Výsledky vyhledávání - Algorithms implemented in hardware General Terms Algorithm

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    Thesis Advisors: Picos Gayà, Rodrigo, Stavrinides, Stavros G.

    Zdroj: TDX (Tesis Doctorals en Xarxa)

    Time: 621.3

    Popis souboru: application/pdf

    Přístupová URL adresa: http://hdl.handle.net/10803/688339

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    Přispěvatelé: University/Department: Universitat Ramon Llull. Departament d'Enginyeria

    Thesis Advisors: Alsina Pagès, Rosa Maria

    Zdroj: TDX (Tesis Doctorals en Xarxa)

    Popis souboru: application/pdf

    Přístupová URL adresa: http://hdl.handle.net/10803/674149

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    Přispěvatelé: University/Department: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions

    Thesis Advisors: Fernández Prades, Carles

    Zdroj: TDX (Tesis Doctorals en Xarxa)

    Time: 621.3

    Popis souboru: application/pdf

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    Přispěvatelé: Patrick Bergman, Assostand Professor

    Zdroj: Design of an Intelligent Wearable to Assess Physical Activity and Health Related Outcomes - the DIWAH Study
    Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, Carty C, Chaput JP, Chastin S, Chou R, Dempsey PC, DiPietro L, Ekelund U, Firth J, Friedenreich CM, Garcia L, Gichu M, Jago R, Katzmarzyk PT, Lambert E, Leitzmann M, Milton K, Ortega FB, Ranasinghe C, Stamatakis E, Tiedemann A, Troiano RP, van der Ploeg HP, Wari V, Willumsen JF. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020 Dec;54(24):1451-1462. doi: 10.1136/bjsports-2020-102955.
    Wright SP, Hall Brown TS, Collier SR, Sandberg K. How consumer physical activity monitors could transform human physiology research. Am J Physiol Regul Integr Comp Physiol. 2017 Mar 1;312(3):R358-R367. doi: 10.1152/ajpregu.00349.2016. Epub 2017 Jan 4.
    Hosanee M, Chan G, Welykholowa K, Cooper R, Kyriacou PA, Zheng D, Allen J, Abbott D, Menon C, Lovell NH, Howard N, Chan WS, Lim K, Fletcher R, Ward R, Elgendi M. Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring. J Clin Med. 2020 Mar 7;9(3):723. doi: 10.3390/jcm9030723.
    Aromatario O, Van Hoye A, Vuillemin A, Foucaut AM, Crozet C, Pommier J, Cambon L. How do mobile health applications support behaviour changes? A scoping review of mobile health applications relating to physical activity and eating behaviours. Public Health. 2019 Oct;175:8-18. doi: 10.1016/j.puhe.2019.06.011. Epub 2019 Jul 30.
    Bayoumy K, Gaber M, Elshafeey A, Mhaimeed O, Dineen EH, Marvel FA, Martin SS, Muse ED, Turakhia MP, Tarakji KG, Elshazly MB. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol. 2021 Aug;18(8):581-599. doi: 10.1038/s41569-021-00522-7. Epub 2021 Mar 4.
    Greiwe J, Nyenhuis SM. Wearable Technology and How This Can Be Implemented into Clinical Practice. Curr Allergy Asthma Rep. 2020 Jun 6;20(8):36. doi: 10.1007/s11882-020-00927-3.
    Peake JM, Kerr G, Sullivan JP. A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations. Front Physiol. 2018 Jun 28;9:743. doi: 10.3389/fphys.2018.00743. eCollection 2018.
    Bergman P. The number of repeated observations needed to estimate the habitual physical activity of an individual to a given level of precision. PLoS One. 2018 Feb 1;13(2):e0192117. doi: 10.1371/journal.pone.0192117. eCollection 2018.
    Bergman P, Hagstromer M. No one accelerometer-based physical activity data collection protocol can fit all research questions. BMC Med Res Methodol. 2020 Jun 3;20(1):141. doi: 10.1186/s12874-020-01026-7.
