Evaluation of Wearable Optical Heart Rate Monitoring Sensors

Heart rate monitoring provides valuable information about an individual’s physiological condition. The information obtained from heart rate monitoring can be used for a wide range of purposes such as clinical diagnostics, assessment of the efficiency of training for sports and fitness, or of sleep quality and stress levels in wellbeing applications. Other useful parameters for describing a person’s fitness, such as maximal oxygen uptake and energy expenditure, can also be estimated using heart rate measurement. The traditional ‘gold standard’ for heart rate monitoring is the electrocardiograph, but nowadays there are a number of alternative methods too. Of these, optical sensors provide a relatively simple, lowcost and unobtrusive technology for monitoring heart rate and they are widely accepted by users. There are many factors affecting the measurement of optical signals that have an effect on the accuracy of heart rate estimation. However, there is a lack of standardized and unified methodology for comparing the accuracy of optical heart rate sensors to the ‘gold standard’ methods of measuring heart rate. The widespread use of optical sensors for different purposes has led to a pressing need for a common objective methodology for the evaluation of how accurate these sensors are. This thesis presents a methodology for the objective evaluation of optical heart-rate sensors. The methodology is applied in evaluation studies of four commercially available optical sensors. These evaluations were carried out during both controlled and non-controlled sporting and daily life activities. In addition, evaluation of beat detection accuracy was carried out in non-controlled sleep conditions. The accuracy of wrist-worn optical heart-rate sensors in estimating of maximal oxygen uptake during submaximal exercise and energy expenditure during maximal exercise using heart rate as input parameter were also evaluated. The accuracy of a semi-continuous heart rate estimation algorithm designed to reduce power consumption for long-term monitoring was also evaluated in various conditions. The main findings show that optical heart-rate sensors may be highly accurate during rhythmic sports activities, such as jogging, running, and cycling, including ramp-up running during maximal exercise testing. During non-rhythmic activities, such as intermittent hand movements, the sensors’ accuracy depends on where they are worn. During sleep and motionless conditions, the optical heart-rate sensors’ estimates for beat detection and inter-beat interval showed less than one percent inaccuracy against the values obtained using standard measurement techniques. The sensors were also sufficiently accurate at measuring the interbeat intervals to be used for calculating the heart rate variability parameters. The estimation accuracy of the fitness parameters derived from measured heart rate can be described as follows. An assessment of the maximal oxygen uptake estimation during a sub-maximal outdoor exercise had a precision close to a sport laboratory measurement. The energy expenditure estimation during a maximal exercise was more accurate during higher intensity of exercise above aerobic threshold but the accuracy decreased at lower intensity of exercise below the aerobic threshold, in comparison with the standardized reference measurement. The semi-continuous algorithm was nearly as accurate as continuous heart-rate detection, and there was a significant reduction in the power consumption of the optical chain components up to eighty percent. The results obtained from these studies show that, under certain conditions, optical sensors may be similarly accurate in measuring heart rate as the ‘gold standard’ methods and they can be relied on to monitor heart rate for various purposes during sport, everyday activities, or sleep

Learning a Physical Activity Classifier for a Low-power Embedded Wrist-located Device

This article presents and evaluates a novel algorithm for learning a physical activity classifier for a low-power embedded wrist-located device. The overall system is designed for real-time execution and it is implemented in the commercial low-power System-on-Chips nRF51 and nRF52. Results were obtained using a database composed of 140 users containing more than 340 hours of labeled raw acceleration data. The final precision achieved for the most important classes, (Rest, Walk, and Run), was of 96%, 94%, and 99% and it generalizes to compound activities such as XC skiing or Housework. We conclude with a benchmarking of the system in terms of memory footprint and power consumption.Comment: Submitted to the 2018 IEEE International Conference on Biomedical and Health Informatic

Detection of Beat-to-Beat Intervals from Wrist Photoplethysmography in Patients with Sinus Rhythm and Atrial Fibrillation after Surgery

