Wearable Photoplethysmography Research Articles

Systematic Review on Fabrication, Properties, and Applications of Advanced Materials in Wearable Photoplethysmography Sensors

AbstractPhotoplethysmography (PPG) technology enables the measurement of multiple physiological and psychological parameters with low‐cost wearable sensors and is reshaping modern healthcare. Advanced materials play a vital role in improving reliability and accuracy of PPG sensors. Recently, various advanced materials have been explored to optimize PPG sensor design, while some challenges exist toward large‐scale validation and mass production. This paper focuses on advanced materials applied in the photodetectors, light sources, and circuits of PPG sensors. The materials are categorized into four groups: inorganic, organic, nanomaterials, and hybrid materials. The properties and fabrication processes are summarized. Other technical details including the mode of operation, measurement sites, testing, and validation are discussed. The merits and limitations of the state of the art are highlighted to provide some suggestions for the future development of PPG sensors based on advanced materials.

Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography.

Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection. In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices. Our method achieved 70.17% accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor. The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.

Multiscale Bidirectional Temporal Convolutional Network for Sleep Apnea Detection Based on Wearable Photoplethysmography Bracelet.

Sleep apnea syndrome (SAS), which can lead to a range of Cardiopulmonary diseases, is a common chronic sleep disorder. The unobtrusive detection based on wearable devices is helpful for early diagnosis and treatment of SAS. To this end, this paper presents a method based on a one-dimensional multi-scale bidirectional temporal convolutional neural network (1D-MsBiTCNet) and two model performance optimization techniques, i.e., regularized dropout (RD) and logit adjustment (LA). Among them, 1D-MsBiTCNet has outstanding capabilities in both feature extraction and temporal dependence representation. RD and LA play an effective role in solving the overfitting problem of model training and the class imbalance problem of the dataset, respectively. The proposed model was trained and tested on a photoplethysmography (PPG) dataset (including data from 92 subjects) collected from commercial wearable bracelets. On this dataset, our method achieved accuracy, sensitivity and specificity of 82.76%, 71.58%, 86.74% for per-segment detection, and 97.83%, 88.89%, 100.00% for per-recording severe SAS detection. For the precise quantification of apnea-hypopnea index (AHI), our method achieved a mean absolute error of 5.44 between the predicted AHI and the ground truth AHI. The experimental results show that our proposed method has an outstanding performance and can provide a methodological reference for large-scale SAS automatic detection.

Machine learning framework for Inter-Beat Interval estimation using wearable Photoplethysmography sensors

Accurate estimation of Heart Rate (HR) is crucial for various health and fitness tracking applications in wearable devices. Among the existing techniques, those based on Photoplethysmography (PPG) signals have been extensively explored for continuous HR monitoring. In this paper, we introduce a new framework that utilizes machine learning methods to estimate Inter-Beat Interval (IBI) from wrist PPG signals. The primary objective of the proposed methods is to enhance the accuracy of IBI estimates while ensuring their feasibility for wearable devices, considering typical computational power, and memory constraints. This is achieved through two combined approaches. The first method involves optimizing a set of parameters associated with the interpolation of the systolic peaks. During the training stage of this method, we optimize the weights applied to the neighboring samples of the systolic peaks while minimizing the error between the fine-tuned peak positions and their corresponding ground truth. The second method is related to a supervised machine learning approach that handles the R-peak detection as a classification task. This allows us to accurately detect systolic peaks in PPG signals by classifying candidate peaks as true or false systolic peaks. To evaluate the effectiveness of our proposed methods, we conducted experiments on a dataset collected and carefully organized to this end. It consists of PPG and Electrocardiogram (ECG) signals obtained from 46 volunteers, including individuals with normal sinus rhythm, atrial fibrillation, and other non-specified cardiac arrhythmias. Comparing our framework with other state-of-the-art methods, we observed excellent performance, with lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) in IBI estimation. The proposed approach also achieved higher levels of Precision and Recall in terms of the detection of systolic peaks.

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation.

Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%. Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.