    Jensen MT, Treskes RW, Caiani EG, Casado-Arroyo R, Cowie MR, Dilaveris P, Duncker D, Di Rienzo M, Frederix I, De Groot N, Kolh PH, Kemps H, Mamas M, McGreavy P, Neubeck L, Parati G, Platonov PG, Schmidt-Trucksass A, Schuuring MJ, Simova I, Svennberg E, Verstrael A, Lumens J. ESC working group on e-cardiology position paper: use of commercially available wearable technology for heart rate and activity tracking in primary and secondary cardiovascular prevention-in collaboration with the European Heart Rhythm Association, European Association of Preventive Cardiology, Association of Cardiovascular Nursing and Allied Professionals, Patient Forum, and the Digital Health Committee. Eur Heart J Digit Health. 2021 Feb 8;2(1):49-59. doi: 10.1093/ehjdh/ztab011. eCollection 2021 Mar.
    Wong CK, Mentis HM, Kuber R. The bit doesn't fit: Evaluation of a commercial activity-tracker at slower walking speeds. Gait Posture. 2018 Jan;59:177-181. doi: 10.1016/j.gaitpost.2017.10.010. Epub 2017 Oct 9.
    Brage S, Brage N, Franks PW, Ekelund U, Wong MY, Andersen LB, Froberg K, Wareham NJ. Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. J Appl Physiol (1985). 2004 Jan;96(1):343-51. doi: 10.1152/japplphysiol.00703.2003. Epub 2003 Sep 12.
    Keadle SK, Lyden KA, Strath SJ, Staudenmayer JW, Freedson PS. A Framework to Evaluate Devices That Assess Physical Behavior. Exerc Sport Sci Rev. 2019 Oct;47(4):206-214. doi: 10.1249/JES.0000000000000206.
    Muhlen JM, Stang J, Lykke Skovgaard E, Judice PB, Molina-Garcia P, Johnston W, Sardinha LB, Ortega FB, Caulfield B, Bloch W, Cheng S, Ekelund U, Brond JC, Grontved A, Schumann M. Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network. Br J Sports Med. 2021 Jul;55(14):767-779. doi: 10.1136/bjsports-2020-103148. Epub 2021 Jan 4.
    Gillinov S, Etiwy M, Wang R, Blackburn G, Phelan D, Gillinov AM, Houghtaling P, Javadikasgari H, Desai MY. Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise. Med Sci Sports Exerc. 2017 Aug;49(8):1697-1703. doi: 10.1249/MSS.0000000000001284.
    Oja, P. & Tuxworth, B. Eurofit for adults. Assessment of health-related fitness. Strasbourg: Council of Europe-UKK Institute, Tampere. (1995).
    Podsiadlo D, Richardson S. The timed "Up & Go": a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991 Feb;39(2):142-8. doi: 10.1111/j.1532-5415.1991.tb01616.x.
    Westerterp KR. Doubly labelled water assessment of energy expenditure: principle, practice, and promise. Eur J Appl Physiol. 2017 Jul;117(7):1277-1285. doi: 10.1007/s00421-017-3641-x. Epub 2017 May 15.
    Arvidsson D, Fridolfsson J, Borjesson M. Measurement of physical activity in clinical practice using accelerometers. J Intern Med. 2019 Aug;286(2):137-153. doi: 10.1111/joim.12908. Epub 2019 Apr 16.
    Liu F, Wanigatunga AA, Schrack JA. Assessment of Physical Activity in Adults Using Wrist Accelerometers. Epidemiol Rev. 2022 Jan 14;43(1):65-93. doi: 10.1093/epirev/mxab004.
    Rastogi T, Backes A, Schmitz S, Fagherazzi G, van Hees V, Malisoux L. Advanced analytical methods to assess physical activity behaviour using accelerometer raw time series data: a protocol for a scoping review. Syst Rev. 2020 Nov 7;9(1):259. doi: 10.1186/s13643-020-01515-2.
    Garatachea N, Torres Luque G, Gonzalez Gallego J. Physical activity and energy expenditure measurements using accelerometers in older adults. Nutr Hosp. 2010 Mar-Apr;25(2):224-30.