Wrist photoplethysmography (PPG) allows unobtrusive monitoring of the heart rate (HR). PPG is affected by the capillary blood perfusion and the pumping function of the heart, which generally deteriorate with age and due to presence of cardiac arrhythmia. The performance of wrist PPG in monitoring beat-to-beat HR in older patients with arrhythmia has not been reported earlier. We monitored PPG from wrist in 18 patients recovering from surgery in the post anesthesia care unit, and evaluated the inter-beat interval (IBI) detection accuracy against ECG based R-to-R intervals (RRI). Nine subjects had sinus rhythm (SR, 68.0y$\pm$10.2y, 6 males) and nine subjects had atrial fibrillation (AF, 71.3y$\pm$7.8y, 4 males) during the recording. For the SR group, 99.44% of the beats were correctly identified, 2.39% extra beats were detected, and the mean absolute error (MAE) was 7.34 ms. For the AF group, 97.49% of the heartbeats were correctly identified, 2.26% extra beats were detected, and the MAE was 14.31 ms. IBI from the PPG were hence in close agreement with the ECG reference in both groups. The results suggest that wrist PPG provides a comfortable alternative to ECG and can be used for long-term monitoring and screening of AF episodes.Comment: Submitted to the 2018 IEEE International Conference on Biomedical and Health Informatic

Evaluation of Wearable Optical Heart Rate Monitoring Sensors

Heart rate monitoring provides valuable information about an individual’s physiological condition. The information obtained from heart rate monitoring can be used for a wide range of purposes such as clinical diagnostics, assessment of the efficiency of training for sports and fitness, or of sleep quality and stress levels in wellbeing applications. Other useful parameters for describing a person’s fitness, such as maximal oxygen uptake and energy expenditure, can also be estimated using heart rate measurement. The traditional ‘gold standard’ for heart rate monitoring is the electrocardiograph, but nowadays there are a number of alternative methods too. Of these, optical sensors provide a relatively simple, lowcost and unobtrusive technology for monitoring heart rate and they are widely accepted by users. There are many factors affecting the measurement of optical signals that have an effect on the accuracy of heart rate estimation. However, there is a lack of standardized and unified methodology for comparing the accuracy of optical heart rate sensors to the ‘gold standard’ methods of measuring heart rate. The widespread use of optical sensors for different purposes has led to a pressing need for a common objective methodology for the evaluation of how accurate these sensors are. This thesis presents a methodology for the objective evaluation of optical heart-rate sensors. The methodology is applied in evaluation studies of four commercially available optical sensors. These evaluations were carried out during both controlled and non-controlled sporting and daily life activities. In addition, evaluation of beat detection accuracy was carried out in non-controlled sleep conditions. The accuracy of wrist-worn optical heart-rate sensors in estimating of maximal oxygen uptake during submaximal exercise and energy expenditure during maximal exercise using heart rate as input parameter were also evaluated. The accuracy of a semi-continuous heart rate estimation algorithm designed to reduce power consumption for long-term monitoring was also evaluated in various conditions. The main findings show that optical heart-rate sensors may be highly accurate during rhythmic sports activities, such as jogging, running, and cycling, including ramp-up running during maximal exercise testing. During non-rhythmic activities, such as intermittent hand movements, the sensors’ accuracy depends on where they are worn. During sleep and motionless conditions, the optical heart-rate sensors’ estimates for beat detection and inter-beat interval showed less than one percent inaccuracy against the values obtained using standard measurement techniques. The sensors were also sufficiently accurate at measuring the interbeat intervals to be used for calculating the heart rate variability parameters. The estimation accuracy of the fitness parameters derived from measured heart rate can be described as follows. An assessment of the maximal oxygen uptake estimation during a sub-maximal outdoor exercise had a precision close to a sport laboratory measurement. The energy expenditure estimation during a maximal exercise was more accurate during higher intensity of exercise above aerobic threshold but the accuracy decreased at lower intensity of exercise below the aerobic threshold, in comparison with the standardized reference measurement. The semi-continuous algorithm was nearly as accurate as continuous heart-rate detection, and there was a significant reduction in the power consumption of the optical chain components up to eighty percent. The results obtained from these studies show that, under certain conditions, optical sensors may be similarly accurate in measuring heart rate as the ‘gold standard’ methods and they can be relied on to monitor heart rate for various purposes during sport, everyday activities, or sleep

