The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.
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ABSTRACT:
SUBMITTER: Park H
PROVIDER: S-EPMC5539477 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:Inappropriate physical inactivity is a global health problem increasing the risk of cardiometabolic diseases. Wearable sensors show great potential to promote physical activity and thus a healthier lifestyle. While commercial activity trackers are available to estimate energy expenditure (EE) in non-disabled individuals, they are not designed for reliable assessments in individuals with an incomplete spinal cord injury (iSCI). Furthermore, activity recommendations for this population are currently rather vague and not tailored to their individual needs, and activity guidelines provided for the non-disabled population may not be easily translated for this population. However, especially in iSCI individuals with impaired abilities to stand and walk, the assessment of physical activities and appropriate recommendations for a healthy lifestyle are challenging. Therefore, the study aimed at developing an EE estimation model for iSCI individuals able to walk based on wearable sensor data. Additionally, the data collected within this study was used to translate common activity recommendations for the non-disabled population to easily understandable activity goals for ambulatory individuals with an iSCI. In total, 30 ambulatory individuals with an iSCI were equipped with wearable sensors while performing 12 different physical activities. EE was measured continuously and demographic and anthropometric variables, clinical assessment scores as well as wearable-sensor-derived features were used to develop different EE estimation models. The best EE estimation model comprised the estimation of resting EE using the updated Harris-Benedict equation, classifying activities using a k-nearest neighbor algorithm, and applying a multiple linear regression-based EE estimation model for each activity class. The mean absolute estimation error of this model was 15.2 ± 6.3% and the corresponding mean signed error was -3.4 ± 8.9%. Translating activity recommendations of global health institutions, we suggest a minimum of 2,000-3,000 steps per day for ambulatory individuals with an iSCI. If ambulatory individuals with an iSCI targeted the popular 10,000 steps a day recommendation for the non-disabled population, their equivalent would be around 8,000 steps a day. The combination of the presented dedicated EE estimation model for ambulatory individuals with an iSCI and the translated activity recommendations is an important step toward promoting an active lifestyle in this population.
| S-EPMC6838774 | biostudies-literature
Project description:BACKGROUND:Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures. OBJECTIVE:The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. METHODS:We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability. RESULTS:We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. CONCLUSIONS:Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.
| S-EPMC7509623 | biostudies-literature
Project description:As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.
| S-EPMC7010823 | biostudies-literature
Project description:We present a multi-device and multi-modal dataset, called WEEE, collected from 17 participants while they were performing different physical activities. WEEE contains: (1) sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist); (2) respiratory data collected with an indirect calorimeter serving as ground-truth information; (3) demographics and body composition data (e.g., fat percentage); (4) intensity level and type of physical activities, along with their corresponding metabolic equivalent of task (MET) values; and (5) answers to questionnaires about participants' physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations, we believe that the dataset will enable the development of novel human energy expenditure (EE) estimation techniques for a diverse set of application scenarios. EE refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. A reliable estimate of people's EE thus enables computing systems to make inferences about users' physical activity and help them promoting a healthier lifestyle.
| S-EPMC9436988 | biostudies-literature
Project description:BackgroundAccurate assessment of energy expenditure (EE) is important for the study of energy balance and metabolic disorders. Combined heart rate (HR) and acceleration (ACC) sensing may increase precision of physical activity EE (PAEE) which is the most variable component of total EE (TEE).ObjectiveTo evaluate estimates of EE using ACC and HR data with or without individual calibration against doubly-labelled water (DLW) estimates of EE.Design23 women and 23 men (22-55 yrs, 48-104 kg, 8-46%body fat) underwent 45-min resting EE (REE) measurement and completed a 20-min treadmill test, an 8-min step test, and a 3-min walk test for individual calibration. ACC and HR were monitored and TEE measured over 14 days using DLW. Diet-induced thermogenesis (DIT) was calculated from food-frequency questionnaire. PAEE (TEE ÷ REE ÷ DIT) and TEE were compared to estimates from ACC and HR using bias, root mean square error (RMSE), and correlation statistics.ResultsMean(SD) measured PAEE and TEE were 66(25) kJ·day(-1)·kg(-1), and 12(2.6) MJ·day(-1), respectively. Estimated PAEE from ACC was 54(15) kJ·day(-1)·kg(-1) (p<0.001), with RMSE 24 kJ·day(-1)·kg(-1) and correlation r = 0.52. PAEE estimated from HR and ACC+HR with treadmill calibration were 67(42) and 69(25) kJ·day(-1)·kg(-1) (bias non-significant), with RMSE 34 and 20 kJ·day(-1)·kg(-1) and correlations r = 0.58 and r = 0.67, respectively. Similar results were obtained with step-calibrated and walk-calibrated models, whereas non-calibrated models were less precise (RMSE: 37 and 24 kJ·day(-1)·kg(-1), r = 0.40 and r = 0.55). TEE models also had high validity, with biases <5%, and correlations r = 0.71 (ACC), r = 0.66-0.76 (HR), and r = 0.76-0.83 (ACC+HR).ConclusionsBoth accelerometry and heart rate may be used to estimate EE in adult European men and women, with improved precision if combined and if heart rate is individually calibrated.
| S-EPMC4562631 | biostudies-literature
Project description:Application of ultra-short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people's daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.
