BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

163 related articles for article (PubMed ID: 24091138)

  • 1. Hand, belt, pocket or bag: Practical activity tracking with mobile phones.
    Antos SA; Albert MV; Kording KP
    J Neurosci Methods; 2014 Jul; 231():22-30. PubMed ID: 24091138
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Validity of activity trackers, smartphones, and phone applications to measure steps in various walking conditions.
    Höchsmann C; Knaier R; Eymann J; Hintermann J; Infanger D; Schmidt-Trucksäss A
    Scand J Med Sci Sports; 2018 Jul; 28(7):1818-1827. PubMed ID: 29460319
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Health monitors for chronic disease by gait analysis with mobile phones.
    Juen J; Cheng Q; Prieto-Centurion V; Krishnan JA; Schatz B
    Telemed J E Health; 2014 Nov; 20(11):1035-41. PubMed ID: 24694291
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Single-accelerometer-based daily physical activity classification.
    Long X; Yin B; Aarts RM
    Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():6107-10. PubMed ID: 19965261
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identifying typical physical activity on smartphone with varying positions and orientations.
    Miao F; He Y; Liu J; Li Y; Ayoola I
    Biomed Eng Online; 2015 Apr; 14():32. PubMed ID: 25889811
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Validity of a Smartphone-Based Fall Detection Application on Different Phones Worn on a Belt or in a Trouser Pocket.
    Vermeulen J; Willard S; Aguiar B; De Witte LP
    Assist Technol; 2015; 27(1):18-23. PubMed ID: 26132221
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Assessment of tremor activity in the Parkinson's disease using a set of wearable sensors.
    Rigas G; Tzallas AT; Tsipouras MG; Bougia P; Tripoliti EE; Baga D; Fotiadis DI; Tsouli SG; Konitsiotis S
    IEEE Trans Inf Technol Biomed; 2012 May; 16(3):478-87. PubMed ID: 22231198
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Using mobile phones for activity recognition in Parkinson's patients.
    Albert MV; Toledo S; Shapiro M; Kording K
    Front Neurol; 2012; 3():158. PubMed ID: 23162528
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.
    Khan AM; Siddiqi MH; Lee SW
    Sensors (Basel); 2013 Sep; 13(10):13099-122. PubMed ID: 24084108
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Dynamic SVM detection of tremor and dyskinesia during unscripted and unconstrained activities.
    Cole BT; Ozdemir P; Nawab SH
    Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():4927-30. PubMed ID: 23367033
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.
    O'Brien MK; Shawen N; Mummidisetty CK; Kaur S; Bo X; Poellabauer C; Kording K; Jayaraman A
    J Med Internet Res; 2017 May; 19(5):e184. PubMed ID: 28546137
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Activity recognition with smartphone support.
    Guiry JJ; van de Ven P; Nelson J; Warmerdam L; Riper H
    Med Eng Phys; 2014 Jun; 36(6):670-5. PubMed ID: 24641812
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Long-term activity recognition from wristwatch accelerometer data.
    Garcia-Ceja E; Brena RF; Carrasco-Jimenez JC; Garrido L
    Sensors (Basel); 2014 Nov; 14(12):22500-24. PubMed ID: 25436652
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Change-of-state determination to recognize mobility activities using a BlackBerry smartphone.
    Wu HH; Lemaire ED; Baddour N
    Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():5252-5. PubMed ID: 22255522
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Ambulatory monitoring of physical activities in patients with Parkinson's disease.
    Salarian A; Russmann H; Vingerhoets FJ; Burkhard PR; Aminian K
    IEEE Trans Biomed Eng; 2007 Dec; 54(12):2296-9. PubMed ID: 18075046
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An Automatic User-Adapted Physical Activity Classification Method Using Smartphones.
    Li P; Wang Y; Tian Y; Zhou TS; Li JS
    IEEE Trans Biomed Eng; 2017 Mar; 64(3):706-714. PubMed ID: 27249822
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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. PubMed ID: 19745474
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer.
    Vähä-Ypyä H; Vasankari T; Husu P; Suni J; Sievänen H
    Clin Physiol Funct Imaging; 2015 Jan; 35(1):64-70. PubMed ID: 24393233
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Where to wear accelerometers to measure physical activity in people?
    Thaler-Kall K; Tusker F; Hermsdörfer J; Gorzelniak L; Horsch A
    Stud Health Technol Inform; 2013; 192():1045. PubMed ID: 23920819
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Development of an automated physical activity classification application for mobile phones.
    Xia Y; Cheung V; Garcia E; Ding H; Karunaithi M
    Stud Health Technol Inform; 2011; 168():188-94. PubMed ID: 21893928
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.