Wireless Wearable Bio Signal Monitor With Automated Motion Artifact Rejection Using Accelerometer Dual Channels

1. Biomedical Energy-Efficient Electronics Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

Wearable devices have made significant progress in the field of health care, fitness, and diagnosis over the past few decades. Unobtrusive, user-friendly, and long-term usable fully integrated wearable systems enable healthcare devices to monitor the human body condition continuously and to give feedback to users ( Dias and Paulo Silva Cunha, 2018 ). The advances of application-specific integrated circuit (ASIC) technology reduce the size of electrical devices, thereby increasing the availability of small-size devices in everyday life ( Yin and Ghovanloo, 2007 ; Zou et al., 2008 ). In addition, recent substantial developments in computation algorithms and wide-bandwidth wireless communication technologies foster the everyday use of wearable devices ( Aun et al., 2017 ; Beniczky et al., 2020 ). Especially in 2020, the World Health Organization (WHO) proclaims the COVID-19 pandemic, which increases the global demand for in-home patient monitoring based on the temperature, respiration rate, and blood oxygen content ( Chamola et al., 2020 ; Hedayatipour and Mcfarlane, 2020 ). To get through this era, many researchers are currently proposing wearable biosensors as a solution to an epidemic prevention system against COVID-19 ( Seo et al., 2020 ; Shan et al., 2020 ; Zhao et al., 2021 ; LifeSignals, 2021 ).

Figure 1 shows several kinds of the current wearable healthcare devices measuring various bio-signals such as electroencephalography (EEG), photoplethysmography (PPG), and electrocardiography (ECG). EEG devices in Figure 1A , mostly worn on a head, are utilized for attention training, sleep stage monitoring, machine controlling, and seizure detecting by analyzing human brain activity. PPG signals, especially used in the sports field to monitor the amount of exercise, are also collected with wearable devices spread widely on the body in the form of smartwatch, earphone, clothes, and patch as depicted in Figure 1B . Moreover, the heart rate (HR) or biochemical levels (e.g., glucose) can be analyzed as well by using smartphone-linked wearable devices so as to help diagnose diseases such as arrhythmia or diabetes in daily life, as shown in Figures 1C,D .

State-of-the-art wearable healthcare devices: (A) EEG monitoring headset ( EMOTIV, 2021 ; Cognionics, 2021 ; NeuroSky, 2021 ); (B) PPG devices in the form of earplug ( FreeWavz, 2021 ), watch ( Apple, 2021 ), band ( Humon, 2021 ), and socks ( Owlet, 2021 ); (C) heart rate monitoring device ( Wahoo Fitness, 2021 ; and (D) skin contact sweat patch ( Epicore Biosystems and Gatorade, 2021 ), continuous glucose monitoring system ( Dexcom, 2021 ), and insulin management system ( Insulet Corporation, 2021 ).

As the demand for continuous monitoring bio-signals in daily life greatly increases, many attempts have been made to implement long-term robust recording of ambulatory bio-signals. Especially, wearable EEG and PPG devices are spotlighted owing to wide applications and prominent user convenience. EEG can provide long-term real-time neuromonitoring in a safe and noninvasive manner. Moreover, compared with other bio-signals, EEG has been used for the broader field of applications, including neurological disorder management, emotional monitoring, and brain–computer communication. In addition, PPG has received great attention in the wearable healthcare market because PPG enables single spot acquisition with convenient light-based devices, and therefore possibly allows for daily life cardiovascular monitoring for the management of major adult diseases. The significance of PPG recently skyrockets owing to its possibility for the early detection of COVID-19 ( Guler et al., 2020 ).

However, unfortunately, the current wearable devices for EEG and PPG recording cannot completely avoid issues coming from motion artifacts. It is because motion artifacts typically are at least ten times greater in amplitude than bio-signals. Particularly, solving the artifact issues in EEG applications is a difficult task due to its smaller amplitude and nonstationary waveform than other bio-signals. For PPG monitoring, motion artifact caused by sensor displacement is a major signal distortion source in ambulatory conditions. In addition to motion artifacts, many kinds of physiological artifacts and electromagnetic interference are also challenging hurdles of accurate EEG and PPG measurement in daily life.

