REFERENCES
1. Gong C. Human-machine interface: design principles of visual information in human-machine interface design. In: 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics; 2009 Aug 26-27; Hangzhou, China. IEEE; 2009. p. 262-5.
2. Hristov H, Stavrev S. Generations of human-computer interactions: evolution, tendencies and perspectives. J Phys Conf Ser 2022;2339:012009.
3. Francés-Morcillo L, Morer-Camo P, Rodríguez-Ferradas MI, Cazón-Martín A. Wearable design requirements identification and evaluation. Sensors 2020;20:2599.
4. Yin R, Wang D, Zhao S, Lou Z, Shen G. Wearable sensors-enabled human-machine interaction systems: from design to application. Adv Funct Mater 2021;31:2008936.
5. Shi Q, Dong B, He T, et al. Progress in wearable electronics/photonics-moving toward the era of artificial intelligence and internet of things. InfoMat 2020;2:1131-62.
6. Godfrey A, Hetherington V, Shum H, Bonato P, Lovell NH, Stuart S. From A to Z: wearable technology explained. Maturitas 2018;113:40-7.
7. Tang G, Shi Q, Zhang Z, He T, Sun Z, Lee C. Hybridized wearable patch as a multi-parameter and multi-functional human-machine interface. Nano Energy 2021;81:105582.
8. Park S, Jayaraman S. Enhancing the quality of life through wearable technology. IEEE Eng Med Biol Mag 2003;22:41-8.
9. Zhu M, Sun Z, Zhang Z, et al. Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications. Sci Adv 2020;6:eaaz8693.
10. Gao W, Emaminejad S, Nyein HYY, et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 2016;529:509-14.
11. Bifulco P, Cesarelli M, Fratini A, Ruffo M, Pasquariello G, Gargiulo G. A wearable device for recording of biopotentials and body movements. In: 2011 IEEE International Symposium on Medical Measurements and Applications; 2011 May 30-31; Bari, Italy. IEEE; 2011. p. 469-72.
12. Vehkaoja A, Lekkala J. Wearable wireless biopotential measurement device. Proceedings of 26th annual international conference of the IEEE engineering in medicine and biology society; 2004 Feb p. 2177-9; San Francisco, CA, USA. IEEE; 2004.
13. Cinar E, Sahin F. EOG controlled mobile robot using radial basis function networks. In: 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control; 2009 Feb 2-4; Famagusta . North Cyprus. IEEE; 2009. p. 1-4.
14. Sasaki M, Matsushita K, Rusyidi MI, et al. Robot control systems using bio-potential signals. AIP Conf Proc 2020;2217:020008.
15. Ding J, Tang Y, Zhang L, Yan F, Gu X, Wu R. A novel front-end design for bioelectrical signal wearable acquisition. IEEE Sensors J 2019;19:8009-18.
16. Magno M, Benini L, Spagnol C, Popovici E. Wearable low power dry surface wireless sensor node for healthcare monitoring application. In: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob); 2013 Oct 7-9; Lyon, France.IEEE; 2013. p. 189-95.
18. Waldert S. Invasive vs. Non-invasive neuronal signals for brain-machine interfaces: will one prevail? Front Neuros 2016;10:295.
20. Pascual-Valdunciel A, Rajagopal A, Pons JL, Delp S. Non-invasive electrical stimulation of peripheral nerves for the management of tremor. J Neurol Sci 2022;435:120195.
21. Koutsou AD, Moreno JC, Del Ama AJ, Rocon E, Pons JL. Advances in selective activation of muscles for non-invasive motor neuroprostheses. J Neuroeng Rehabil 2016;13:56.
22. Caramenti M, Bartenbach V, Gasperotti L, Oliveira da Fonseca L, Berger TW, Pons JL. Challenges in neurorehabilitation and neural engineering. In: Pons JL, Raya R, González J, editors. Emerging Therapies in Neurorehabilitation II. Cham: Springer International Publishing; 2016. pp. 1-27.
23. Kruse J, Lee S. Biopotential electrode sensors in ECG/EEG/EMG systems. Analog Devices 2008;200:1 2. Available from: https://www.analog.com/en/technical-articles/biopotential-electrode-sensors-ecg-eeg-emg.html#/. [Last accessed on 21 Jun].
24. Picton TW, Bentin S, Berg P, et al. Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiol 2000;37:127-52.
25. Albulbul A. Evaluating major electrode types for idle biological signal measurements for modern medical technology. Bioengineering 2016;3:20.
