Accuracy Of Heart Rate Measurement Research Articles

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

A Preliminary Investigation about the Influence of WIMU PROTM Location on Heart Rate Accuracy: A Comparative Study in Cycle Ergometer.

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar-20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00-1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00-1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00-1.00], p < 0.001). The Bland-Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions.

Industrial design of wearable intelligent devices based on wireless networks

In the current digital era, intelligent wearable devices have become an indispensable part of people's lives and work. Through their unique design and equipped hardware and software technologies, they enable users to quickly and efficiently acquire and process personal data, thus achieving better life and work efficiency. In these devices, wireless sensing technology plays a very important role. By combining wireless networks, sensing technology, and human-computer interaction, these devices provide rich functionality and a convenient user experience. However, the current wearable intelligent devices are lacking in specific applications, and there is a risk of data privacy leakage. Through control variable method, comparative method and experimental method, the experimental part conducted research and exploration on counting accuracy, distribution range and accuracy of heart rate measurement under different exercise states, data transmission speed under different wireless connection methods, and performance testing of wearable intelligent devices (high-temperature environment performance; low-temperature environment performance; waterproof level indicators; battery life). After testing, the overall step accuracy of the product designed in this article was above 96.5 %, and the overall measurement accuracy of heart rate under various exercise states was above 92 %. The data transmission speed is fast and stable, and the other evaluations are relatively excellent. Wearable intelligent devices based on wireless networks have broad application prospects in improving quality of life, health management, and intelligent services. With the advancement of technology and the continuous changes in user needs, wearable devices can continue to develop and bring more convenience and intelligent experiences to people's daily lives.

Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial.

Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate. ClinicalTrials.gov NCT05418881; https://www.clinicaltrials.gov/ct2/show/NCT05418881.

Non-contact heart rate estimation based on singular spectrum component reconstruction using low-rank matrix and autocorrelation.

The remote photoplethysmography (rPPG) based on cameras, a technology for extracting pulse wave from videos, has been proved to be an effective heart rate (HR) monitoring method and has great potential in many fields; such as health monitoring. However, the change of facial color intensity caused by cardiovascular activities is weak. Environmental illumination changes and subjects' facial movements will produce irregular noise in rPPG signals, resulting in distortion of heart rate pulse signals and affecting the accuracy of heart rate measurement. Given the irregular noises such as motion artifacts and illumination changes in rPPG signals, this paper proposed a new method named LA-SSA. It combines low-rank sparse matrix decomposition and autocorrelation function with singular spectrum analysis (SSA). The low-rank sparse matrix decomposition is employed to globally optimize the components of the rPPG signal obtained by SSA, and some irregular noise is removed. Then, the autocorrelation function is used to optimize the global optimization results locally. The periodic components related to the heartbeat signal are selected, and the denoised rPPG signal is obtained by weighted reconstruction with a singular value ratio. The experiment using UBFC-RPPG and PURE database is performed to assess the performance of the method proposed in this paper. The average absolute error was 1.37 bpm, the 95% confidence interval was -7.56 bpm to 6.45 bpm, and the Pearson correlation coefficient was 98%, superior to most existing video-based heart rate extraction methods. Experimental results show that the proposed method can estimate HR effectively.

Accuracy of Heart Rate Measurement with Wrist-Worn Wearable Devices in Various Skin Tones: a Systematic Review.