While various articles rigorously review artifact reduction schemes for EEG ( Xu et al., 2017 ; Jiang et al., 2019 ; Shad et al., 2020 ) and PPG ( Periyasamy et al., 2017 ; Biswas et al., 2019 ), this article covers cross-border EEG motion artifact removal schemes that can be utilized at each part of the entire acquisition system: from the recording front-end hardware to the processing algorithm software, emphasizing the necessity of a diversified approach on the artifact issues. Furthermore, in-ear EEG researches that are actively studied in recent years are carefully reviewed from a perspective of the motion artifact removal. While a standard for classification of PPG motion artifact compensation methods based on the dependency relationship between motion artifact and PPG signals is suggested, techniques using learning-based systems such as deep learning and support vector machine are also included as emerging strategies.

In the following sections, we review state-of-the-art solutions tackling various motion artifacts in EEG and PPG wearable devices. In Section 2 , the background of EEG and PPG including fundamentals, history, and research trends are studied. In Section 3 , methods to alleviate motion artifact from EEG are reviewed in the aspects of the electrodes, electric circuits, and signal processing. In Section 4 , PPG motion artifact compensation methods are presented with the relationship between PPG signal and motion artifact, followed by conclusion in Section 5 .

EEG and PPG are the most common bio-signals that are collected by wearable devices for research and/or clinical purposes. Therefore, in this section, the fundamentals of EEG and PPG are studied.

Electroencephalography (EEG) is the brain’s spontaneous electrical activity recorded on the scalp surface in a noninvasive fashion. It reflects the current flow during synchronized excitation of multiple pyramidal neurons in the cerebral cortex ( Silva and da Silva, 2005 ; Teplan, 2002 ). International Federation’s 10–20 system is used for standard electrode placement in human EEG recording ( Jasper, 1958 ). The amplitude of scalp-recorded brain wave is approximately 20–200 V and typically classified according to frequency: alpha (8–13 Hz), beta (14–30 Hz), gamma ( 30 Hz), theta (4–7 Hz), and delta ( 3.5 Hz) ( Webster, 2009 ). EEG provides useful information on human brain functions including cognition (alertness and cognitive engagement), disease (epileptic seizure and sleep disorder), and the effect of drugs ( Bickford, 1987 ).

Both applications and acquisition techniques of EEG have been significantly developed, as shown in Figure 2A . After Hans Berger (1873–1941) successfully obtained human EEG from the scalp, clinical EEG studies related especially on epilepsy were actively carried out by researchers including William G. Lennox (1884–1960) and Frederick Gibbs (1903–1999) ( Mecarelli, 2019 ). Recently, the range of applications is being extended to sleep research, brain–computer interfaces (BCI), neuromarketing, neuroprosthesis, augmented cognition, and neurofeedback ( Wolpaw et al., 2002 ; Xu and Zhong, 2018 ; Abiri et al., 2019 ). In addition to EEG applications, accurate and comfortable EEG acquisition techniques have been evolved by decreasing the size of EEG devices. Large EEG recording units should be located outside of recording sites. As such, a long wire connection between the recording unit and electrodes is mandatory. This results in various problems including motion artifact, limited mobility, cross-talk, and interference. However, recent small-form factor energy-efficient integrated circuit (IC) chips for an amplifier, an analog-to-digital converter (ADC), and a wireless transmitter allow for miniaturized battery-operated EEG units. Consequently, preparation process is much simplified, and long-term recording becomes more feasible ( Casson et al., 2008 ).

History of bio-signals from its first experiment to modern commercialized products: (A) EEG ( Blom and Anneveldt, 1982 ; g.tec, 2021 ; NeuroSky MindWave, 2021 ; Kappel, 2018 ) and (B) PPG ( Nellcor, 2021 ; Nonin Medical, 2021 ; Tamura et al., 2014 ; Poh et al., 2010 )

As EEG technology has advanced, researchers attempted to record EEG from ear canals, called in-ear EEG ( Looney et al., 2011 ; Kidmose et al., 2012 ). In order to assess the validity of in-ear EEG data, several well-known paradigms such as alpha attenuation response (AAR) and auditory steady-state response (ASSR) are widely utilized ( Kidmose et al., 2013 ; Mikkelsen et al., 2015 ). These paradigms confirm that temporal lobe EEG and in-ear EEG contain similar information. Furthermore, the method to create an in-ear EEG forward model has been developed, which enables mapping the brain sources to potentials in the ear ( Goverdovsky et al., 2017 ; Kappel et al., 2019a ). In near future, the in-ear EEG method is expected to analyze EEG and treat brain diseases through both sound and electrical stimulation in real time by adding in-ear EEG recording function into a currently used wireless earphone.