    Heesch KC, Hill RL, Aguilar-Farias N, van Uffelen JGZ, Pavey T. Validity of objective methods for measuring sedentary behaviour in older adults: a systematic review. Int J Behav Nutr Phys Act. 2018 Nov 26;15(1):119. doi: 10.1186/s12966-018-0749-2.
    Phillips LJ, Petroski GF, Markis NE. A Comparison of Accelerometer Accuracy in Older Adults. Res Gerontol Nurs. 2015 Sep-Oct;8(5):213-9. doi: 10.3928/19404921-20150429-03. Epub 2015 May 7.
    Sheng, B., Moosman, O. M., Del Pozo-Cruz, B., Del Pozo-Cruz, J., Alfonso-Rosa, R. M. & Zhang, Y. A comparison of different machine learning algorithms, types and placements of activity monitors for physical activity classification. Measurement 154, 107480 (2020).
    Montoye AHK, Pivarnik JM, Mudd LM, Biswas S, Pfeiffer KA. Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior. AIMS Public Health. 2016 May 20;3(2):298-312. doi: 10.3934/publichealth.2016.2.298. eCollection 2016.
    Montoye AHK, Westgate BS, Fonley MR, Pfeiffer KA. Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer. J Appl Physiol (1985). 2018 May 1;124(5):1284-1293. doi: 10.1152/japplphysiol.00760.2017. Epub 2018 Jan 25.
    Ahmadi MN, Chowdhury A, Pavey T, Trost SG. Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation. PLoS One. 2020 May 20;15(5):e0233229. doi: 10.1371/journal.pone.0233229. eCollection 2020.
    Stewart T, Narayanan A, Hedayatrad L, Neville J, Mackay L, Duncan S. A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults. Med Sci Sports Exerc. 2018 Dec;50(12):2595-2602. doi: 10.1249/MSS.0000000000001717.
    Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Sci Rep. 2018 May 21;8(1):7961. doi: 10.1038/s41598-018-26174-1.
    Narayanan A, Desai F, Stewart T, Duncan S, Mackay L. Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review. J Phys Act Health. 2020 Mar 1;17(3):360-383. doi: 10.1123/jpah.2019-0088.
    Galán-Mercant, A., Ortiz, A., Herrera-Viedma, E., Tomas, M. T., Fernandes, B. & Moral-Munoz, J. A. Assessing physical activity and functional fitness level using convolutional neural networks. Knowledge-Based Systems 185 (2019).
    Hamid A, Duncan MJ, Eyre ELJ, Jing Y. Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data. Eur J Sport Sci. 2021 Jun;21(6):918-926. doi: 10.1080/17461391.2020.1789749. Epub 2020 Jul 16.
    van Kuppevelt D, Heywood J, Hamer M, Sabia S, Fitzsimons E, van Hees V. Segmenting accelerometer data from daily life with unsupervised machine learning. PLoS One. 2019 Jan 9;14(1):e0208692. doi: 10.1371/journal.pone.0208692. eCollection 2019.
    Jones PJ, Catt M, Davies MJ, Edwardson CL, Mirkes EM, Khunti K, Yates T, Rowlands AV. Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review. Gait Posture. 2021 Oct;90:120-128. doi: 10.1016/j.gaitpost.2021.08.007. Epub 2021 Aug 13.
    Hochberg I, Feraru G, Kozdoba M, Mannor S, Tennenholtz M, Yom-Tov E. Encouraging Physical Activity in Patients With Diabetes Through Automatic Personalized Feedback via Reinforcement Learning Improves Glycemic Control. Diabetes Care. 2016 Apr;39(4):e59-60. doi: 10.2337/dc15-2340. Epub 2016 Jan 28. No abstract available.
    Wijkman M, Carlsson M, Darwiche G, Nystrom FH. A pilot study of hypertension management using a telemedicine treatment approach. Blood Press Monit. 2020 Feb;25(1):18-21. doi: 10.1097/MBP.0000000000000413.

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    Zdroj: Veterinary Medicine & Public Health Journal. Sep2025, Vol. 6 Issue 3, p105-118. 14p.

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