Wearable consumer technology allows for the collection of a growing amount of personal health data. Through the analysis of reflected LED light on the skin, heart rate measurement and arrhythmia detection can be performed. Given that melanin alters skin light absorption, this study seeks to summarize the accuracy of cardiac data from wrist-worn wearable devices for participants of varying skin tones. We conducted a systematic review, searching Embase, MEDLINE, CINAHL, and Cochrane for original studies that stratified heart rate and rhythm data for consumer wearable technology according to participant race and/or skin tone. A total of 10 studies involving 469 participants met inclusion criteria. The frequency-weighted Fitzpatrick score for skin tone was reported in six studies (n = 293), with a mean participant score of 3.5 (range 1-6). Overall, four of the ten studies reported a significant reduction in accuracy of heart rate measurement with wearable devices in darker-skinned individuals, compared to participants with lighter skin tones. Four studies noted no effect of user skin tone on accuracy. The remaining two studies showed mixed results. Preliminary evidence is inconclusive, but some studies suggest that wearable devices may be less accurate for detecting heart rate in participants with darker skin tones. Higher quality evidence is necessary, with larger sample sizes and more objective stratification of participants by skin tone, in order to characterize potential racial bias in consumer devices.

Improved Heart Rate Measurement Accuracy by Reducing Artifact Noise from Finger Sensors Using Digital Filters

Heart rate is an important indicator in the health sector that can be used as an effective and rapid evaluation to determine the health status of the body. Motion or noise artifacts, power line interference, low amplitude PPG, and signal noise are all issues that might arise when measuring heart rate. This study aims to develop a digital filter that reduces noise artifacts on the finger sensor to improve heart rate measurement accuracy. Adaptive LMS and Butterworth are the two types of digital filters used in this research. In this study, data were collected from the patient while he or she was calm and moving around. In this research, the Nellcor finger sensor was employed to assess the blood flow in the fingers. The heart rate sensor will detect any changes in heart rate, and the measurement results will be presented on a personal computer (PC) as signals and heart rate values. The results of this investigation showed that utilizing an adaptive LMS filter and a Butterworth low pass filter with a cut-off frequency of 6Hz, order 4, and a sampling frequency of 1000Hz, with the Butterworth filter producing the least error value of 7.57 and adaptive LMS maximum error value of 27.65 as predicted by the researcher to eliminate noise artifacts. This research could be applied to other healthcare equipment systems that are being monitored to increase patient measurement accuracy.

Heart rate prediction from facial video with masks using eye location and corrected by convolutional neural networks

Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications. The emergence of novel coronavirus pneumonia COVID-19 has attracted worldwide attentions. Contact photoplethysmography (cPPG) methods need to contact the detection equipment with the patient, which may accelerate the spread of the epidemic. In the future, the non-contact heart rate detection will be an urgent need. However, existing heart rate measuring methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and wearing a mask). In this paper, we proposed a method of heart rate detection based on eye location of region of interest (ROI) to solve the problem of missing information when wearing masks. Besides, a model to filter outliers based on residual network was conceived first by us and the better heart rate measurement accuracy was generated. To validate our method, we also created a mask dataset. The results demonstrated that after using our method for correcting the heart rate (HR) value measured with the traditional method, the accuracy reaches 4.65 bpm, which is 0.42 bpm higher than that without correction.

Accuracy of Heart Rate Measurements of Three Smartwatch Models in Dogs

This study was aimed to evaluate the accuracy of heart rate (HR) measurements using 3 smartwatch models designed for human use when applied to dogs. A group of 15 mixed breed dogs, ages ranging from 2 to 3 years, weighing between 20 and 30 kg were used. The Garmin Fenix 5X plus (GF5Xp), Samsung Gear S3 (SGS3), and Polygold A-6 (PDA6), were randomly placed on the tibia of dogs to collect the HR recordings every 5 minutes for 1 hour. A veterinary patient monitor (VPM) was used as the reference method. A Bland-Altman plot was applied to determine the difference between smartwatches. The receiver operating characteristic curves were developed analysis for sensitivity and specificity. The HR measured by VPM ranged from 65 to 200 bpm (mean, 149.3 ± 28.1 bpm; median, 154 bpm). Among the smartwatches, the lowest (44 bpm) HR was measured by PDA6, while the highest (201 bpm) was measured by SGS3. Among the smartwatches; GF5Xp and SGS3 had slopes equal to 1 and residual standard deviation closer to 0, in agreement with VPM. At the cut-off HR (160 bpm), SGS3 had the highest sensitivity (97.4%), whereas PDA6 had the lowest sensitivity (68.0%). The specificity of GF5Xp, SGS3, and PDA6 was 97.1%, 98.0%, and 98.0%, respectively. The SGS3 and GF5Xp can be ideal tools for veterinarians to monitor the HR of dogs without requiring an additional device.