26. Bao S, Gia TN, Chen W, Westerlund T. Wearable health monitoring system using flexible materials electrodes. In: 2020 IEEE 6th World Forum on Internet of Things (WF-IoT); 2020 Jun 2-16; New Orleans, LA, USA. IEEE; 2020. p. 1-2.
27. Lopez-Gordo MA, Sanchez-Morillo D, Pelayo Valle F. Dry EEG electrodes. Sensors 2014;14:12847-70.
28. Fu Y, Zhao J, Dong Y, Wang X. Dry electrodes for human bioelectrical signal monitoring. Sensors 2020;20:3651.
29. Herbert R, Jeong JW, Yeo AW. Soft material-enabled electronics for medicine, healthcare, and human-machine interfaces. Materials 2020;13:517.
30. Herbert R, Kim JH, Kim YS, Lee HM, Yeo WH. Soft Material-enabled, flexible hybrid electronics for medicine, healthcare, and human-machine interfaces. Materials 2018;11:187.
31. Kim T, Cho M, Yu KJ. Flexible and stretchable bio-integrated electronics based on carbon nanotube and graphene. Materials 2018;11:1163.
32. Yu X, Xie Z, Yu Y, et al. Skin-integrated wireless haptic interfaces for virtual and augmented reality. Nature 2019;575:473-9.
33. Zhao Y, Hou N, Wang Y, et al. All-fiber structure covered with two-dimensional conductive MOF materials to construct a comfortable, breathable and high-quality self-powered wearable sensor system. J Mater Chem A 2022;10:1248-56.
34. Zheng Y, Yu Z, Zhang S, et al. A molecular design approach towards elastic and multifunctional polymer electronics. Nat Commun 2021;12:5701.
35. Matsuhisa N, Niu S, O'Neill SJK, et al. High-frequency and intrinsically stretchable polymer diodes. Nature 2021;600:246-52.
36. Jiang Y, Zhang Z, Wang YX, et al. Topological supramolecular network enabled high-conductivity, stretchable organic bioelectronics. Science 2022;375:1411-7.
37. Zheng YQ, Liu Y, Zhong D, et al. Monolithic optical microlithography of high-density elastic circuits. Science 2021;373:88-94.
38. Vallem V, Sargolzaeiaval Y, Ozturk M, Lai YC, Dickey MD. Energy harvesting and storage with soft and stretchable materials. Adv Mater 2021;33:e2004832.
39. Song Y, Wang N, Hu C, Wang ZL, Yang Y. Soft triboelectric nanogenerators for mechanical energy scavenging and self-powered sensors. Nano Energy 2021;84:105919.
40. Song Y, Min J, Yu Y, et al. Wireless battery-free wearable sweat sensor powered by human motion. Sci Adv 2020;6:eaay9842.
41. Park Y, Kwon K, Kwak SS, et al. Wireless, skin-interfaced sensors for compression therapy. Sci Adv 2020;6:eabe1655.
42. Kwon YT, Kim H, Mahmood M, Kim YS, Demolder C, Yeo WH. Printed, wireless, soft bioelectronics and deep learning algorithm for smart human-machine interfaces. ACS Appl Mater Interfaces 2020;12:49398-406.
43. Wei H, Li K, Liu WG, Meng H, Zhang PX, Yan CY. 3D printing of free-standing stretchable electrodes with tunable structure and stretchability. Adv Eng Mater 2017;19:1700341.
44. Zhou LY, Gao Q, Fu JZ, et al. Multimaterial 3D printing of highly stretchable silicone elastomers. ACS Appl Mater Interfaces 2019;11:23573-83.
45. Muth JT, Vogt DM, Truby RL, et al. Embedded 3D printing of strain sensors within highly stretchable elastomers. Adv Mater 2014;26:6307-12.
47. Farina D, Holobar A. Human?machine interfacing by decoding the surface electromyogram [Life Sciences]. IEEE Signal Process Mag 2015;32:115-20.
48. Farina D, Jiang N, Rehbaum H, et al. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges. IEEE Trans Neural Syst Rehabil Eng 2014;22:797-809.
49. Holobar A, Farina D. Noninvasive neural interfacing with wearable muscle sensors: combining convolutive blind source separation methods and deep learning techniques for neural decoding. IEEE Signal Process Mag 2021;38:103-18.
50. Kollmitzer J, Ebenbichler GR, Kopf A. Reliability of surface electromyographic measurements. Clin Neurophysiol 1999;110:725-34.
51. Yeon SH, Shu T, Rogers EA, et al. Flexible dry electrodes for emg acquisition within lower extremity prosthetic sockets. In: 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). 2020. pp. 1088-95.