Enabling low-drift flexible perovskite photodetectors by electrical modulation for wearable health monitoring and weak light imaging

Metal halide perovskites are promising for next-generation flexible photodetectors owing to their low-temperature solution processability, mechanical flexibility, and excellent photoelectric properties. However, the defects and notorious ion migration in polycrystalline metal halide perovskites often lead to high and unstable dark current, thus deteriorating their detection limit and long-term operations. Here, we propose an electrical field modulation strategy to significantly reduce the dark current of metal halide perovskites-based flexible photodetector more than 1000 times (from ~5 nA to ~5 pA). Meanwhile, ion migration in metal halide perovskites is effectively suppressed, and the metal halide perovskites-based flexible photodetector shows a long-term continuous operational stability (~8000 s) with low signal drift (~4.2 × 10−4 pA per second) and ultralow dark current drift (~1.3 × 10−5 pA per second). Benefitting from the electrical modulation strategy, a high signal-to-noise ratio wearable photoplethysmography sensor and an active-matrix photodetector array for weak light imaging are successfully demonstrated. This work offers a universal strategy to improve the performance of metal halide perovskites for wearable flexible photodetector and image sensor applications.

Towards PPG-based anger detection for emotion regulation

BackgroundAnger dyscontrol is a common issue after traumatic brain injury (TBI). With the growth of wearable physiological sensors, there is new potential to facilitate the rehabilitation of such anger in the context of daily life. This potential, however, depends on how well physiological markers can distinguish changing emotional states and for such markers to generalize to real-world settings. Our study explores how wearable photoplethysmography (PPG), one of the most widely available physiological sensors, could be used detect anger within a heterogeneous population.MethodsThis study collected the TRIEP (Toronto Rehabilitation Institute Emotion-Physiology) dataset, which comprised of 32 individuals (10 TBI), exposed to a variety of elicitation material (film, pictures, self-statements, personal recall), over two day sessions. This complex dataset allowed for exploration into how the emotion-PPG relationship varied over changes in individuals, endogenous/exogenous drivers of emotion, and day-to-day differences. A multi-stage analysis was conducted looking at: (1) times-series visual clustering, (2) discriminative time-interval features of anger, and (3) out-of-sample anger classification.ResultsCharacteristics of PPG are largely dominated by inter-subject (between individuals) differences first, then intra-subject (day-to-day) changes, before differentiation into emotion. Both TBI and non-TBI individuals showed evidence of linear separable features that could differentiate anger from non-anger classes within time-interval analysis. However, what is more challenging is that these separable features for anger have various degrees of stability across individuals and days.ConclusionThis work highlights how there are contextual, non-stationary challenges to the emotion-physiology relationship that must be accounted for before emotion regulation technology can perform in real-world scenarios. It also affirms the need for a larger breadth of emotional sampling when building classification models.

Smartphone Photoplethysmography Pulse Rate Covaries With Stress and Anxiety During a Digital Acute Social Stressor.

Heart rate is a transdiagnostic correlate of affective states and the stress diathesis model of health. Although most psychophysiological research has been conducted in laboratory environments, recent technological advances have provided the opportunity to index pulse rate dynamics in real-world environments with commercially available mobile health and wearable photoplethysmography (PPG) sensors that allow for improved ecologically validity of psychophysiological research. Unfortunately, adoption of wearable devices is unevenly distributed across important demographic characteristics, including socioeconomic status, education, and age, making it difficult to collect pulse rate dynamics in diverse populations. Therefore, there is a need to democratize mobile health PPG research by harnessing more widely adopted smartphone-based PPG to both promote inclusivity and examine whether smartphone-based PPG can predict concurrent affective states. In the current preregistered study with open data and code, we examined the covariation of smartphone-based PPG and self-reported stress and anxiety during an online variant of the Trier Social Stress Test, as well as prospective relationships between PPG and future perceptions of stress and anxiety in a sample of 102 university students. Smartphone-based PPG significantly covaries with self-reported stress and anxiety during acute digital social stressors. PPG pulse rate was significantly associated with concurrent self-reported stress and anxiety ( b = 0.44, p = .018) as well as prospective stress and anxiety at the subsequent time points, although the strength of this association diminished the farther away pulse rate got from self-reported stress and anxiety (lag 1 model: b = 0.42, p = .024; lag 2 model: b = 0.38, p = .044). These findings indicate that PPG provides a proximal measure of the physiological correlates of stress and anxiety. Smartphone-based PPG can be used as an inclusive method for diverse populations to index pulse rate in remote digital study designs.

Stretchable Photodiodes with Polymer-Engineered Semiconductor Nanowires for Wearable Photoplethysmography.