Comparison of heart rate monitoring accuracy between chest strap and vest during physical training and implications on training decisions

Heart rate (HR) and heart rate variability (HRV) based physiological metrics such as Excess Post‐exercise Oxygen Consumption (EPOC), Energy Expenditure (EE), and Training Impulse (TRIMP) are widely utilized in coaching to monitor and optimize an athlete’s training load. Chest straps, and re-cently also dry electrodes integrated to special sports vests, are used to monitor HR during sports. Mechanical design, placement of electrodes, and ergonomics of the sensor affect the measured signal quality and artefacts. To evaluate the impact of the sensor mechanical design on the accuracy of the HR/HRV and further on to estimation of EPOC, EE, and TRIMP, we recorded HR and HRV from a chest strap and a vest with the same ECG sensor during supervised exercise protocol. A 3‐lead clinical Holter ECG was used as a reference. Twenty‐five healthy subjects (six females) participated. Mean absolute percentage error (MAPE) for HR was 0.76% with chest strap and 3.32% with vest. MAPE was 1.70% vs. 6.73% for EE, 0.38% vs. 8.99% for TRIMP and 3.90% vs. 54.15% for EPOC with chest strap and vest, respectively. Results suggest superior accuracy of chest strap over vest for HR and physiological metrics monitoring during sports.publishedVersionPeer reviewe

Estimating Heart Rate, Energy Expenditure, and Physical Performance With a Wrist Photoplethysmographic Device During Running

BACKGROUND: Wearable sensors enable long-term monitoring of health and wellbeing indicators. An objective evaluation of sensors' accuracy is important, especially for their use in health care. OBJECTIVE: The aim of this study was to use a wrist-worn optical heart rate (OHR) device to estimate heart rate (HR), energy expenditure (EE), and maximal oxygen intake capacity (VO2Max) during running and to evaluate the accuracy of the estimated parameters (HR, EE, and VO2Max) against golden reference methods. METHODS: A total of 24 healthy volunteers, of whom 11 were female, with a mean age of 36.2 years (SD 8.2 years) participated in a submaximal self-paced outdoor running test and maximal voluntary exercise test in a sports laboratory. OHR was monitored with a PulseOn wrist-worn photoplethysmographic device and the running speed with a phone GPS sensor. A physiological model based on HR, running speed, and personal characteristics (age, gender, weight, and height) was used to estimate EE during the maximal voluntary exercise test and VO2Max during the submaximal outdoor running test. ECG-based HR and respiratory gas analysis based estimates were used as golden references. RESULTS: OHR was able to measure HR during running with a 1.9% mean absolute percentage error (MAPE). VO2Max estimated during the submaximal outdoor running test was closely similar to the sports laboratory estimate (MAPE 5.2%). The energy expenditure estimate (n=23) was quite accurate when HR was above the aerobic threshold (MAPE 6.7%), but MAPE increased to 16.5% during a lighter intensity of exercise. CONCLUSIONS: The results suggest that wrist-worn OHR may accurately estimate HR during running up to maximal HR. When combined with physiological modeling, wrist-worn OHR may be used for an estimation of EE, especially during higher intensity running, and VO2Max, even during submaximal self-paced outdoor recreational running.Peer reviewe

A Modular System for Rapid Development of Telemedical Devices

Remote patient monitoring is gradually attracting more attention as the population in developed countries ages, and as chronic diseases appear more frequently in the population. Miniaturization in electronics and mobile technologies has led to rapid development of various wearable systems for remote monitoring of vital signs, supervision systems in home care, assistive technologies and similar systems. There is a significant demand for developing the necessary devices very rapidly, especially for shortening the way from an idea to a first function sample. This paper presents a solution for rapidly developing devices for telemedical applications, remote monitoring and assistive technologies. The approach used here is to design and realize a modular system consisting of input modules for signal acquisition, a control unit for signal pre-processing, handshaking of data communication, controlling the system and providing the user interface and communication modules for data transmission to a superordinate system. A description of specific applications developed on the basis of the system is also presented in the paper

Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation

Objective: Atrial fibrillation (AF) causes marked risk for patients, while silent fibrillation may remain unnoticed if not suspected and screened. Development of comfortable yet accurate beat-to-beat heart rate (HR) monitoring with good AF detection sensitivity would facilitate screening and improve treatment. The purpose of this study was to evaluate whether a wrist-worn photoplethysmography (PPG) device can be used to monitor beat-to-beat HR accurately during post-operative treatment in patients suffering from AF and whether wrist-PPG can be used to distinguish AF from sinus rhythm (SR). Approach: Twenty-nine patients (14 with AF, 15 with SR, mean age 71.5 years) with multiple comorbidities were monitored during routine post-operative treatment. The monitoring included standard ECG, finger PPG monitoring and a wrist-worn PPG monitor with green and infrared light sources. The HR from PPG sensors was compared against ECG-derived HR. Main results: The wrist PPG technology had very good HR and beat detection accuracy when using green light. For the SR group, the mean absolute error (MAE) for HR was 1.50 bpm, and for the inter-beat intervals (IBI), the MAE was 7.64 ms. For the AF group, the MAE for HR was 4.28 bpm and for IBI, the MAE was 14.67 ms. Accuracy for the infrared (IR) channel was worse. Finger PPG provided similar accuracy for HR and better accuracy for the IBI. AF detection sensitivity using green light was 99.0% and the specificity was 93.0%. Performance can be improved by discarding unreliable IBI periods. Significance: Results suggest that wrist PPG measurement allows accurate HR and beat-to-beat HR monitoring also in AF patients, and could be used for differentiating between SR and AF with very good sensitivity.acceptedVersionPeer reviewe

The Performance of Wrist Photoplethysmography in Monitoring Atrial Fibrillation in Post Cardiac Surgery Patients

Background and Aim: Atrial fibrillation (AF) is the most common cardiac arrhythmia, associated with an increased risk of thromboembolic ischemic stroke. Subjects with CHA2DS2-VASc score greater than one have 2.2% or higher annual risk for stroke if not treated with anticoagulant medicine. The presence of AF is normally examined with 24 or 48 h ECG Holter monitoring that is inefficient in case of rarely occurring paroxysmal AF episodes. We evaluated the performance of a wrist-worn photoplethysmografic (PPG) device in monitoring cardiac rhythm and detecting AF. While being comfortable to wear, wrist PPG could provide a solution for continuous 24/7 monitoring. Methods: 30 cardiac surgery patients (9 female, 21 male, 69.3 ± 6.9 years old) were recruited for the study in Cardiac surgery ward at Tampere University Hospital. The subjects were monitored for 24 hours with a wrist-worn PPG monitor (PulseOn Oy, Espoo, Finland) leading to roughly 700 hours of data. 5-lead Holter ECG was used as a reference. The monitoring was started on 2nd to 4th post-operative day and the subjects were mostly staying in bed during the monitoring. The study was approved by the local ethical committee. Inter-beat-intervals (IBI) including signal quality information were estimated from the PPG and further used to detect AF in 5-minute intervals. Results: 12.3 % of the 5-minute segments were discarded due to inadequate signal quality and the remaining data was classified to AF and non-AF. Three out of the 30 subject developed AF during the monitoring period leading to 22 hours of AF data. All data segments during AF were correctly labeled as AF providing 100% sensitivity. From the non-AF data, 96.1% was correctly classified. Most of the incorrect classifications resulted from the presence of very frequent ectopic beats (> 10 per minute). Ignoring these segments improved the specificity to 99.7%.publishedVersionPeer reviewe