Accuracy of Heart Rate Measurement by the Fitbit Charge 2 During Wheelchair Activities in People With Spinal Cord Injury: Instrument Validation Study.

BackgroundHeart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique.ObjectiveThe objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level.MethodsThe HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups—the no SCI group and based on lesion level into the <T5 (midthoracic and lower) group, T5-T1 (high-thoracic) group, and >T1 (cervical) group. Mean absolute percentage error (MAPE) and concordance correlation coefficient were determined for each group for each activity type, that is, rest, wheelchair activities, and strength exercise.ResultsWith an overall MAPEall lesions of 12.99%, the accuracy fell below the standard acceptable MAPE of –10% to +10% with a moderate agreement (concordance correlation coefficient=0.577). The HR accuracy of Fitbit Charge 2 seems to be reduced in those with cervical lesion level in all activities (MAPEno SCI=8.09%; MAPE<T5=11.16%; MAPET1−T5=10.5%; and MAPE>T1=20.43%). The accuracy of the Fitbit Charge 2 decreased with increasing intensity in all lesions (MAPErest=6.5%, MAPEactivity=12.97%, and MAPEstrength=14.2%).ConclusionsHR measured with the PPG technique showed lower accuracy in people with SCI than in those without SCI. The accuracy was just above the acceptable level in people with paraplegia, whereas in people with tetraplegia, a worse accuracy was found. The accuracy seemed to worsen with increasing intensities. Therefore, high-intensity HR data, especially in people with cervical lesions, should be used with caution.

Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study.

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and a cellphone app (Polar Beat app using iPhone 7 as a hardware platform) in a cross-sectional field study. Thirty-six moderate endurance-trained adults (20 males/16 females) completed a test battery consisting of walking and running 3 km, a 1.6 km interval run (standard 400 m outdoor stadium), 3 km forest run (outdoor), 500/1000 m swim and 4.3/31.5 km cycling tests. Heart rate was recorded via a Polar H10 chest strap and distance was controlled via a map, 400 m stadium or 50 m pool. For all tests except swimming, strong correlation values of r > 0.90 were calculated with moderate exercise intensity and a mean absolute percentage error of 2.85%. During the interval run, several significant deviations (p < 0.049) were observed. The swim disciplines showed significant differences (p < 0.001), with the 500 m test having a mean absolute percentage error of 8.61%, and the 1000 m test of 55.32%. In most tests, significant deviations (p < 0.001) were calculated for distance measurement. However, a maximum mean absolute percentage error of 4.74% and small mean absolute error based on the total route lengths were calculated. This study showed that the accuracy of heart rate measurements could be rated as good, except for rapid changing heart rate during interval training and swimming. Distance measurement differences were rated as non-relevant in practice for use in sports.

Wristbands in Home-Based Rehabilitation-Validation of Heart Rate Measurement.

The possibility of using a smartwatch as a rehabilitation tool to monitor patients’ heart rates during exercise has gained the attention of many researchers. This study aimed to evaluate the accuracy and precision of the HR measurement performed by two wrist monitors: the Fitbit Charge 4 and the Xiaomi Mi Band 5. Thirty-one healthy volunteers were asked to perform a stress test on a treadmill. Their heart rates were recorded simultaneously by the wristbands and an electrocardiogram (ECG) at 1 min intervals. The mean absolute error percentage (MAPE), Lin’s concordance correlation coefficient (LCCC), and Bland–Altman analysis were calculated to compare the precision and accuracy of heart rate measurements. The estimated validation criteria were MAPE < 10% and LCCC < 0.8. The overall MAPE and LCCC of the Fitbit were 10.19% (±11.79%) and 0.753 (95% CI: 0.717–0.785), respectively. The MAPE and LCCC of the Xiaomi were 6.89% (±9.75) and 0.903 (0.886–0.917), respectively. The precision and accuracy of both devices decreased with the increased exercise intensity. The accuracy of wearable wrist-worn heart rate monitors varies and depends on the intensity of training. Therefore, the decision to use such a device as a heart rate monitor during in-home rehabilitation should be made with caution.