Healthcare systems worldwide have been stressed to provide sufficient resources to serve the increasing and aging population in our society. The situation became more challenging at the time of pandemic. Technology advancement, especially the adoption of wearable health monitoring devices, has provided an important supplement to current clinical equipment. Most health monitoring devices are rigid, however, human tissues are soft. Such a difference has prohibited intimate contact between the two and jeopardized wearing comfortableness, which hurdles measurement accuracy especially during longtime usage. Here, we report a soft and stretchable photodiode that can conformally adhere onto the human body without any pressure and measure cardiovascular variables for an extended period with higher reliability than commercial devices. The photodiode used a composite light absorber consisting of an organic bulk heterojunction embedded into an elastic polymer matrix. It is discovered that the elastic polymer matrix not only improves the morphology of the bulk heterojunction for obtaining the desired mechanical properties but also alters its electronic band structure and improves the electrical properties that lead to a reduced dark current and enhanced photovoltage in the stretchable photodiode. The work has demonstrated high fidelity measurements and longtime monitoring of heat rate variability and oxygen saturation, potentially enabling next-generation wearable photoplethysmography devices for point-of-care diagnosis of cardiovascular diseases in a more accessible and affordable way.

A wearable photoplethysmography sensor for non-invasive equine heart rate monitoring

In recent years, advancement in biomedical sensors has led to increasingly accurate and accessible biological vital sign collection in nontraditional and noncontrolled application domains. Despite much progress, the implementation within the veterinary medicine field is still limited, which, in part, is due to a lack of rapid, robust, and easy-to-use sensors. To tackle this challenge this work presents a compact and wearable photoplethysmography (PPG) sensor for monitoring equine heart rates (HRs) from the ventral midline near the base of the tail. The PPG waveforms were robustly collected from a group of 50 equine subjects with various age, sex, and breed and then used for HR calculation. The system achieved an accuracy of 94% for HR calculation from the subjects at rest compared to the ground truth HRs acquired from the same subjects by a stethoscope. Statistical analysis on the collected data was conducted to further investigate the robustness of the HR calculations in different age, sex, and breed demographics. This work demonstrates that the PPG-based sensor for HR monitoring shows great potential of being a complementary tool in veterinary medicine for promoting furtherance of the animal health.

ASSESSING WORKFLOW, SATISFACTION, AND POTENTIAL COST REDUCTION WHEN USING A CUFFLESS AMBULATORY BLOOD PRESSURE MONITOR

Objective: Hypertension is a major risk factor for cardiovascular diseases. Ambulatory blood pressure monitoring (ABPM) is used for the diagnosis of hypertension, however, currently-used devices have several drawbacks complicating their use and accuracy. We tested the workflow of a wearable photoplethysmography (PPG)-based cuffless device when compared to the traditional cuff-based ABPM device. Design and method: Patients were recruited in a single community clinic and randomly assigned to either a cuff-based or a PPG-based 24-hour ABPM test. An assessment team recorded the time to complete each workflow component with each device. Questionnaires on user experience and workflow were filled by physicians, nurses, and recruited patients. Results: Workflow assessment of onboarding patients showed that it was faster with the PPG-based device than with the cuff-based device (301.17±58.39 seconds vs 1185±172.34 seconds on average, respectively). The PPG-based device received a higher score in all aspects of usability. The direct manpower cost reduction was calculated as 108,000 USD per year for 4,500 tests. Conclusions: The PPG-based device has several advantages when compared to the traditional cuff-based ABPM device, potentially increasing both patient and medical staff satisfaction, and as a result, increasing compliance. This could help improve the diagnosis and treatment of hypertension in the community.

Feasibility of Wearable PPG for Simultaneous Hand Gesture and Force Level Classification

Simultaneous decoding of hand gestures and force levels has the potential to unlock a broad range of home-based application scenarios in post-stroke rehabilitation. However, current wearable approaches cannot struggle to balance wearability and functionality well. We thus propose and investigate the feasibility of wearable photoplethysmography (PPG) for simultaneous hand gesture and force level classification. A customized wristband embedded with green, red, and infrared light PPG sensors was developed. Fourteen subjects wore this wristband and performed four pinching gestures at 10%, 40%, and 70% maximum volunteer contraction (MVC) force levels. The results showed that the proposed method achieved satisfactory recognition accuracy, and the combination of three reflected light PPG signals was more accurate than any single signal or two-signal combinations. Force level classification accuracy within each hand gesture was less robust to the variety of hand gestures, and hand gesture classification accuracy at different force levels decreased as the force level increased. In addition, the proposed method still achieved reasonable recognition accuracy, with a relatively small amount of training data, potentially saving time in practical applications. These results demonstrate the feasibility of a wearable, low-cost PPG approach for simultaneous hand gesture and force level classification, which could facilitate more widespread hand mobility and strength training both inside and outside the clinic.