52. Murphy BB, Mulcahey PJ, Driscoll N, et al. A gel-free Ti3C2Tx-based electrode array for high-density, high-resolution surface electromyography. Adv Mater Technol 2020;5:2000325.
53. Driscoll N, Erickson B, Murphy BB, et al. MXene-infused bioelectronic interfaces for multiscale electrophysiology and stimulation. Sci Transl Med 2021;13:eabf8629.
54. Hammock ML, Chortos A, Tee BC, Tok JB, Bao Z. 25th anniversary article: the evolution of electronic skin (e-skin): a brief history, design considerations, and recent progress. Adv Mater 2013;25:5997-6038.
55. Goyal K, Borkholder DA, Day SW. Dependence of skin-electrode contact impedance on material and skin hydration. Sensors 2022;22:8510.
56. Yu Y, Li J, Solomon SA, et al. All-printed soft human-machine interface for robotic physicochemical sensing. Sci Robot 2022;7:eabn0495.
57. Zeng X, Dong Y, Wang X. Flexible electrode by hydrographic printing for surface electromyography monitoring. Materials 2020;13:2339.
58. Motti VG, Caine KE. Human factors considerations in the design of wearable devices. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2014;58:1820-4.
59. Huang C, Chiu C. Facile fabrication of a stretchable and flexible nanofiber carbon film-sensing electrode by electrospinning and its application in smart clothing for ECG and EMG monitoring. ACS Appl Electron Mater 2021;3:676-86.
60. Li J, Ma Y, Huang D, et al. High-performance flexible microneedle array as a low-impedance surface biopotential dry electrode for wearable electrophysiological recording and polysomnography. Nanomicro Lett 2022;14:132.
62. Tyagi A, Semwal S, Shah G. A review of EEG sensors used for data acquisition.In: National Conference on Future Aspects of Artificial intelligence in Industrial Automation (NCFAAIIA 2012), Proceedings published by International Journal of Computer Applications® (IJCA),2012:13-7. Acailable from: https://www.researchgate.net/publication/308259085_A_Review_of_Eeg_Sensors_used_for_Data_Acquisition.[Last accessed on 21 Jun]
63. Ganzer PD, Colachis SC 4th, Schwemmer MA, et al. Restoring the sense of touch using a sensorimotor demultiplexing neural interface. Cell 2020;181:763-773.e12.
64. Bouton CE, Shaikhouni A, Annetta NV, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 2016;533:247-50.
65. McFarland DJ, Sarnacki WA, Wolpaw JR. Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 2010;7:036007.
66. McNaughton BL, O'Keefe J, Barnes CA. The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J Neurosci Methods 1983;8:391-7.
68. Ponce CR, Lomber SG, Livingstone MS. Posterior inferotemporal cortex cells use multiple input pathways for shape encoding. J Neurosci 2017;37:5019-34.
69. Huang Z, Zhou Z, Zeng J, Lin S, Wu H. Flexible electrodes for non-invasive brain-computer interfaces: a perspective. APL Materials 2022;10:090901.
70. Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. A comprehensive review of EEG-based brain-computer interface paradigms. J Neural Eng 2019;16:011001.
72. Orban M, Elsamanty M, Guo K, Zhang S, Yang H. A review of brain activity and EEG-Based brain-computer interfaces for rehabilitation application. Bioengineering 2022;9:768.
73. Li G, Wang S, Duan YY. Towards conductive-gel-free electrodes: understanding the wet electrode, semi-dry electrode and dry electrode-skin interface impedance using electrochemical impedance spectroscopy fitting. Sensor Actuat B-Chem 2018;277:250-60.
74. Pradhapan P, Velazquez ER, Witteveen JA, Tonoyan Y, Mihajlović V. The role of features types and personalized assessment in detecting affective state using dry electrode EEG. Sensors 2020;20:6810.
75. Marini F, Lee C, Wagner J, Makeig S, Gola M. A comparative evaluation of signal quality between a research-grade and a wireless dry-electrode mobile EEG system. J Neural Eng 2019;16:054001.
76. Baum U, Baum A, Deike R, et al. Eignung eines mobilen trockenelektroden-EEG-Gerätes im Rahmen der Epilepsiediagnostik. Klin Neurophysiol 2020;51:156-60.
77. Mota A, Duarte L, Rodrigues D, et al. Development of a quasi-dry electrode for EEG recording. Sensor Actuat A-Phys 2013;199:310-7.
