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Items: 1 to 20 of 99

1.

BALANCE: Towards a Usable Pervasive Wellness Application with Accurate Activity Inference.

Denning T, Andrew A, Chaudhri R, Hartung C, Lester J, Borriello G, Duncan G.

Proc IEEE Workshop Mob Comput Syst Appl. 2009;2009:5.

2.

Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults.

Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T.

JMIR Mhealth Uhealth. 2015 May 14;3(2):e42. doi: 10.2196/mhealth.4160.

3.

Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping.

Hill NJ, Gupta D, Brunner P, Gunduz A, Adamo MA, Ritaccio A, Schalk G.

J Vis Exp. 2012 Jun 26;(64). pii: 3993. doi: 10.3791/3993.

4.

Functionalities and input methods for recording food intake: a systematic review.

Rusin M, Arsand E, Hartvigsen G.

Int J Med Inform. 2013 Aug;82(8):653-64. doi: 10.1016/j.ijmedinf.2013.01.007. Epub 2013 Feb 13. Review.

PMID:
23415822
5.

Mobile Phone and Tablet Apps to Support Young People's Management of Their Physical Long-Term Conditions: A Systematic Review Protocol.

Majeed-Ariss R, Hall AG, McDonagh J, Fallon D, Swallow V.

JMIR Res Protoc. 2015 Apr 7;4(2):e40. doi: 10.2196/resprot.4159.

6.

A Robust Camera-Based Interface for Mobile Entertainment.

Roig-Maimó MF, Manresa-Yee C, Varona J.

Sensors (Basel). 2016 Feb 19;16(2):254. doi: 10.3390/s16020254.

7.

Mobile health applications to assist patients with diabetes: lessons learned and design implications.

Årsand E, Frøisland DH, Skrøvseth SO, Chomutare T, Tatara N, Hartvigsen G, Tufano JT.

J Diabetes Sci Technol. 2012 Sep 1;6(5):1197-206. Review.

8.

[Mobile phone platform for wireless monitoring of human dynamic plantar pressure].

Wang H, Han M, Liu J.

Zhongguo Yi Liao Qi Xie Za Zhi. 2010 Nov;34(6):403-7. Chinese.

PMID:
21360974
9.

Determining energy expenditure from treadmill walking using hip-worn inertial sensors: an experimental study.

Vathsangam H, Emken A, Schroeder ET, Spruijt-Metz D, Sukhatme GS.

IEEE Trans Biomed Eng. 2011 Oct;58(10):2804-15. doi: 10.1109/TBME.2011.2159840. Epub 2011 Jun 16.

10.
11.

Evaluation of the Accuracy of a Triaxial Accelerometer Embedded into a Cell Phone Platform for Measuring Physical Activity.

Manohar CU, McCrady SK, Fujiki Y, Pavlidis IT, Levine JA.

J Obes Weight Loss Ther. 2011 Dec 20;1(106). pii: 3309.

12.

Using reality mining to improve public health and medicine.

Pentland A, Lazer D, Brewer D, Heibeck T.

Stud Health Technol Inform. 2009;149:93-102.

PMID:
19745474
13.

FoodSwitch: A Mobile Phone App to Enable Consumers to Make Healthier Food Choices and Crowdsourcing of National Food Composition Data.

Dunford E, Trevena H, Goodsell C, Ng KH, Webster J, Millis A, Goldstein S, Hugueniot O, Neal B.

JMIR Mhealth Uhealth. 2014 Aug 21;2(3):e37. doi: 10.2196/mhealth.3230.

14.

Mobile real-time data acquisition system for application in preventive medicine.

Neubert S, Arndt D, Thurow K, Stoll R.

Telemed J E Health. 2010 May;16(4):504-9. doi: 10.1089/tmj.2009.0123.

PMID:
20420541
15.

A Group-Based Mobile Application to Increase Adherence in Exercise and Nutrition Programs: A Factorial Design Feasibility Study.

Du H, Venkatakrishnan A, Youngblood GM, Ram A, Pirolli P.

JMIR Mhealth Uhealth. 2016 Jan 15;4(1):e4. doi: 10.2196/mhealth.4900.

16.

Toward adaptive VR simulators combining visual, haptic, and brain-computer interfaces.

Lécuyer A, George L, Marchal M.

IEEE Comput Graph Appl. 2013 Sep-Oct;33(5):18-23. doi: 10.1109/MCG.2013.80.

PMID:
24808078
17.

Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing.

Medina E, Lopez D, Meseguer R, Ochoa SF, Royo D, Santos R.

Sensors (Basel). 2016 Jul 9;16(7). pii: E1062. doi: 10.3390/s16071062.

18.

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

Kim B, Kang S, Ha JY, Song J.

Sensors (Basel). 2015 Jul 16;15(7):17274-99. doi: 10.3390/s150717274.

19.

Weather effects on the patterns of people's everyday activities: a study using GPS traces of mobile phone users.

Horanont T, Phithakkitnukoon S, Leong TW, Sekimoto Y, Shibasaki R.

PLoS One. 2013 Dec 18;8(12):e81153. doi: 10.1371/journal.pone.0081153. eCollection 2013.

20.

Use of technology in children's dietary assessment.

Boushey CJ, Kerr DA, Wright J, Lutes KD, Ebert DS, Delp EJ.

Eur J Clin Nutr. 2009 Feb;63 Suppl 1:S50-7. doi: 10.1038/ejcn.2008.65.

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