{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:59:49Z","timestamp":1779382789007,"version":"3.53.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T00:00:00Z","timestamp":1648339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living conditions, with data recording on an SD or wireless transmission to an external elaborator. Thanks to the wide horizon of applications, research is very active in this field, and in the last few years, some devices have been introduced on the market. The main problem with those devices is that their operation is time-limited, so they do not match the growing demand for long monitoring, which is a must-have feature in diagnosing specific diseases. Furthermore, their versatility is hampered by the fact that they have been designed to record just one type of signal. Using ST-Qvar sensors, we acquired an electrocardiogram trace and single-channel scalp electroencephalogram from the frontal lobes, together with an electrooculogram. Excellent results from all three types of acquisition tests were obtained. The power consumption is very low, demonstrating that, thanks to the MEMS technology, a continuous acquisition is feasible for several days.<\/jats:p>","DOI":"10.3390\/s22072566","type":"journal-article","created":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T21:31:25Z","timestamp":1648416685000},"page":"2566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1232-0354","authenticated-orcid":false,"given":"Alessandro","family":"Manoni","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Gumiero","sequence":"additional","affiliation":[{"name":"STMicroelectronics, 20864 Agrate Brianza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0227-1993","authenticated-orcid":false,"given":"Alessandro","family":"Zampogna","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4996-2820","authenticated-orcid":false,"given":"Chiara","family":"Ciarlo","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0238-2837","authenticated-orcid":false,"given":"Lorenzo","family":"Panetta","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9903-5550","authenticated-orcid":false,"given":"Antonio","family":"Suppa","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy"},{"name":"IRCCS Neuromed, 86077 Pozzilli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"Della Torre","sequence":"additional","affiliation":[{"name":"STMicroelectronics, 20864 Agrate Brianza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1831-6786","authenticated-orcid":false,"given":"Fernanda","family":"Irrera","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zampogna, A., Mileti, I., Palermo, E., Celletti, C., Paoloni, M., Manoni, A., Mazzetta, I., Dalla Costa, G., P\u00e9rez-L\u00f3pez, C., and Camerota, F. (2020). Fifteen years of wireless sensors for balance assessment in neurological disorders. Sensors, 20.","DOI":"10.3390\/s20113247"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jani, A.B., Bagree, R., and Roy, A.K. (November, January 29). Design of a low-power, low-cost ECG & EMG sensor for wearable biometric and medical application. Proceedings of the 2017 IEEE SENSORS, Glasgow, UK.","DOI":"10.1109\/ICSENS.2017.8234427"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mazzetta, I., Zampogna, A., Suppa, A., Gumiero, A., Pessione, M., and Irrera, F. (2019). Wearable sensors system for an improved analysis of freezing of gait in parkinson\u2019s disease using electromyography and inertial signals. Sensors, 19.","DOI":"10.3390\/s19040948"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mai, N.-D., Hoang Long, N.M., and Chung, W.-Y. (2021, January 20\u201323). 1D-CNN-based BCI system for detecting emotional states using a wireless and wearable 8-channel custom-designed eeg headset. Proceedings of the 2021 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), Manchester, UK.","DOI":"10.1109\/FLEPS51544.2021.9469818"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"250","DOI":"10.3389\/fnhum.2019.00250","article-title":"Analysis of prefrontal single-channel EEG data for portable auditory ERP-based brain\u2014Computer interfaces","volume":"13","author":"Ogino","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1177\/155005940003100305","article-title":"EEG in the elderly: Seizures vs. syncope","volume":"31","author":"Hughes","year":"2000","journal-title":"Clin. Electroencephalogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1002\/ana.10547","article-title":"Slowing of electroencephalogram in rapid eye movement sleep behavior disorder: Electroencephalogram in RBD","volume":"53","author":"Gagnon","year":"2003","journal-title":"Ann. Neurol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.resuscitation.2021.05.032","article-title":"Early recovery of frontal EEG slow wave activity during propofol sedation predicts outcome after cardiac arrest","volume":"165","author":"Kortelainen","year":"2021","journal-title":"Resuscitation"},{"key":"ref_9","unstructured":"(2021, December 13). Diadem. Available online: https:\/\/www.bitbrain.com\/neurotechnology-products\/dry-eeg\/diadem."