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Advancements in integrated circuit (IC) technology have accelerated the miniaturization of body-worn sensors and systems, enabling long-term health monitoring. Wearable electrocardiogram (ECG), finger photoplethysmogram (PPG), and wrist-worn PPG have shown great success and significantly improved life quality. Chest-based PPG has the potential to extract multiple vital signs but requires ultra-high dynamic range (DR) IC to read out the small PPG signal among large respiration and artifacts inherent in daily life. This paper presents a dedicated high DR system for wearable chest PPG applications with a small form factor. The whole measurement system is integrated on a 20 cm2 PCB board. We have formulated a comprehensive evaluation protocol to validate the system with on-body chest PPG measurement in the workspace environment. First, chest PPG data was obtained from 6 adults and compared to data from a standard ECG patch. This system showed an average absolute deviation (AD) of 0.41 beats per minute, achieving > 99.53% heart rate (HR) accuracy. Second, chest PPG was recorded and compared to conventional PPG finger clip and PPG wristband, also showing > 98.6% HR matching and an absolute deviation in the standard deviation of NN intervals (SDNN) of < 12.8 ms for HRV monitoring within the protocol. Moreover, it successfully derives other vital parameters such as respiration rate and blood oxygen level (SpO2), showing the advancement among all these three reference modalities. This system can pave the way for new application areas, such as chest patches, to monitor chronic heart and respiratory diseases., (© 2024 The Authors.)

Background: Chronic stress is increasing in prevalence and is associated with several physical and mental disorders. Although it is proven that acute stress changes physiology, much less is known about the relationship between physiology and long-term stress. Continuous measurement of vital signs in daily life and chronic stress detection algorithms could serve this purpose. For this, it is paramount to model the effects of chronic stress on human physiology and include other cofounders, such as demographics, enabling the enrichment of a population-wide approach with individual variations., Objective: The main objectives of this study were to investigate the effect of chronic stress on heart rate (HR) over time while correcting for weekdays versus weekends and to test a possible modulation effect by gender and age in a healthy cohort., Methods: Throughout 2016 and 2017, healthy employees of technology companies were asked to participate in a 5-day observation stress study. They were required to wear two wearables, of which one included an electrocardiogram sensor. The derived HR was averaged per hour and served as an output for a mixed design model including a trigonometric fit over time with four harmonics (periods of 24, 12, 8, and 6 hours), gender, age, whether it was a workday or weekend day, and a chronic stress score derived from the Perceived Stress Scale (PSS) as predictors., Results: The study included 328 subjects, of which 142 were female and 186 were male participants, with a mean age of 38.9 (SD 10.2) years and a mean PSS score of 13.7 (SD 6.0). As main effects, gender (χ 2 1 =24.02, P<.001); the hour of the day (χ 2 1 =73.22, P<.001); the circadian harmonic (χ 2 2 =284.4, P<.001); and the harmonic over 12 hours (χ 2 2 =242.1, P<.001), over 8 hours (χ 2 2 =23.78, P<.001), and over 6 hours (χ 2 2 =82.96, P<.001) had a significant effect on HR. Two three-way interaction effects were found. The interaction of age, whether it was a workday or weekend day, and the circadian harmonic over time were significantly correlated with HR (χ 2 2 =7.13, P=.03), as well as the interaction of gender, PSS score, and the circadian harmonic over time (χ 2 2 =7.59, P=.02)., Conclusions: The results show a relationship between HR and the three-way interaction of chronic stress, gender, and the circadian harmonic. The modulation by gender might be related to evolution-based energy utilization strategies, as suggested in related literature studies. More research, including daily cortisol assessment, longer recordings, and a wider population, should be performed to confirm this interpretation. This would enable the development of more complete and personalized models of chronic stress., (©Alex Wilhelmus Jacobus van Kraaij, Giuseppina Schiavone, Erika Lutin, Stephan Claes, Chris Van Hoof. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.09.2020.)

Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) and two-layer long short-term network (LSTM) was successfully reported for estimating HR from MA-induced PPG signals. However, such a network topology with large number of parameters presents a challenge, towards low-complexity hardware implementation aimed at on-node processing. In this paper, we demonstrate a fully binarized network (bCorNET) topology and its corresponding algorithm-to-architecture mapping and energy-efficient implementation for HR estimation. The proposed framework achieves a MAE of 6.67 ± 5.49 bpm when evaluated on 22 IEEE SPC subjects. The design, synthesized with ST65 nm technology library achieving 3 GOPS @ 1 MHz, consumes 56.1 μJ per window with occupied 1634K NAND2 equivalent cell area and had a latency of 32 ms when estimating HR every 2 s from PPG signals.

Advancements in wireless sensor networks (WSN) technology and miniaturization of wearable sensors have enabled long-term continuous pervasive biomedical signal monitoring. Wrist-worn photoplethysmography (PPG) sensors have gained popularity given their form factor. However the signal quality suffers due to motion artifacts when used in ambulatory settings, making vital parameter estimation a challenging task. In this paper, we present a novel deep learning framework, BioTranslator, for computing the instantaneous heart rate (IHR), using wrist-worn PPG signals collected during physical activity. Using one-dimensional Convolution-Deconvolution Network, we translate a single channel PPG signal to an electrocardiogram(ECG)-like time series signal, from which relevant R-peak information can be inferred enabling IHR measures. The proposed network configuration was evaluated on 12 subjects of the TROIKA dataset, involved in physical activity. The proposed network identifies 92.8% of R-peaks, besides achieving a mean absolute error of 51±6.3ms with respect to reference ECG-derived IHR.

Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram, suffers from motion artifacts inherent in daily life. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only a wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-term memory layers, followed by a dense output layer for modeling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: regression layer-having a single neuron to predict HR; classification layer-two neurons that identify a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47 ± 3.37 beats per minute for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

A continuous wave Doppler radar, operating as a phase-locked-loop in phase demodulator configuration, is proposed and in vivo demonstrated for noncontact vital signs monitoring. The radar architecture exhibits a unique precision in tracking the phase modulation caused by human cardiopulmonary activity from which heartbeat and respiration can simultaneously be extracted. The single mixer architecture is immune to the null point and does not require small-angle approximation conditions, which distinguishes it from pre-existing other approaches. This enables the proposed radar to behave highly linear, with very precise detection of phase modulations induced by any kind of movement, independently from amplitude and speed. After simulations and technical tests to validate functionality and safety of the proposed architecture, a practical setup was demonstrated on human volunteers. Wavelet independent component analysis was applied to successfully retrieve respiratory and heart rate information from the radar baseband signal.

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

This paper proposes a real time digitally assisted analog motion artifact reduction ASIC with ECG measurement simultaneously. It features one ECG monitoring and in- and quad-phase electrode-skin impedance measurement, which are used to estimate motion artifacts. The implemented ASIC is capable of actual motion artifact reduction in the analog domain before final amplification.

Heart rate monitoring has been a significant topic of interest in the areas of healthcare, sports and gaming. Compared to locations such as the neck, ear, or chest, the wrist is a convenient measurement point, as the measurement technology can be integrated into a wristwatch. However, key technical challenges exist, namely a small physiological SNR and large disturbances due to motion artifact. This paper reports early results on a packaging concept to monitor the heartrate during rest and motion using off-the-shelf piezoelectric PVDF film sensors. Evaluation has shown good results at rest and unsatisfactory results during motion. Results from this investigation will nonetheless be used as input for the development of a wrist-based heartrate monitor which could function during activities such as running, walking or typing on a keyboard.