Search Results (50)

The advancement of sports science has heightened demands for precise monitoring of athletes’ technical movements, physiological status, and performance. Optical fiber sensing (OFS) technology, with its unique advantages including high sensitivity, immunity to electromagnetic interference, capability for distributed sensing, and strong biocompatibility, demonstrates significant application potential in sports science. This review systematically examines the technical principles, innovative breakthroughs, and practical application cases of optical fiber sensors in various domains: monitoring key human physiological parameters such as respiration, heart rate, and body temperature; capturing motion and analyzing movement covering muscle activity, joint angles, and gait; integrating within smart sports equipment and protective gear; and monitoring sports apparatus and environments. The value of OFS technology is further analyzed in areas including sports biomechanics analysis, training load monitoring, injury prevention, and rehabilitation optimization. Concurrently, current technical bottlenecks such as the need for enhanced sensitivity, advancements in flexible packaging technologies, cost control, system integration, and miniaturization are discussed. Future development trends involving the integration of OFS with artificial intelligence, the Internet of Things, and new materials are explored, aiming to provide a theoretical foundation for sports medicine and training optimization. Full article

This study proposes a novel heart rate estimation method that detects subtle cardiac-induced vibrations propagated through the cardiovascular system based on the ballistocardiography (BCG) principle, using a six-axis heart rate sensing device that integrates a three-axis accelerometer and a three-axis gyroscope. To validate the effectiveness of the proposed method, a comparative analysis was conducted against heart rate measurements obtained from photoplethysmography (PPG) sensors, which are widely used in conventional heart rate monitoring. Experiments were conducted on 20 adult participants, and frequency domain analysis was performed using different time windows of 30 s, 20 s, 8 s, and 4 s. The results showed that the 4 s window provided the highest accuracy in heart rate estimation, demonstrating that the proposed method can effectively capture fine cardiac-induced vibrations. This approach offers a significant advantage by utilizing inertial sensors commonly embedded in wearable devices for heart rate monitoring without the need for additional optical sensors. Compared to optical-based systems, the proposed method is more power-efficient and less affected by environmental factors such as ambient lighting conditions. The findings suggest that heart rate estimation using the six-axis heart rate sensing device presents a reliable, continuous, and non-invasive alternative for cardiovascular monitoring. Full article

The awareness of the importance of monitoring human vital signs has increased recently due to the outbreak of the COVID-19 pandemic. Non-invasive heart rate monitoring devices, in particular, have become some of the most popular tools for health monitoring. However, heart rate data alone are not enough to reflect the health of one’s cardiovascular function or arterial health. This growing interest has spurred research into developing high-fidelity non-invasive pulse waveform sensors. These sensors can provide valuable information such as data on blood pressure, arterial stiffness, and vascular aging from the pulse waveform. Among these sensors, optical fiber sensors (OFSs) stand out due to their remarkable properties, including resistance to electromagnetic interference, capability in monitoring multiple vital signals simultaneously, and biocompatibility. This paper reviews the latest advancements in using OFSs to measure human vital signs, with a focus on pulse waveform analysis. The various working mechanisms of OFSs and their performances in measuring the pulse waveform are discussed. In addition, we also address the challenges faced by OFSs in pulse waveform monitoring and explore the opportunities for future development. This technology shows great potential for both clinical and personal non-invasive pulse waveform monitoring applications. Full article

Wearables with optical sensors for heart rate (HR) measurement are widely available in the market. However, their accuracy in water is still underexplored. The aim of the present study was to test the agreement of two different devices for HR monitoring with a chest strap while swimming at different intensities. Twenty male and ten female subjects (mean 19.6 ± 0.7 years old, 173.3 ± 5.4 cm, and 67.1 ± 6.6 kg) performed an intermittent progressive protocol of 3 × 30 s tethered front crawl swimming followed by a 1 min rest period. A chest strap, a wristwatch, and a multi-site optical sensor placed at the temple were used simultaneously. A strong association, an excellent intra-class correlation, and a low mean bias were denoted (R² = 0.85, ICC = 0.94, b = −1) between HR_chest vs. HR_temple. Both indicators increased throughout the test, denoting an increase in accuracy from light to vigorous exercise intensity. HR_chest and HR_watch showed a moderate association for the whole test (R² = 0.23) but a weak association, a poor consistency, and a high mean bias stepwise (0.01 ≤ R² ≤ 0.06, 0.03 ≤ ICC ≤ 0.42, −48.1 ≤ b≤ −16.1). During swimming, the HR values from the temple showed a better agreement with the chest strap than those from the wristwatch. The temple reading accuracy might be enhanced by using the device during the dryland warm-up routine. Full article

A photoplethysmography (PPG) pulse in reflection mode represents the change in diffuse reflectance at the skin surface during a cardiac cycle and is commonly used in wearable devices to monitor heart rate. Commercial PPG sensors often rely on the reflectance signal from light sources at two different wavelength regions, green, such as λ = 523 nm, and near infrared (NIR), such as λ = 945 nm. Early in vivo studies of wearable sensors showed that green light is more beneficial than NIR light in optimizing PPG sensitivity. This contradicts the common trends in the standard near infrared spectroscopy techniques, which rely on the long optical pathlengths at NIR wavelengths to achieve optimal depth sensitivity. To quantitatively analyze the spectral characteristics of PPG across the wavelength region of 500–900 nm in a controlled environment, this study performs the spectral measurement of PPG signals using a simple and cost-effective optical phantom model with two distinct layers and a customized diffuse reflectance spectroscopy system. In addition, Monte Carlo simulations are used to elaborate the underlying phenomena at the green and NIR wavelengths when considering different epithelial thicknesses and source–detector distances (SDD). Full article