| S-EPMC7764161 | biostudies-literature
Project description:BackgroundActive commuting may help to increase adults' physical activity levels. However, estimates of its energy cost are derived from a small number of studies which are laboratory-based or use self-reported measures.MethodsAdults working in Cambridge (UK) recruited through a predominantly workplace-based strategy wore combined heart rate and movement sensors and global positioning system (GPS) devices for one week, and completed synchronous day-by-day travel diaries in 2010 and 2011. Commuting journeys were delineated using GPS data, and metabolic intensity (standard metabolic equivalents; MET) was derived and compared between journey types using mixed-effects linear regression.Results182 commuting journeys were included in the analysis. Median intensity was 1.28 MET for car journeys; 1.67 MET for bus journeys; 4.61 MET for walking journeys; 6.44 MET for cycling journeys; 1.78 MET for journeys made by car in combination with walking; and 2.21 MET for journeys made by car in combination with cycling. The value for journeys made solely by car was significantly lower than those for all other journey types (p<0.04). On average, 20% of the duration of journeys incorporating any active travel (equating to 8 min) was spent in moderate-to-vigorous physical activity (MVPA).ConclusionsWe have demonstrated how GPS and activity data from a free-living sample can be used simultaneously to provide objective estimates of commuting energy expenditure. On average, incorporating walking or cycling into longer journeys provided over half the weekly recommended activity levels from the commute alone. This may be an efficient way of achieving physical activity guidelines and improving population health.
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Project description:In France, new cancer cases keep on increasing with around 150 000 deaths yearly. Cancer therapy research is constantly evolving. Indeed, several studies explore new treatments or their combination with conventional cancer treatments. But, at the same time, complementary and alternative medicines, as osteopathy, remain little explored upon their role in the combination with conventional therapy.
Several studies showed indirect interaction between vagus nerve and cancer. Firstly, vagus nerve regulates homeostasis and immunity by reducing systemic inflammation while maintaining local inflammation and antitumor effects. Secondly, vagus nerve stimulation increases Heart Rate Variability (HRV). Moreover, a higher HRV is associated with an improvement of vital prognosis in cancer patients. Vagus nerve could be stimulated by noninvasive osteopathic manipulations.
This prospective, monocentric and randomized study is a collaboration between the Centre Hospitalier d’Avignon and the Institut de Formation en Ostéopathie du Grand Avignon. It focuses on using noninvasive osteopathic mobilizations to stimulate vagus nerve. Indeed, this study aims to evaluate effects of vagus nerve osteopathic stimulations on HRV in patients with lung cancer, colorectal cancer, Non Hodgkin Lymphoma or Multiple Myeloma. More specifically, this study will tell us whether vagus nerve noninvasive osteopathic stimulations induce increase of HRV associated with a decrease of systemic inflammation and an improvement of patient’s quality of life.
| 2364606 | ecrin-mdr-crc
Project description:Heart rate variability (HRV) provides insight into cardiovascular health and autonomic function. Electrocardiography (ECG) provides gold standard HRV measurements but is inconvenient for continuous acquisition when monitored from the extremities. Optical techniques such as photoplethysmography (PPG), often found in health and wellness trackers for heart rate measurements, have been used to estimate HRV peripherally but decline in accuracy during increased physical stress. Speckleplethysmography (SPG) is a recently introduced optical technique that provides benefits over PPG, such as increased signal amplitude and reduced susceptibility to temperature-induced vasoconstriction. In this research, we compare SPG and PPG to ECG for estimation of HRV during an orthostatic challenge performed by 17 subjects. We find that SPG estimations of HRV are highly correlated to ECG HRV for both time and frequency domain parameters and provide increased accuracy over PPG estimations of HRV. The results suggest SPG measurements are a viable alternative for HRV estimation when ECG measurements are impractical.
| S-EPMC6773734 | biostudies-literature
Project description:The study examined kinematic parameters and their inter- and intrasubject variability in the topspin forehand of seven top-level table tennis players. A wireless inertial measurement unit (IMU) system measured the movement of the playing hand to analyze the Ready position, Backswing, and Forward events, and a racket-mounted piezoelectric sensor captured the racket-ball Contact. In a four-phase cycle (Backswing, Hitting, Followthrough, and Back to Ready position), body sensors recorded the cycle and phase duration; angles in the sagittal plane at the shoulder, elbow, and wrist of the playing hand and at the knee joints; and acceleration of the playing hand at the moment of racket-ball contact. The coefficient of variation (CV) was calculated to determine the variability of kinematic parameters within and between players. The observed variability in stroke time duration was low (CV < 20%) indicating constancy. The small-to-medium intraindividual variability of angles (CV < 40%) indicates that each player used a broadly repeatable technique. The large intraindividual variability in movement was probably functional (i.e., motor adjustment and injury avoidance). Interindividual and intraindividual variability of knee and elbow angles was low; wrist extension was the most variable parameter (CV > 40%) for all tasks, and shoulder joint variability was medium-to-large. Variability in hand acceleration was low (CV < 20%). Individual players achieved relatively constant hand acceleration at the moment of contact, possibly because angular changes at one joint (e.g., shoulder) could be compensated for by changes at another (e.g., wrist). These findings can help to guide the teaching-learning process and to individualize the training process.
| S-EPMC7238326 | biostudies-literature