Electrocardiogram (ECG) is one of the major physiological vital signs and an effective monitoring method for patients with cardiovascular diseases. However, existing ECG recordings require a galvanic body contact, which is unpractical in daily life. This paper presents the design of an ECG chip that facilitates non-contact ECG recording through capacitive coupling. With the input impedance boosting techniques, as well as an active driven-right-leg (DRL) which boosts common-mode rejection ratio (CMRR) to 70 dB, the single-ended capacitive feedback active electrode (AE) achieves ultra-high input impedance of 400 GΩ (< 0.5 Hz), a large common-mode (CM) interference tolerance (2.8 Vpp), and a high linear-input-range (220 mVpp). Implemented in 0.18 μm 5V CMOS process, the prototype occupies an area of 1.23 mm2, and consumes 18 μA and 13 μA for the AE and DRL respectively. Real life non-contact capacitively coupled ECG acquisition has been demonstrated, obtaining ECG waves and heart rate in the presence of motion artifacts as well as ambient interference.

Remote home monitoring via wearables is rapidly gaining popularity, thanks to ever more complex vital signs recording and data analytics that are embedded into single devices. True system-on-chips (SoCs) push the envelope of power and form factor, while wearables are becoming more common-place, novel health sensing paradigms appear. Non-contact sensing technologies enable vital signs sensing without requiring any physical contact with the human body. This paper reviews existing multiple-modality bio-signal recording and processing SoCs, circuit techniques for individual bio-signal readout channels including electrocardiography (ECG), Electroencephalography (EEG), Bio-impedance (BioZ), photoplethysmography (PPG), and non-contact sensing with high user comfort. The stateof- the-art wearable sensor interfaces and systems together with future development trends are provided.

Sleep-related conditions require high-cost and low-comfort diagnosis at the hospital during one night or longer. To overcome this situation, this work aims to evaluate an unobtrusive monitoring technique for sleep apnea. This paper presents, for the first time, the evaluation of contactless capacitively-coupled electrocardiography (ccECG) signals for the extraction of sleep apnea features, together with a comparison of different signal quality indicators. A multichannel ccECG system is used to collect signals from 15 subjects in a sleep environment from different positions. Reference quality labels were assigned for every 30-s segment. Quality indicators were calculated, and their signal classification performance was evaluated. Features for the detection of sleep apnea were extracted from capacitive and reference signals. Sleep apnea features related to heart rate and heart rate variability achieved high similarity to the reference values, with p-values of 0.94 and 0.98, which is in line with the more than 95% beat-matching obtained. Features related to signal morphology presented lower similarity with the reference, although signal similarity metrics of correlation and coherence were relatively high. Quality-based automatic classification of the signals had a maximum accuracy of 91%. Best-performing quality indicators were based on template correlation and beat-detection. Results suggest that using unobtrusive cardiac signals for the automatic detection of sleep apnea can achieve similar performance as contact signals, and indicates clinical value of ccECG. Moreover, signal segments can automatically be classified by the proposed quality metrics as a pre-processing step. Including contactless respiration signals is likely to improve the performance and provide a complete unobtrusive cardiorespiratory monitoring solution; this is a promising alternative that will allow the screening of more patients with higher comfort, for a longer time, and at a reduced cost.

Redox balance—the equilibrium between oxidants and reductants—is a key modulator of a healthy gut and consequently overall well-being. Excess reactive species, resulting in oxidative stress, are linked to deleterious processes including inflammation and microbiome dysbiosis. However, a lack of suitable in vivo methods has restricted measurements of redox balance in the human gut. Here we report a miniaturized ingestible sensor that is equipped with an oxidation–reduction potential sensor, an electrochemical reference electrode and pH and temperature sensors. We preclinically validate our wireless gastrointestinal (GI) smart module (GISMO) in GI fluids and an animal model and report in-human measurements in 15 healthy individuals. Our high-temporal-resolution data, measured every 20 s, reveal consistent profiles from an oxidative environment in the stomach to a strongly reducing environment in the large intestine. This non-intrusive method has the potential to advance (GI) disease monitoring and offer insights into the gut microbiome.

Unobtrusive monitoring of drivers’ physiological parameters is a topic gaining interest, potentially allowing to improve the performance of safety systems to prevent accidents, as well as to improve the driver’s experience or provide health-related services. In this article, two unobtrusive sensing techniques are evaluated: capacitively coupled sensing of the electrocardiogram and respiration, and radar-based sensing of heartbeat and respiration. A challenge for use of these techniques in vehicles are the vibrations and other disturbances that occur in vehicles to which they are inherently more sensitive than contact-based sensors. In this work, optimized sensor architectures and signal processing techniques are proposed that significantly improve the robustness to artefacts. Experimental results, conducted under real driving conditions on public roads, demonstrate the feasibility of the proposed approach. R peak sensitivities and positive predictivities higher than 98% both in highway and city traffic, heart rate mean absolute error of 1.02 bpm resp. 2.06 bpm in highway and city traffic and individual beat R-R interval 95% percentile error within ±27.3 ms are demonstrated. The radar experimental results show that respiration can be measured while driving and heartbeat can be recovered from vibration noise using an accelerometer-based motion reduction algorithm.

Unobtrusive measurement of vital signs is a research topic of increasing interest, both due to the advances in different technologies and the need for a more patient-centered healthcare. A limitation of such measurements is their sensitivity to motion artifacts, which challenges their use in daily life activities. In this paper, a novel system for the simultaneous acquisition of multichannel capacitively coupled ECG (ccECG) and radar signals is presented, which allows for respiration rate and heart rate (HR) extractions. Furthermore, a combination of signal quality and confidence indicators is proposed, which serves as a first step towards sensor fusion for an increased accuracy and time coverage of HR monitoring. After applying the proposed quality estimation algorithms, sensor fusion methods were performed including quality-based ccECG channel selection, confidence-based radar HR source selection, and Bayesian fusion. Both the system and the proposed algorithms were tested in a total of 125 min of measurements obtained from five different volunteers performing normal office work. An improvement in time coverage (defined as the time in which the HR error is lower than 3 bpm) from 2% to 32.5% was obtained for radar signals with the proposed confidence-based algorithm; the same metric for ccECG increased from 52.2% to 82.1% when automatically selecting the periods with the availability of good quality ccECG signals, while achieving 35.7% coverage in the remaining periods, by using one of the proposed Bayesian fusion methods; errors in each case were reduced accordingly, and a clear division of two sets of signals with different (increased) coverage levels was achieved.