A low-cost dual-FBG (fiber Bragg grating) architecture is employed to capture the pulse waveform of the artery at the subject’s wrist by measuring changes in optical power. The pulse transit time (PTT), pulse ascending time, and pulse descending time extracted from the pulse waveform are used in a blood pressure (BP) estimation model by fitting the measured BP with the reference BP obtained from a commercial sphygmomanometer. The estimation model is developed using data from 29 subjects at the age of 20 to 54. The results demonstrate that the errors between the calculated values and reference values of SBP and DBP for all of the 29 subjects both range from −4 to 5 mmHg with mean errors of 0.72 mmHg and 0.83 mmHg, respectively. The standard error can be found to be 2.45 and 2.59 mmHg for SBP and DBP, respectively. Also, it is found that this BP estimation model outperforms two BP models derived by considering PTT only. Full article

Invisible health monitoring is currently a topic of global interest within the scientific community. Sensorization of everyday objects can provide valuable health information without requiring any changes in people’s routines. In this work, a feasibility study of photoplethysmography (PPG) acquisition in the lower limbs for continuous and real-time monitoring of the vital signs, including heart rate (HR) and respiratory rate (RR), is presented. The proposed system uses two MAX30102 sensors to obtain PPG signals from the back of the thigh. As proof of concept, tests were conducted in 17 volunteers (age group between 22 and 40 years old, twelve females and five males), and the results were compared to those of reference sensors. A Pearson correlation coefficient of r = 0.92 and r = 0.77 and a mean difference of 1.2 bpm and 0.9 rpm for HR and RR, respectively, were obtained between the developed system and reference. System accuracies of 95.9% for HR and 91.3% for RR were achieved, showing the system viability for vital sign monitoring of the lower limbs. Full article

In an intelligent driving environment, monitoring the physiological state of drivers is crucial for ensuring driving safety. This paper proposes a method for monitoring and analyzing driver physiological characteristics by combining electronic vehicle identification (EVI) with multimodal biometric recognition. The method aims to efficiently monitor the driver’s heart rate, breathing frequency, emotional state, and fatigue level, providing real-time feedback to intelligent driving systems to enhance driving safety. First, considering the precision, adaptability, and real-time capabilities of current physiological signal monitoring devices, an intelligent cushion integrating MEMSs (Micro-Electro-Mechanical Systems) and optical sensors is designed. This cushion collects heart rate and breathing frequency data in real time without disrupting the driver, while an electrodermal activity monitoring system captures electromyography data. The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle’s operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver’s physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring. Full article

This paper describes the design, realization, and application of a wearable sensor based on the photoplethysmography (PPG) principle supplemented with a force-sensitive resistor and a thermometer for the measurement of contact pressure force and the temperature of the skin at the point where the optical part of the PPG sensor touches the finger. The performed experiments confirmed the essential influence of the applied contact force on the amplitude and ripple of the sensed PPG signal and the stability and precision of heart rate values determined from the PPG wave. Preliminary measurements showed that the response to the applied contact force was principally different for fingers of male and female tested persons, so different scaling and pressure levels were applied in the main experiments. Contrariwise, differences between left and right hands were not significant. The influence of skin temperature changes could be ignored for these measurements due to the short time duration of the PPG signal recording (approx. 1 min). Full article

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography. Full article

Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity. Full article

This work proposes a highly sensitive sandwich heterostructure multimode optical fiber microbend sensor for heart rate (HR), respiratory rate (RR), and ballistocardiography (BCG) monitoring, which is fabricated by combining a sandwich heterostructure multimode fiber Mach–Zehnder interferometer (SHMF-MZI) with a microbend deformer. The parameters of the SHMF-MZI sensor and the microbend deformer were analyzed and optimized in detail, and then the new encapsulated method of the wearable device was put forward. The proposed wearable sensor could greatly enhance the response to the HR signal. The performances for HR, RR, and BCG monitoring were as good as those of the medically approved commercial monitors. The sensor has the advantages of high sensitivity, easy fabrication, and good stability, providing the potential for application in the field of daily supervision and health monitoring. Full article

Reflection-type photoplethysmography (PPG) pulse sensors used in wearable smart watches, true wireless stereo, etc., have been recently considered a key component for monitoring biological signals such as heart rate, SPO₃, and blood pressure. Typically, the optical front end (OFE) of these PPG sensors is heterogeneously configured and packaged with light sources and receiver chips. In this paper, a novel quarter-annulus photodetector (NQAPD) with identical inner and outer radii of curvature has been developed using a plasma dicing process to realize a ring-type OFE receiver, which maximizes manufacturing efficiency and increases the detector collection area by 36.7% compared to the rectangular PD. The fabricated NQAPD exhibits a high quantum efficiency of over 90% in the wavelength of 500 nm to 740 nm and the highest quantum efficiency of 95% with a responsivity of 0.41 A/W at the wavelength of 530 nm. Also, the NQAPD is shown to increase the SNR of the PPG signal by 5 to 7.6 dB compared to the eight rectangular PDs. Thus, reflective PPG sensors constructed with NQAPD can be applied to various wearable devices requiring low power consumption, high performance, and cost-effectiveness. Full article

Many factors affect photoplethysmography (PPG) signal quality, one of them being the actual temperature of the skin surface. This paper describes the process of design, realization, and testing of a special wearable PPG sensor prototype with the contact thermometer measuring in detail the skin temperature in the place where the optical part of the PPG sensor touches a finger/wrist. Performed experiments confirm continual increase of temperature at the place of worn PPG sensors during the whole measurement, influencing mainly the PPG signal range. Other parameters seem to be temperature-independent or influenced by other factors—blood pressure, heart rate, etc. Full article

More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, $\kappa$ = 0.79), as well as the H10 (84.4%, $\kappa$ = 0.76), and VS (84.2%, $\kappa$ = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice. Full article