The Accuracy of Wrist-Worn Photoplethysmogram-Measured Heart and Respiratory Rates in Abdominal Surgery Patients: Observational Prospective Clinical Validation Study.

Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established. This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients. The accuracy of the wrist-worn PPG sensor was assessed in 62 post-abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m2). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy. Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis. The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained. ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127.

On-Device Multi-Level Signal Quality Aware Compression for Energy-Efficient Wearable PPG Sensing

On-device computing in biomedical sensors has become attractive for developing wearable health monitoring systems. The challenge is to make a compromise between the latency and complexity in a resource-constrained implementation. This article describes an on-device implementation of multi-level signal quality aware and quality controlled compression (MSQQCC) that enhances the compression factor while preserving the clinical features in a wearable photoplethysmography (PPG) sensing application. The multi-level quality assessment (QA) provides three eligible PPG qualities, viz., “excellent,” “good,” and “average,” based on which corresponding upper limits are set for further compression using a discrete wavelet transform, while the “corrupted” segments are discarded. A pretrained multilayer perceptron neural network (MLPNN) provides the optimal quantization level of coefficients. The residual data is separately compressed using an autoencoder (AE). MSQQCC was evaluated with 300 min of PPG data from three public data sets and 110 min of data collected at a laboratory. The end-to-end pipeline was implemented in a standalone system with an ARM Cortex A53 controller, requiring 35.51 kB of memory and 1.8 s latency to process 4 s PPG data. The on-device QA achieved 98.45% overall accuracy (Ac), which outperforms published works on PPG QA. The mean deviation of PPG clinical features by 5%, with overall compression ratio (CR) and percentage root mean squared difference (PRD) were 40.85 and 2.52, which are superior to many published works. Real-time transmission over Bluetooth shows an improvement of energy efficiency by a significant factor and a 34% extended battery life for wearable PPG sensors. The results are encouraging for the adoption of MSQQCC in wearable biomedical health monitoring.

Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets.

Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.

Brief research report: Evaluation of photoplethysmographic heart rate monitoring for sheep under heat-stressed conditions

The objective of this study was to investigate the accuracy of a wearable photoplethysmography (PPG) sensor in monitoring heart rate (HR) of sheep housed in high-temperature environments. We hypothesized that the PPG sensor would be capable of differentiating low, normal, and high HR, but would struggle to produce exact HR estimates. The sensor was open source and comprised of a microprocessor (SparkFun® ThingPlus), a photoplethysmography sensor (SparkFun® MAX30101 & MAX32664), and a data storage module (SD Card 16GB), all sewn into a nylon collar with hook-and-loop closure. Sheep (n=4) were divided into 2 groups and exposed to different thermal environments in a cross-over design. The collar was placed around the neck of the sheep during the data collection phase and the manual HR were collected twice a day using a stethoscope. Precision and accuracy of numeric heart rate estimates were analyzed in R software using Pearson correlation and root mean squared prediction errors. Random forest regression was used to classify HR based on low, medium, and high to determine opportunities to leverage the PPG sensors for HR classification. Sensitivity, specificity, and accuracy were measured to evaluate the classification approach. Our results indicated that the PPG-based sensor measured sheep HR with poor accuracy and with higher average estimates in comparison with manually measured with a stethoscope. Categorical classification of HR was also poor, with accuracies ranging from 32% to 49%. Additional work is needed focusing on data analytics, and signal optimization to further rely on PPG sensors for accurately measuring HR in sheep.

