Kligfield P, Gettes LS, Bailey JJ, Childers R, Deal BJ, Hancock EW, et al. Recommendations for the Standardization and Interpretation of the Electrocardiogram: part I: The Electrocardiogram and Its Technology A Scientific Statement From the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society Endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol. 2007;49(10):1109–27.
Article
Google Scholar
Pagani M, Malfatto G, Pierini S, Casati R, Masu AM, Poli M, et al. Spectral analysis of heart rate variability in the assessment of autonomic diabetic neuropathy. J Auton Nerv Syst. 1988;23(2):143–53.
Article
Google Scholar
Barbieri R, Matten EC, Alabi AA, Brown EN. A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am J Physiol Heart Circ Physiol. 2005;288(1):H424–35.
Article
Google Scholar
JDRF Continuous Glucose Monitoring Study Group. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med. 2008;359(14):1464–76.
Article
Google Scholar
Fensli R, Gunnarson E, Gundersen T. A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation. In: 18th IEEE Symposium on Computer-Based Medical Systems (CBMS’05). 2005. p. 407–12.
Yang G-Z, editor. Body Sensor Networks [Internet]. London: Springer-Verlag; 2006. https://www.springer.com/gp/book/9781846284847. Accessed 3 Jun 2019.
Milenković A, Otto C, Jovanov E. Wireless sensor networks for personal health monitoring: issues and an implementation. Comput Commun. 2006;29(13):2521–33.
Article
Google Scholar
Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern. 2010;40(1):1–12.
Article
Google Scholar
Gravina R, Alinia P, Ghasemzadeh H, Fortino G. Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf Fusion. 2017;1(35):68–80.
Article
Google Scholar
Tabazadeh A, Jensen EJ, Toon OB, Drdla K, Schoeberl MR. Role of the stratospheric polar freezing belt in denitrification. Science. 2001;291(5513):2591–4.
Article
Google Scholar
Technical Features and Functionalities of Myo Armband. An overview on related literature and advanced applications of myoelectric armbands mainly focused on arm prostheses. Int J Smart Sens Intellig Syst. 2018;11:1.
Google Scholar
Finni T, Hu M, Kettunen P, Vilavuo T, Cheng S. Measurement of EMG activity with textile electrodes embedded into clothing. Physiol Meas. 2007;28(11):1405–19.
Article
Google Scholar
Xu PJ, Zhang H, Tao XM. Textile-structured electrodes for electrocardiogram. Textile Progress. 2008;40(4):183–213.
Article
Google Scholar
Löfhede J, Seoane F, Thordstein M. Textile electrodes for eeg recording—a pilot study. Sensors (Basel). 2012;12(12):16907–19.
Article
Google Scholar
Kinkeldei T, Zysset C, Cherenack KH, Tröster G. A textile integrated sensor system for monitoring humidity and temperature. In: 2011 16th international solid-state sensors, actuators and microsystems conference. 2011. p. 1156–9.
Mattana G, Kinkeldei T, Leuenberger D, Ataman C, Ruan JJ, Molina-Lopez F, et al. Woven temperature and humidity sensors on flexible plastic substrates for E-textile applications. IEEE Sens J. 2013;13(10):3901–9.
Article
Google Scholar
Merritt CR, Nagle HT, Grant E. Textile-based capacitive sensors for respiration monitoring. IEEE Sens J. 2009;9(1):71–8.
Article
Google Scholar
Lee J, Kwon H, Seo J, Shin S, Koo JH, Pang C, et al. Conductive fiber-based ultrasensitive textile pressure sensor for wearable electronics. Adv Mater Weinheim. 2015;27(15):2433–9.
Article
Google Scholar
Meyer J, Arnrich B, Schumm J, Troster G. Design and modeling of a textile pressure sensor for sitting posture classification. Sens J IEEE. 2010;1(10):1391–8.
Article
Google Scholar
Holleczek T, Rüegg A, Harms H, Tröster G. Textile pressure sensors for sports applications. In: 2010 IEEE Sensors. 2010. p. 732–7.
