE Trans Biomed Eng; 2012 Nov; 59(11):3177-84. PubMed ID: 23086195
[TBL] [Abstract][Full Text] [Related]
3. An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors.
Wang Z; Jiang M; Hu Y; Li H
IEEE Trans Inf Technol Biomed; 2012 Jul; 16(4):691-9. PubMed ID: 22614724
[TBL] [Abstract][Full Text] [Related]
4. Comparing metabolic energy expenditure estimation using wearable multi-sensor network and single accelerometer.
Dong B; Biswas S; Montoye A; Pfeiffer K
Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():2866-9. PubMed ID: 24110325
[TBL] [Abstract][Full Text] [Related]
5. Physical Human Activity Recognition Using Wearable Sensors.
Attal F; Mohammed S; Dedabrishvili M; Chamroukhi F; Oukhellou L; Amirat Y
Sensors (Basel); 2015 Dec; 15(12):31314-38. PubMed ID: 26690450
[TBL] [Abstract][Full Text] [Related]
6. Walking pattern classification and walking distance estimation algorithms using gait phase information.
Wang JS; Lin CW; Yang YT; Ho YJ
IEEE Trans Biomed Eng; 2012 Oct; 59(10):2884-92. PubMed ID: 22893370
[TBL] [Abstract][Full Text] [Related]
7. Energy expenditure estimation during normal ambulation using triaxial accelerometry and barometric pressure.
Wang J; Redmond SJ; Voleno M; Narayanan MR; Wang N; Cerutti S; Lovell NH
Physiol Meas; 2012 Nov; 33(11):1811-30. PubMed ID: 23110944
[TBL] [Abstract][Full Text] [Related]
8. Accurate prediction of energy expenditure using a shoe-based activity monitor.
Sazonova N; Browning RC; Sazonov E
Med Sci Sports Exerc; 2011 Jul; 43(7):1312-21. PubMed ID: 21131868
[TBL] [Abstract][Full Text] [Related]
9. Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning.
Altini M; Penders J; Vullers R; Amft O
IEEE J Biomed Health Inform; 2015 Jan; 19(1):219-26. PubMed ID: 24691168
[TBL] [Abstract][Full Text] [Related]
10. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.
Ordóñez FJ; Roggen D
Sensors (Basel); 2016 Jan; 16(1):. PubMed ID: 26797612
[TBL] [Abstract][Full Text] [Related]
11. Posture and activity recognition and energy expenditure estimation in a wearable platform.
Sazonov E; Hegde N; Browning RC; Melanson EL; Sazonova NA
IEEE J Biomed Health Inform; 2015 Jul; 19(4):1339-46. PubMed ID: 26011870
[TBL] [Abstract][Full Text] [Related]
12. Wireless design of a multisensor system for physical activity monitoring.
Mo L; Liu S; Gao RX; John D; Staudenmayer JW; Freedson PS
IEEE Trans Biomed Eng; 2012 Nov; 59(11):3230-7. PubMed ID: 23086196
[TBL] [Abstract][Full Text] [Related]
13. Energy expenditure prediction using a miniaturized ear-worn sensor.
Atallah L; Leong JJ; Lo B; Yang GZ
Med Sci Sports Exerc; 2011 Jul; 43(7):1369-77. PubMed ID: 21200349
[TBL] [Abstract][Full Text] [Related]
14. An artificial neural network model of energy expenditure using nonintegrated acceleration signals.
Rothney MP; Neumann M; Béziat A; Chen KY
J Appl Physiol (1985); 2007 Oct; 103(4):1419-27. PubMed ID: 17641221
[TBL] [Abstract][Full Text] [Related]
15. Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life.
Gyllensten IC; Bonomi AG
IEEE Trans Biomed Eng; 2011 Sep; 58(9):2656-63. PubMed ID: 21712150
[TBL] [Abstract][Full Text] [Related]
16. Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors.
Cvetkovic B; Milic R; Lustrek M
IEEE J Biomed Health Inform; 2016 Jul; 20(4):1081-7. PubMed ID: 25974959
[TBL] [Abstract][Full Text] [Related]
17. A method to deal with installation errors of wearable accelerometers for human activity recognition.
Jiang M; Shang H; Wang Z; Li H; Wang Y
Physiol Meas; 2011 Mar; 32(3):347-58. PubMed ID: 21330698
[TBL] [Abstract][Full Text] [Related]
18. A stepwise validation of a wearable system for estimating energy expenditure in field-based research.
Rumo M; Amft O; Tröster G; Mäder U
Physiol Meas; 2011 Dec; 32(12):1983-2001. PubMed ID: 22056999
[TBL] [Abstract][Full Text] [Related]
19. Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.
Yang J; Singh H; Hines EL; Schlaghecken F; Iliescu DD; Leeson MS; Stocks NG
Artif Intell Med; 2012 Jun; 55(2):117-26. PubMed ID: 22503644
[TBL] [Abstract][Full Text] [Related]
20. Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm.
Ohkawara K; Oshima Y; Hikihara Y; Ishikawa-Takata K; Tabata I; Tanaka S
Br J Nutr; 2011 Jun; 105(11):1681-91. PubMed ID: 21262061
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]