Multication Tin‐Lead Perovskite Photodiodes with Engineered Lattice Strain for Ultrasensitive Broadband Photodetection

AbstractTin‐lead alloyed perovskites with broadband absorption over 1000 nm, are suitable for UV‐vis‐NIR light detection that rivals the crystalline silicon semiconductors. These alloyed perovskites usually experience local lattice distortion and inherent lattice strain, which affect the electronic band structures and introduce detrimental trap states subsequently. To date, the lattice strain and its impact on the physical properties of tin‐lead alloyed perovskites are still less understood. Herein, it is shown that the lattice strain contributes substantially to the band edge electronic structure of multication tin‐lead alloyed perovskites by tilting the metal halide octahedral configuration. It has been found that the release of lattice strain heavily affects the band edge electronic structure of tin‐lead alloyed perovskites by reducing the band tail and shallow trap states as well as narrowing the bandgap simultaneously. Leveraging these insights, the optimized photodetector improves nearly 10‐fold the light detection limit to 0.49 pW cm−2, achieving a record detectivity of >6 × 1012 Jones in the near‐infrared range. The photodetectors on flexible substrates can be further integrated into an amplifier‐free wearable photoplethysmography device for real‐time heart rate and blood oxygen saturation monitoring, showing the promising potential for real applications.

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability.

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

Opto-ultrasound biosensor for wearable and mobile devices: realization with a transparent ultrasound transducer.

Mobile and wearable healthcare electronics are widely used for measuring bio-signals using various fusion sensors that employ photoplethysmograms, cameras, microphones, ultrasound (US) sensors, and accelerometers. However, the consumer demand for small form factors has significantly increased as the integration of multiple sensors is difficult in small mobile or wearable devices. This study proposes two novel opto-US sensors, namely (1) a wearable photoplethysmography (PPG)-US device and (2) a PPG sensor built-in mobile smartphone with a US sensor, seamlessly integrated using a transparent ultrasound transducer (TUT). The TUT exhibits a center frequency of 6 MHz with a 50% bandwidth and 82% optical transparency in visible and near-infrared regions. We developed an integrated wearable PPG-US device to demonstrate its feasibility and coupled the TUT sensor with a smartphone. We measured the heart rates optically and acoustically in human subjects and quantified the oxygen saturation optically by passing light through the TUT. The proposed proof-of-concept is a novel sensor fusion for mobile and wearable devices that require a small form factor and aim to improve digital healthcare. The results of this study can form the basis for innovative developments in sensor-based high-tech industrial applications, such as automobiles, robots, and drones, in addition to healthcare applications.

Cost-effectiveness of Screening for Atrial Fibrillation Using Wearable Devices

Undiagnosed atrial fibrillation (AF) is an important cause of stroke. Screening for AF using wrist-worn wearable devices may prevent strokes, but their cost-effectiveness is unknown. To evaluate the cost-effectiveness of contemporary AF screening strategies, particularly wrist-worn wearable devices. This economic evaluation used a microsimulation decision-analytic model and was conducted from September 8, 2020, to May 23, 2022, comprising 30 million simulated individuals with an age, sex, and comorbidity profile matching the US population aged 65 years or older. Eight AF screening strategies, with 6 using wrist-worn wearable devices (watch or band photoplethysmography, with or without watch or band electrocardiography) and 2 using traditional modalities (ie, pulse palpation and 12-lead electrocardiogram) vs no screening. The primary outcome was the incremental cost-effectiveness ratio, defined as US dollars per quality-adjusted life-year (QALY). Secondary measures included rates of stroke and major bleeding. In the base case analysis of this model, the mean (SD) age was 72.5 (7.5) years, and 50% of the individuals were women. All 6 screening strategies using wrist-worn wearable devices were estimated to be more effective than no screening (range of QALYs gained vs no screening, 226-957 per 100 000 individuals) and were associated with greater relative benefit than screening using traditional modalities (range of QALYs gained vs no screening, -116 to 93 per 100 000 individuals). Compared with no screening, screening using wrist-worn wearable devices was associated with a reduction in stroke incidence by 20 to 23 per 100 000 person-years but an increase in major bleeding by 20 to 44 per 100 000 person-years. The overall preferred strategy was wearable photoplethysmography, followed conditionally by wearable electrocardiography with patch monitor confirmation, which had an incremental cost-effectiveness ratio of $57 894 per QALY, meeting the acceptability threshold of $100 000 per QALY. The cost-effectiveness of screening was consistent across multiple scenarios, including strata of sex, screening at earlier ages (eg, ≥50 years), and with variation in the association of anticoagulation with risk of stroke in the setting of screening-detected AF. This economic evaluation of AF screening using a microsimulation decision-analytic model suggests that screening using wearable devices is cost-effective compared with either no screening or AF screening using traditional methods.