},{"key":"ref_10","unstructured":"(2021, December 13). BrainBit. Available online: http:\/\/brainbit.com\/."},{"key":"ref_11","unstructured":"CGX (2021, December 13). Dry EEG Headsets\u2014Products. Available online: https:\/\/www.cgxsystems.com\/products."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"371","DOI":"10.3389\/fnagi.2019.00371","article-title":"Age-related changes in cortical connectivity during surgical anesthesia","volume":"11","author":"Li","year":"2020","journal-title":"Front. Aging Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Miller, S., Chelian, S., Mcburnett, W., Tsou, W., and Kruse, A. (2019, January 23\u201327). An investigation of computer-based brain training on the cognitive and EEG performance of employees. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856758"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3389\/fnhum.2019.00063","article-title":"Classification of movement intention using independent components of premovement EEG","volume":"13","author":"Kim","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_15","unstructured":"(2021, December 13). DSI 7 Flex. Available online: https:\/\/wearablesensing.com\/products\/dsi-7-flex\/."},{"key":"ref_16","unstructured":"(2022, February 02). Product. Available online: https:\/\/mentalab.com\/product."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Apicella, A., Arpaia, P., Mastrati, G., and Moccaldi, N. (2021). High-Wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions, Research Square.","DOI":"10.21203\/rs.3.rs-493089\/v1"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2800108","DOI":"10.1109\/JTEHM.2017.2702558","article-title":"Automatic sleep monitoring using ear-EEG","volume":"5","author":"Nakamura","year":"2017","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TBME.2019.2911423","article-title":"Hearables: Automatic overnight sleep monitoring with standardized in-ear EEG sensor","volume":"67","author":"Nakamura","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.3758\/s13428-021-01538-0","article-title":"Mobile ear-EEG to study auditory attention in everyday life","volume":"53","author":"Meekes","year":"2021","journal-title":"Behav. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s10548-020-00793-2","article-title":"The sensitivity of ear-EEG: Evaluating the source-sensor relationship using forward modeling","volume":"33","author":"Meiser","year":"2020","journal-title":"Brain Topogr."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kowey, P., Piccini, J.P., Naccarelli, G., and Reiffel, J.A. (2017). Extended ECG monitoring. Cardiac Arrhythmias, Pacing and Sudden Death, Springer International Publishing. Cardiovascular Medicine.","DOI":"10.1007\/978-3-319-58000-5"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"16","DOI":"10.36290\/vnl.2021.002","article-title":"Long-term ECG monitoring","volume":"67","year":"2021","journal-title":"Vnitr. Lek."},{"key":"ref_24","first-page":"8","article-title":"Prolonged holter-ECG monitoring found to improve detection of atrial fibrillation after acute stroke","volume":"17","author":"Bender","year":"2017","journal-title":"Neurol. Today"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/RBME.2018.2840336","article-title":"Noncontact wearable wireless ECG systems for long-term monitoring","volume":"11","author":"Majumder","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_26","unstructured":"Fourth Frontier (2021, December 10). Real-Time ECG and Alerts. Available online: https:\/\/fourthfrontier.com\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","article-title":"Smart wearable devices in cardiovascular care: Where we are and how to move forward","volume":"18","author":"Bayoumy","year":"2021","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s13534-018-00093-6","article-title":"Wearable EEG and beyond","volume":"9","author":"Casson","year":"2019","journal-title":"Biomed. Eng. Lett."},{"key":"ref_29","unstructured":"(2021, February 10). DSD TECH HM19\u2014Product Datasheet. DSD TECH HM-19 Bluetooth 5.0 BLE Module with CC2640R2F Chip. Available online: dsdtech-global.com."