B-PO04-193 BUT FOR THE BLIND SPOT: ACCURACY AND DIAGNOSTIC PERFORMANCE OF THE APPLE WATCH™ CARDIAC FEATURES IN PEDIATRIC PATIENTS

New wearable technologies have features of ambulatory ECG monitors. The Apple Watch™ (AW) offers heart rate (HR) tracking by photoplethysmography (PPG) and single lead ECG recordings. The accuracy of HR measurements by AW and diagnostic performance of AW ECGs among children have not been explored. The primary objective was to assess the accuracy of AW HR measurements compared to gold-standard modalities (GSM) in children. Secondary objectives included differentiation of sinus from non-sinus rhythm using AW ECGs. Patients ≤18 years were prospectively enrolled to wear an AW during 1) telemetry admission, 2) electrophysiology study (EPS), or 3) exercise stress test (EST). AW HR measurements were compared to those of a GSM by intra-class correlation coefficients (ICC). AW ECGs were recorded with concurrent 12 lead ECGs and reviewed by two blinded pediatric electrophysiologists for rhythm determination. 53 patients (median age 10 years, IQR 1.2-15.0) wore AW during telemetry (48%, n=26), EPS (20%, n=11), or EST (32%, n=16). Peak HR measured by AW had high ICC to GSM, but AW were unable to record HR > 210 bpm by PPG (Table 1). 56 AW ECGs were reviewed. Excellent agreement was seen among raters (agreement 92.5%, Kappa 0.85 +/- 0 .13). Identification of non-sinus rhythm by AW ECG showed a sensitivity of 90.3-93.5% and a specificity of 95.5-96.0%. AW HR values by PPG tightly correlated with GSM measurements with the notable limitation of inability to record HR > 210 bpm. This represents a blind-spot in the application of AW for tachycardia evaluation in pediatric patients. AW ECGs had good sensitivity and specificity in identification of non-sinus rhythm.

Optimal color channel combination across skin tones for remote heart rate measurement in camera-based photoplethysmography

ObjectiveThe heart rate is an essential vital sign that can be measured remotely with camera-based photoplethysmography (cbPPG). Systems for cbPPG typically use cameras that deliver red, green, and blue (RGB) channels. The combination of these channels has been proven to increase signal-to-noise ratio (SNR) and heart rate measurement accuracy (ACC). However, many combinations remain untested, the comparison of proposed combinations on large datasets is insufficiently investigated, and the interplay with skin tone is rarely addressed. MethodsEight regions of interest and eight color spaces with a total of 25 color channels were compared in terms of ACC and SNR based on the Binghamton-Pittsburgh-RPI Multimodal Spontaneous Emotion Database (BP4D+). Additionally, two systematic grid searches were performed to evaluate ACC in the space of linear combinations of the RGB channels. ResultsGlabella and forehead regions of interest provided highest ACC (up to 74.1 %) and SNR (> -3 dB) with the hue channel H from HSV color space and the chrominance channel Q from NTSC color space. The grid searches revealed a global optimum of linear RGB combinations (ACC: 79.2 %). This optimum occurred for all skin tones, although ACC dropped for darker skin tones. ConclusionThrough systematic grid searches we were able to identify the skin tone independent optimal linear RGB color combination for measuring heart rate with cbPPG. Our results proved on a large dataset that the identified optimum outperformed conventionally used color channels. SignificanceThe presented findings provide useful evidence for future considerations of algorithmic approaches for cbPPG.