78. Lin S, Liu J, Li W, et al. A flexible, robust, and gel-free electroencephalogram electrode for noninvasive brain-computer interfaces. Nano Lett 2019;19:6853-61.
79. Huang K, Liu J, Lin S, et al. Flexible silver nanowire dry electrodes for long-term electrocardiographic monitoring. Adv Compos Hybrid Mater 2022;5:220-8.
80. Lee JH, Hwang JY, Zhu J, et al. Flexible conductive composite integrated with personal earphone for wireless, real-time monitoring of electrophysiological signs. ACS Appl Mater Interfaces 2018;10:21184-90.
81. Grozea C, Voinescu CD, Fazli S. Bristle-sensors--low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications. J Neural Eng 2011;8:025008.
82. Wang L, Liu J, Yang B, Yang C. PDMS-based low cost flexible dry electrode for long-term EEG measurement. IEEE Sensors J 2012;12:2898-904.
83. Li G, Wu J, Xia Y, et al. Towards emerging EEG applications: a novel printable flexible Ag/AgCl dry electrode array for robust recording of EEG signals at forehead sites. J Neural Eng 2020;17:026001.
84. Golparvar A, Ozturk O, Yapici MK. Gel-free wearable electroencephalography (eeg) with soft graphene textiles. In: 2021 IEEE Sensors; 2021 31 Oct-3 Nov; Sydney, Australia. IEEE; 2021. p. 1-4.
85. Carneiro MR, de Almeida AT, Tavakoli M. Wearable and comfortable e-textile headband for long-term acquisition of forehead EEG signals. IEEE Sensors J 2020;20:15107-16.
86. Li G, Zhang D, Wang S, Duan YY. Novel passive ceramic based semi-dry electrodes for recording electroencephalography signals from the hairy scalp. Sensor Actuat B-Chem 2016;237:167-78.
87. Liu J, Lin S, Li W, et al. Ten-hour stable noninvasive brain-computer interface realized by semidry hydrogel-based electrodes. Research 2022;2022:9830457.
88. Krishnan A, Kumar R, Venkatesh P, Kelly S, Grover P. Low-cost carbon fiber-based conductive silicone sponge EEG electrodes. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2018 Jul 18-21; Honolulu, HI, USA. IEEE; 2018. p. 1287-90.
89. Tseghai GB, Malengier B, Fante KA, Langenhove L Van. The status of textile-based dry EEG electrodes. Autex Res J 2021;21:63-70.
90. He G, Dong X, Qi M. From the perspective of material science: a review of flexible electrodes for brain-computer interface. Mater Res Express 2020;7:102001.
91. Stevenson C, Chang Y, He C, et al. Emerging non-invasive brain-computer interface technologies and their clinical applications. In: Chaurasia, M.A., Juang, CF , editors. Emerging IT/ICT and AI technologies affecting society. Singaporer: Springer; 2023. p. 269-90.
92. Dupre A, Vincent S, Iaizzo PA. Basic ECG theory, recordings, and interpretation. In: Iaizzo PA, editor. Handbook of Cardiac Anatomy, Physiology, and Devices. Totowa, NJ: Humana Press; 2005. p. 191-201.
93. Frederiks J, Swenne CA, Kors JA, et al. Within-subject electrocardiographic differences at equal heart rates: role of the autonomic nervous system. Pflugers Arch 2001;441:717-24.
94. Park C, Youn I, Han S. Single-lead ECG based autonomic nervous system assessment for meditation monitoring. Sci Rep 2022;12:22513.
95. Madona P, Basti RI, Zain MM. PQRST wave detection on ECG signals. Gac Sanit 2021;35 Suppl 2:S364-9.
96. AlGhatrif M, Lindsay J. A brief review: history to understand fundamentals of electrocardiography. J Community Hosp Intern Med Perspect 2012;2:14383.
97. Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med 2014;48:133-49.
98. Ramasamy S, Balan A. Wearable sensors for ECG measurement: a review. Sensor Review 2018;38:412-9.
99. Strik M, Ploux S, Weigel D, et al. The use of smartwatch electrocardiogram beyond arrhythmia detection. Trends Cardiovasc Med ;2023:S1050-1738(22)00153.
101. Abt G, Bray J, Benson AC. The validity and inter-device variability of the apple watch™ for measuring maximal heart rate. J Sports Sci 2018;36:1447-52.
102. Yu J, Park S, Kwon S, Cho K, Lee H. AI-based stroke disease prediction system using ECG and PPG bio-signals. IEEE Access 2022;10:43623-38.