Muhammad Sayem AS, Hon Teay S, Shahariar H, Luise Fink P, Albarbar A. Review on smart electro-clothing systems (SeCSs). Sensors. 2020;20(3):587.
Article
Google Scholar
Takeshita T, Yoshida M, Takei Y, Ouchi A, Hinoki A, Uchida H, et al. Relationship between contact pressure and motion artifacts in ECG measurement with electrostatic flocked electrodes fabricated on textile. Sci Rep. 2019;9(1):1–10.
Article
Google Scholar
Wearable sensors for ECG measurement: a review | Emerald Insight. https://www.emerald.com/insight/content/doi/10.1108/SR-06-2017-0110/full/html. Accessed 5 May 2020.
An X, Tangsirinaruenart O, Stylios GK. Investigating the performance of dry textile electrodes for wearable end-uses. J Text Instit. 2019;110:1. https://doi.org/10.1080/00405000.2018.1508799.
Article
Google Scholar
Eskandarian L, Lam E, Rupnow C, Alizadeh Meghrazi M, Robust Naguib HE, Yarns Multifunctional Conductive. Multifunctional conductive yarns for biomedical textile computing. ACS Appl Electron Mater. 2020. https://doi.org/10.1021/acsaelm.0c00171.
Article
Google Scholar
Feasibility of a T-shirt-type wearable electrocardiography monitor for detection of covert atrial fibrillation in young healthy adults| scientific reports. https://www.nature.com/articles/s41598-019-48267-1. Accessed 5 May 2020.
Steinberg C, Philippon F, Sanchez M, Fortier-Poisson P, O’Hara G, Molin F, et al. A novel wearable device for continuous ambulatory ECG recording: proof of concept and assessment of signal quality. Biosensors. 2019;9(1):17.
Article
Google Scholar
Villar R, Beltrame T, Hughson RL. Validation of the Hexoskin wearable vest during lying, sitting, standing, and walking activities. Appl Physiol Nutr Metab. 2015;40(10):1019–24.
Article
Google Scholar
Pan J, Tompkins WJ. A real-time QRS Detection Algorithm. IEEE Trans Biomed Eng. 1985;32(3):230–6.
Article
Google Scholar
Acar G, Ozturk O, Golparvar AJ, Elboshra TA, Böhringer K, Yapici MK. Wearable and flexible textile electrodes for biopotential signal monitoring: a review. Electronics. 2019;8(5):479.
Article
Google Scholar
Taji B, Shirmohammadi S, Groza V. Measuring skin-electrode impedance variation of conductive textile electrodes under pressure. In 2014. p. 1083–8.
Meziane N, Yang S, Shokoueinejad M, Webster JG, Attari M, Eren H. Simultaneous comparison of 1 gel with 4 dry electrode types for electrocardiography. Physiol Meas. 2015;36(3):513–29.
Article
Google Scholar
Ag R. Triboelectric noise. ISA Trans. 1970;1(9):154–8.
Google Scholar
Cömert A, Hyttinen J. Investigating the possible effect of electrode support structure on motion artifact in wearable bioelectric signal monitoring. Biomed Eng Online. 2015;15(14):44.
Article
Google Scholar
Priniotakis G, Westbroek P, Van Langenhove L, Hertleer C. Electrochemical impedance spectroscopy as an objective method for characterization of textile electrodes. Trans Instit Meas Control. 2007;1(29):271–81.
Article
Google Scholar
Wu W, Pirbhulal S, Sangaiah AK, Mukhopadhyay SC, Li G. Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Gener Comput Syst. 2018;1(86):515–26.
Article
Google Scholar
Webster JG. Reducing motion artifacts and interference in biopotential recording. IEEE Trans Biomed Eng. 1984;31(12):823–6.
Article
Google Scholar
Simakov A, Webster J. Motion artifact from electrodes and cables. Iran J Elect Comput Eng. 2010;1(9):139–43.
Google Scholar
Thomopoulos SCA. Sensor Integration And Data Fusion Sensor Fusion II: human and Machine Strategies. Int Soc Optics Photon. 1990;1990:178–91. https://doi.org/10.1117/12.969974.short.