Contact-Free Pulse Signal Extraction from Human Face Videos: A Review and New Optimized Filtering Approach.

In this chapter, we review methods for video-based heart monitoring, from classical signal processing approaches to modern deep learning methods. In addition, we propose a new method for learning an optimal filter that can overcome many of the problems that can affect classical approaches, such as light reflection and subject's movements, at a fraction of the training cost of deep learning approaches. Following the usual procedures for region of interest extraction and tracking, robust skin color estimation and signal pre-processing, we introduce a least-squares error optimal filter, learnt using an established training dataset to estimate the photoplethysmographic (PPG) signal more accurately from the measured color changes over time. This method not only improves the accuracy of heart rate measurement but also resulted in the extraction of a cleaner pulse signal, which could be integrated into many other useful applications such as human biometric recognition or recognition of emotional state. The method was tested on the DEAP dataset and showed improved performance over the best previous classical method on that dataset. The results obtained show that our proposed contact-free heart rate measurement method has significantly improved on existing methods.

A Wireless-Frequency-Locked-Loop-Based Vital Sign Sensor With Quadrature Tracking and Phase-Noise Reduction Capability

Most conventional continuous wave (CW) vital sign sensors suffer from the phase noise of the voltage-controlled oscillator (VCO). This work develops a novel wireless-frequency-locked-loop (FLL)-based sensor to overcome this issue. This technology uses the propagation delay of the sensor signal to make the entire sensing circuit and the subject under test (SUT) a wireless FLL. The wireless FLL uses a quadrature-tracking technique to track the phase of the propagation signal and the phase variation that is caused by the vibration of the SUT. This technique thus enables the wireless FLL not only to reduce the phase noise of the VCO but also to sense the vital signs of the SUT. Moreover, the phase-noise reduction of the wireless FLL increases with the sensing distance. The signal-to-noise ratio (SNR) of the proposed wireless-FLL-based sensor in vital sign sensing is therefore much higher than that of a conventional CW sensor. The SNR performance of the wireless-FLL-based sensor is further optimized using a highly sensitive VCO. Experimental results demonstrate that the SNR of the wireless-FLL-based sensor is around 16 dB better than that of the conventional CW sensor at a detection distance of 4 m. The SNR enhancement increased by approximately 6.6 dB when the detection distance is doubled. With this SNR enhancement, the heart-rate measurement accuracy of the developed 2.4 GHz wireless-FLL-based sensor is 90.5 % at a detection distance of 8 m.

Optimizing a novel PPG sensor patch via optical simulations towards accurate heart rates

This study proposes the design and optimization of the flexible OLED–OPD photoplethysmography (PPG) sensor patch to estimate the long time continuous heart rate. Using optical simulation, the distance between OLED–OPD and the aperture area of the OLED–OPD has been optimized to enhance the AC/DC ratio of the receive PPG signal. The optical simulation incorporates an empirical optical skin model. All the patches incorporate green OLED@525 nm wavelength and red OLED@630 nm wavelength. Simulation results show that the optimized AC/DC ratio of the cross-type patch for the green and the red OLED is 2.16% and 6.25%, respectively. Similarly, the optimized AC/DC ratio of the square-type patch for the green, and the red OLED is 9.6% and 5.8%, respectively. Experiment results show that the received PPG signal AC/DC ratio for the square type and cross-type are 2%@green OLED and 4.5% @green OLED, respectively. Also, the AC/DC ratio of the received PPG signal from the square type and cross-type are 1.4%@red OLED and 1.1%@red OLED, respectively. The AC/DC ratio is reduced because the skin and blood itself act as a lossy medium so that the DC signal increased more; as a result, the overall AC/DC ratio decrease. The best design of the optical patch is the square-type OPD patch due to the wide area of the OPD. The OLED drive current ranges between 0.1 and 0.4 mA. The average OPD current is 800 nA. The flexibility of the design PPG sensor patch is 130°. The non-invasive square-type PPG sensor patch is applied to the wrist artery of 40 subjects for sensing the PPG pulsation of the blood vessel. The heart rate measurement accuracy is 95%, whereas the standard error rate is 0.37 ± 1.96 bpm, respectively.