103. Kim T, Park J, Sohn J, Cho D, Jeon S. Bioinspired, highly stretchable, and conductive dry adhesives based on 1D-2D hybrid carbon nanocomposites for all-in-one ECG electrodes. ACS Nano 2016;10:4770-8.
104. Ankhili A, Tao X, Cochrane C, Koncar V, Coulon D, Tarlet JM. Ambulatory evaluation of ECG signals obtained using washable textile-based electrodes made with chemically modified PEDOT:PSS. Sensors 2019;19:416.
105. Creel DJ. Chapter 33 - the electrooculogram. In: Levin KH, Chauvel P, editors. Handbook of Clinical Neurology. 2019. p. 495-9.
106. Soundariya RS, Renuga R. Eye movement based emotion recognition using electrooculography. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). 2017 Apr 21-22; Vellore, India.IEEE; 2017. p. 1-5.
107. Usakli AB, Gurkan S, Aloise F, Vecchiato G, Babiloni F. On the use of electrooculogram for efficient human computer interfaces. Comput Intell Neurosci 2010;2010:135629.
108. Choudhari AM, Porwal P, Jonnalagedda V, Mériaudeau F. An electrooculography based human machine interface for wheelchair control. Biocybern Biomed Eng 2019;39:673-85.
109. Ramkumar S, Emayavaramban G, Sathesh Kumar K, Macklin Abraham Navamani J, Maheswari K, Packia Amutha Priya P. Task identification system for elderly paralyzed patients using electrooculography and neural networks. In: Haldorai A, Ramu A, Mohanram S, Onn CC, editors. EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing; 2019 Oct 19; Springer, Cham; 2020. p. 151-61.
110. Kumar D, Sharma A. Electrooculogram-based virtual reality game control using blink detection and gaze calibration. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI); 2016 Sep 21-24; Jaipur, India. IEEE;2016. p. 2358-62.
112. Won Y, Lee JJ, Shin J, Lee M, Kim S, Gandla S. Biocompatible, transparent, and high-areal-coverage kirigami PEDOT:PSS electrodes for electrooculography-derived human-machine interactions. ACS Sens 2021;6:967-75.
113. Ameri SK, Kim M, Kuang IA, et al. Imperceptible electrooculography graphene sensor system for human-robot interface. npj 2D Mater Appl 2018:2.
114. Xiao J, Qu J, Li Y. An electrooculogram-based interaction method and its music-on-demand application in a virtual reality environment. IEEE Access 2019;7:22059-70.
115. Beach C, Karim N, Casson AJ. A graphene-based sleep mask for comfortable wearable eye tracking. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019 Jul 23-27; Berlin, Germany. IEEE; 2019. p. 6693 6.
116. Liang S, Kuo C, Lee Y, et al. Development of an EOG-based automatic sleep-monitoring eye mask. IEEE Trans Instrum Meas 2015;64:2977-85.
117. Li W, Lin K, Chen L, Yang D, Ge Q, Wang Z. Self-powered wireless flexible ionogel wearable devices. ACS Appl Mater Interfaces 2023;15:14768-76.
118. Wang W, Wang S, Rastak R, et al. Strain-insensitive intrinsically stretchable transistors and circuits. Nat Electron 2021;4:143-50.
119. Fatayerji HR, Saeed M, Qaisar SM, Alqurashi A, Al Talib R. Application of wavelet decomposition and ma-chine learning for the semg signal based ges-ture recognition. In: Qaisar SM, Nisar H, Subasi A, editors. Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning. Cham: Springer International Publishing; 2023. pp. 133-58.
120. Dong P, Song Y, Yu S, et al. Electromyogram-based lip-reading via unobtrusive dry electrodes and machine learning methods. Small 2023;19:e2205058.
121. Bi Z, Wang Y, Wang H, et al. Wearable EMG bridge-a multiple-gesture reconstruction system using electrical stimulation controlled by the volitional surface electromyogram of a healthy forearm. IEEE Access 2020;8:137330-41.
122. Wei Y, Yang K, Browne M, Bostan L, Worsley P. Wearable electrical stimulation to improve lymphatic function. IEEE Sens Lett 2019;3:1-4.
123. Ohm Y, Pan C, Ford MJ, Huang X, Liao J, Majidi C. An electrically conductive silver-polyacrylamide-alginate hydrogel composite for soft electronics. Nat Electron 2021;4:185-92.
124. Botzanowski B, Donahue MJ, Ejneby MS, et al. Noninvasive stimulation of peripheral nerves using temporally-interfering electrical fields. Adv Healthc Mater 2022;11:e2200075.