},{"key":"ref_30","unstructured":"(2022, February 02). X-NUCLEO-IKS01A1\u2014Motion MEMS and Environmental Sensor Expansion Board for STM32 Nucleo\u2014STMicroelectronics. Available online: https:\/\/www.st.com\/en\/ecosystems\/x-nucleo-iks01a1.html."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"64280","DOI":"10.1039\/C4RA09604E","article-title":"Friction, tribochemistry and triboelectricity: Recent progress and perspectives","volume":"4","author":"Galembeck","year":"2014","journal-title":"RSC Adv."},{"key":"ref_32","unstructured":"(2022, March 03). LSM6DSV16X\u2014INEMO 3D Accelerometer and 3D Gyroscope: Always-on Inertial Module with Embedded Machine Learning Core and Qvar Electrostatic Sensor\u2014STMicroelectronics. Available online: https:\/\/www.st.com\/en\/mems-and-sensors\/lsm6dsv16x.html."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1177\/155005940503600411","article-title":"Full-band EEG (FbEEG): A new standard for clinical electroencephalography","volume":"36","author":"Vanhatalo","year":"2005","journal-title":"Clin. EEG Neurosci."},{"key":"ref_34","unstructured":"(2022, January 31). IEC 60601-2-47:2012, Available online: https:\/\/webstore.iec.ch\/publication\/2666."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.jelectrocard.2020.02.005","article-title":"Comparison of electrocardiogram quality and clinical interpretations using prepositioned ECG electrodes and conventional individual electrodes","volume":"59","author":"Roy","year":"2020","journal-title":"J. Electrocardiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"929","DOI":"10.3109\/03639049709148697","article-title":"Oral controlled-release dosage forms. I. Cellulose ether polymers in hydrophilic matrices","volume":"23","author":"Salsa","year":"1997","journal-title":"Drug Dev. Ind. Pharm."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"128869","DOI":"10.1109\/ACCESS.2019.2939943","article-title":"An accurate QRS complex and P wave detection in ECG signals using complete ensemble empirical mode decomposition with adaptive noise approach","volume":"7","author":"Hossain","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhidong, Z., Yi, L., and Qing, L. (2011). Adaptive Noise Removal of ECG Signal Based on Ensemble Empirical Mode Decomposition, IntechOpen.","DOI":"10.5772\/16263"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cai, Z., Li, J., Zhang, X., Shen, Q., Murray, A., and Liu, C. (2019, January 8\u201311). How accurate are ECG parameters from wearable single-lead ECG system for 24-hours monitoring. Proceedings of the 2019 Computing in Cardiology (CinC), Singapore.","DOI":"10.22489\/CinC.2019.187"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jelectrocard.2021.02.011","article-title":"Usefulness, pitfalls and interpretation of handheld single-lead electrocardiograms","volume":"66","author":"Witvliet","year":"2021","journal-title":"J. Electrocardiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"14839","DOI":"10.3390\/s131114839","article-title":"EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition","volume":"13","author":"Zeng","year":"2013","journal-title":"Sensors"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","article-title":"An information-maximization approach to blind separation and blind deconvolution","volume":"7","author":"Bell","year":"1995","journal-title":"Neural Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"606719","DOI":"10.3389\/fnrgo.2020.606719","article-title":"Electro-encephalography and electro-oculography in aeronautics: A review over the last decade (2010\u20132020)","volume":"1","author":"Belkhiria","year":"2020","journal-title":"Front. Neuroergon."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/B978-0-444-64032-1.00033-3","article-title":"The electrooculogram","volume":"Volume 160","author":"Creel","year":"2019","journal-title":"Handbook of Clinical Neurology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"R1237","DOI":"10.1016\/j.cub.2017.10.026","article-title":"The biology of REM sleep","volume":"27","author":"Peever","year":"2017","journal-title":"Curr. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1016\/j.neuroimage.2006.09.024","article-title":"10\/20, 10\/10, and 10\/5 systems revisited: Their validity as relative head-surface-based positioning systems","volume":"34","author":"Jurcak","year":"2007","journal-title":"Neuroimage"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2566\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:44:25Z","timestamp":1760136265000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,27]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072566"],"URL":"https:\/\/doi.org\/10.3390\/s22072566","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,27]]}}}