Measurement latency significantly contributes to reduced heart rate measurement accuracy in wearable devices

The purpose of this study was to determine the extent to which measurement latency has significance for the accuracy of heart rate (HR) measured by wearable devices during physical activities. Thirty participants (15 female) aged 18–50 years (mean 24.8, 6.3 SD years) with weight ranging from 53–87 kg (mean 70.5, 9.4 SD kg) participated in this cross-sectional study. Participants completed three submaximal exercise conditions; cycling, treadmill walking/running and rapid arm movements. During the exercises, HR was concurrently measured using the Polar H10 (PL), the Apple Watch 2 (AW) and the Garmin Forerunner 235 (GF). Measurement latency significantly affected the discrepancy in HR measures and measurement latency accounted for 15% (GF) and 2% (AW) of the explained discrepancy in HR between the criterion measure PL and the GF and AW. However, the findings also showed that the magnitude of HR measurement latency differed notably among the two wearable devices and measurement latency was greatly influenced not only by type but also by the intensity of exercise. These findings contribute to our understanding of wearable device HR measures discrepancy and this knowledge may be of assistance when electing to use wearable devices to monitor physical exertion in different contexts.

Visual heart rate estimation and negative feedback control for fitness exercise

The heart rate of subjects is extracted from video data during exercises and is treated as a negative feedback parameter for the control of fitness devices. The probability distribution in RGB space of the parameters of the skin reflection model is analyzed based on the experimental data taken under a wide range of environmental settings. A novel pulse extraction algorithm is proposed based on this distribution, which sets aside distribution centers as the model parameters for preliminary pulse separation and adopts cascade least mean square adaptive filters to compensate for modeling errors. A Gaussian distribution-based heart rate estimation algorithm is employed for heart rate stabilization. In addition, the concept of heart rate negative feedback control is introduced, e.g., the treadmill speed and incline can be adjusted interactively based on the subject's heart rate. The heart rate measurement accuracy and the control scheme have been evaluated by a self-collected benchmark dataset and a prototype system. The heart rate measurement accuracy is increased over the state-of-the-art algorithms by more than 10% on average. The heart rate negative feedback control improves human–machine interaction performance compared with a traditional control scheme that uses the predefined program regardless of the subjects’ physical state. The proposed technique pioneers new possibilities for the usage of fitness devices.

An Autonomous Wireless Health Monitoring System Based on Heartbeat and Accelerometer Sensors

Falls are a main cause of injury for patients with certain diseases. Patients who wear health monitoring systems can go about daily activities without limitations, thereby enhancing their quality of life. In this paper, patient falls and heart rate were accurately detected and measured using two proposed algorithms. The first algorithm, abnormal heart rate detection (AHRD), improves patient heart rate measurement accuracy and distinguishes between normal and abnormal heart rate functions. The second algorithm, TB-AIC, combines an acceleration threshold and monitoring of patient activity/inactivity functions to accurately detect patient falls. The two algorithms were practically implemented in a proposed autonomous wireless health monitoring system (AWHMS). The AWHMS was implemented based on a GSM module, GPS, microcontroller, heartbeat and accelerometer sensors, and a smartphone. The measurement accuracy of the recorded heart rate was evaluated based on the mean absolute error, Bland–Altman plots, and correlation coefficients. Fourteen types of patient activities were considered (seven types of falling and seven types of daily activities) to determine the fall detection accuracy. The results indicate that the proposed AWHMS succeeded in monitoring the patient’s vital signs, with heart rate measurement and fall detection accuracies of 98.75% and 99.11%, respectively. In addition, the sensitivity and specificity of the fall detection algorithm (both 99.12%) were explored.