Article
Google Scholar
Stiller C, Puente León F, Kruse M. Information fusion for automotive applications—an overview. Inf Fusion. 2011;12(4):244–52.
Article
Google Scholar
Kam M, Xiaoxun Z, Kalata P. Sensor fusion for mobile robot navigation. Proc IEEE. 1997;85(1):108–19.
Article
Google Scholar
Smith D, Singh S. Approaches to multisensor data fusion in target tracking: a survey. IEEE Trans Knowl Data Eng. 2006;18(12):1696–710.
Article
Google Scholar
Bancroft JB, Lachapelle G. Data fusion algorithms for multiple inertial measurement units. Sensors. 2011;11(7):6771–98.
Article
Google Scholar
Bergamini E, Ligorio G, Summa A, Vannozzi G, Cappozzo A, Sabatini AM. Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks. Sensors. 2014;14(10):18625–49.
Article
Google Scholar
Moody G, Mark R, Zoccola A, Mantero S. Derivation of respiratory signals from multilead ECGs. Computers in Cardiology. 1985;1:12.
Google Scholar
Nemati S, Malhotra A, Clifford GD. Data fusion for improved respiration rate estimation. EURASIP J Adv Signal Process. 2010;2010:1–10.
Article
Google Scholar
Liu K, Gebraeel NZ, Shi J. A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Trans Autom Sci Eng. 2013;10(3):652–64.
Article
Google Scholar
Chung W, Bhardwaj S, Punvar A, Lee D, Myllylae R. A fusion health monitoring using ECG and accelerometer sensors for elderly persons at home. In: 2007 29th annual international conference of the IEEE Engineering In Medicine And Biology Society. 2007. p. 3818–21.
Sanfilippo F, Pettersen KY. A sensor fusion wearable health-monitoring system with haptic feedback. In: 2015 11th International conference on innovations in information technology (IIT). 2015. p. 262–6.
Pani D, Achilli A, Bonfiglio A. Survey on textile electrode technologies for electrocardiographic (ECG) monitoring, from metal wires to polymers. Adv Mater Technol. 2018;3(10):1800008.
Article
Google Scholar
Webster JG. Medical instrumentation: application and design. New York: Wiley; 2009. p. 736.
Google Scholar
Nayak S, Bit A, Dey A, Mohapatra B, Pal K. A review on the nonlinear dynamical system analysis of electrocardiogram signal. J Healthc Eng. 2018;2(2018):1–19.
Article
Google Scholar
Yadav A, Grover N. A review of r peak detection techniques of electrocardiogram (ecg). J Eng Technol. 2017;8(2):15.
Google Scholar
Jenkal W, Latif R, Toumanari A, Dliou A, Bcharri O, Maoulainine FMR. An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybern Biomed Eng. 2016;36(3):499–508.
Article
Google Scholar
Tang J-T, Hua X-R, Cao Z-M, Ren Z-Y, Duan S-L. Hilbert-Huang transformation and noise suppression of magnetotelluric sounding data. Chin J Geophys Chin Ed. 2008;1(51):603–10.
Google Scholar
Pal S, Mitra M. Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method. Measurement. 2010;43(2):255–61.
Article
Google Scholar
Kohler B-U, Hennig C, Orglmeister R. The principles of software QRS detection. IEEE Eng Med Biol Mag. 2002;21(1):42–57.
Article
Google Scholar
Nishant M, Balwalli S, Nikunj M, et al. Hilbert Transform Based Adaptive ECG R-Peak Detection Technique. Int J Electr Comput Eng. 2012;2(5):639–43.
Google Scholar
Prasad ST, Varadarajan S. Heart rate detection using hilbert transform. In 2013.
Boussaa M, Issam A, Atibi M, abdellatif B. ECG Image Classification in Real time based on the Haar-like Features and Artificial Neural Networks. In 2015.
Barbieri R, Brown EN. Analysis of heartbeat dynamics by point process adaptive filtering. IEEE Trans Biomed Eng. 2006;53(1):4–12.
Article
Google Scholar