Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features.

In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability. This study aims to evaluate the possibility of a stable, flexible, and convenient-to-use bio-key using ECGs. We propose an approach that minimizes biosignal variability using normalization, clustering-based binarization, and the fuzzy extractor, enabling the generation of personalized seeds and offering ease of use. The proposed method achieved a maximum entropy of 0.99 and an authentication accuracy of 95%. This study evaluated various parameter combinations for generating effective bio-keys for personal authentication and proposed the optimal combination. Our research holds potential for security technologies applicable to wearable devices and healthcare systems.

Effects of Game-Related Tasks for the Diagnosis and Classification of Gaming Disorder.

Gaming disorder (GD) is an addictive behavior characterized by an insatiable need to play video games and shares similar symptoms with the failure of self-control due to a decline in cognitive function. Current GD diagnostic and screening tools rely on questionnaires and behavioral observations related to cognitive functions to assess an individual's capacity to maintain self-control in everyday life. However, current GD screening approaches rely on subjective symptoms, and a reliable diagnosis requires long-term clinical follow-up. Recent studies have measured biosignals along with cognitive functional tasks to provide objectivity to GD diagnosis and to acquire immediate results. However, people with GD are hypersensitive to game-related cues, so their responses may vary depending on the type of stimuli, and the difference in response to stimuli might manifest as a difference in the degree of change in the biosignal. Therefore, it is critical to choose the correct stimulus type when performing GD diagnostic tasks. In this study, we investigated the task dependence of cognitive decline in GD by comparing two cognitive functional tasks: a continuous performance task (CPT) and video game play. For this study, 69 young male adults were classified into either the gaming disorder group (GD, n = 39) or a healthy control group (HC, n = 30). CPT score, EEG signal (theta, alpha, and beta), and HRV-HF power were assessed. We observed differences in the left frontal region (LF) of the brain between the GD and HC groups during online video game play. The GD group also showed a significant difference in HF power of HRV between CPT and online video gaming. Furthermore, LF and HRV-HF significantly correlated with Young's Internet Addiction Test (Y-IAT) score, which is positively associated with impulsivity score. The amount of change in theta band activity in LF and HRV-HF-both biomarkers for changes in cognitive function-during online video game play suggests that people with GD express task-dependent cognitive decline compared with HC. Our results demonstrate the feasibility of quantifying individual self-regulation ability for gaming and underscore its importance for GD classification.

Quantitative analysis of pulse arrival time and PPG morphological features based cuffless blood pressure estimation: a comparative study between diabetic and non-diabetic groups.

Pulse arrival time (PAT) and PPG morphological features have attracted much interest in cuffless blood pressure (BP) estimation, but their effects are not clearly understood when vascular characteristics are affected by diseases such as diabetes. This work quantitatively analyzes the effect of diabetic disease on the PAT and PPG morphological features-based BP estimation. We selected 112 diabetic patients and 308 non-diabetic subjects from VitalDB, and extracted 16 features including PAT, PPG morphological features, and heart rate. BP estimation performance was statistically compared between groups using linear regression models with several feature sets, and the relative importance of each feature in the optimal feature set was extracted. As a result, the standard deviation of the error and mean absolute error of PAT-based BP estimation were significantly higher in the diabetic group than in the non-diabetic group (p < 0.01). A feature set containing PAT and PPG morphological features achieved the best performance in both groups. However, the relative importance of each feature for BP estimation differed notably between groups. The results indicate that different features are important depending on the vascular characteristics, which could help to construct different models to accommodate specific diseases.

Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method.

Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems.

Prognostic Impact of In-Hospital Use of Mechanical Cardiopulmonary Resuscitation Devices Compared with Manual Cardiopulmonary Resuscitation: A Nationwide Population-Based Observational Study in South Korea.

Background and Objectives: This study analyzed the prognostic impact of mechanical cardiopulmonary resuscitation (CPR) devices in out-of-hospital cardiac arrest (OHCA) patients, in comparison to manual CPR. Materials and Methods: This study was a nationwide population-based observational study in South Korea. Data were retrospectively collected from 142,905 OHCA patients using the South Korean Out-of-Hospital Cardiac Arrest Surveillance database. We included adult OHCA patients who received manual or mechanical CPR in the emergency room. The primary outcome was survival at discharge and the secondary outcome was sustained return of spontaneous circulation (ROSC). Statistical analysis included propensity score matching and multivariate logistic regression. Results: A total of 19,045 manual CPR and 1125 mechanical CPR cases (671 AutoPulseTM vs. 305 ThumperTM vs. 149 LUCASTM) were included. In the matched multivariate analyses, all mechanical CPR devices were associated with a lower ROSC than that of manual CPR. AutoPulseTM was associated with lower survival in the multivariate analysis after matching (aOR with 95% CI: 0.57 (0.33-0.96)), but the other mechanical CPR devices were associated with similar survival to discharge as that of manual CPR. Witnessed arrest was commonly associated with high ROSC, but the use of mechanical CPR devices and cardiac origin arrest were associated with low ROSC. Only target temperature management was the common predictor for high survival. Conclusions: The mechanical CPR devices largely led to similar survival to discharge as that of manual CPR in OHCA patients; however, the in-hospital use of the AutoPulseTM device for mechanical CPR may significantly lower survival compared to manual CPR.

ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions.

The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.

Impact of a Smart-Ring-Based Feedback System on the Quality of Chest Compressions in Adult Cardiac Arrest: A Randomized Preliminary Study.

This study aimed to assess the effectiveness of a novel chest compression (CC) smart-ring-based feedback system in a manikin simulation. In this randomized, crossover, controlled study, we evaluated the effect of smart-ring CC feedback on cardiopulmonary resuscitation (CPR). The learnability and usability of the tool were evaluated with the System Usability Scale (SUS). Participants were divided into two groups and each performed CCs with and without feedback 2 weeks apart, using different orders. The primary outcome was compression depth; the proportion of accurate-depth (5-6 cm) CCs, CC rate, and the proportion of complete CCs (≤1 cm of residual leaning) were assessed additionally. The feedback group and the non-feedback group showed significant differences in compression depth (52.1 (46.3-54.8) vs. 47.1 (40.5-49.9) mm, p = 0.021). The proportion of accurate-depth CCs was significantly higher in the interventional than in the control condition (88.7 (30.0-99.1) vs. 22.6 (0.0-58.5%), p = 0.033). The mean SUS score was 83.9 ± 8.7 points. The acceptability ranges were 'acceptable', and the adjective rating was 'excellent'. CCs with smart-ring feedback could help achieve the ideal range of depth during CPR. The smart-ring may be a valuable source of CPR feedback.

Effects of mobile messenger counseling on case management success for individuals engaging in self-harm or suicide attempts who were discharged from emergency departments.

OBJECTIVE: Postdischarge case management for self-harm or suicide attempters often fails; therefore, this study aimed to investigate the effects of mobile messenger counseling (MMC) on the postdischarge case management results among this patient group. METHODS: A retrospective analysis was done with data collected from March 2015 to February 2020 that included self-harm or suicide attempters who had visited a Korean emergency department and were discharged. If patients consented, postdischarge case management and MMC were conducted from March 2017. The primary outcome was the rate of successful case management, which reflects the patients either connecting to a local psychiatric healthcare center or undergoing a follow-up at a neuropsychiatric outpatient department at least once following discharge. Using univariate and multivariate logistic regression analyses, we evaluated MMC's effects on these patients' postdischarge case management. RESULTS: Of 913 patients, 604 participated in this study. In terms of successful case management, the MMC group showed a significantly higher rate than the non-MMC one (28.3% vs. 16.1%, P=0.001). A multivariate analysis demonstrated that access to postdischarge MMC (odds ratio, 2.149; 95% confidence interval, 1.357-3.403; P=0.001) and giving consent for case management while in the emergency department were significantly associated with successful case management (odds ratio, 8.917; 95% confidence interval, 5.610-14.173; P<0.001). CONCLUSION: The use of MMC for self-harm or suicide attempters is associated with higher case management success rates by increasing their chances of connecting to a psychiatric healthcare center or a neuropsychiatric outpatient department.

Development of Smart-Ring-Based Chest Compression Depth Feedback Device for High Quality Chest Compressions: A Proof-of-Concept Study.

Recently, a smart-device-based chest compression depth (CCD) feedback system that helps ensure that chest compressions have adequate depth during cardiopulmonary resuscitation (CPR) was developed. However, no CCD feedback device has been developed for infants, and many feedback systems are inconvenient to use. In this paper, we report the development of a smart-ring-based CCD feedback device for CPR based on an inertial measurement unit, and propose a high-quality chest compression depth estimation algorithm that considers the orientation of the device. The performance of the proposed feedback system was evaluated by comparing it with a linear variable differential transformer in three CPR situations. The experimental results showed compression depth errors of 2.0 ± 1.1, 2.2 ± 0.9, and 1.4 ± 1.1 mm in the three situations. In addition, we conducted a pilot test with an adult/infant mannequin. The results of the experiments show that the proposed smart-ring-based CCD feedback system is applicable to various chest compression methods based on real CPR situations.

Automated remote decision-making algorithm as a primary triage system using machine learning techniques.

OBJECTIVE: An objective and convenient primary triage procedure is needed for prioritizing patients who need help in mass casualty incident (MCI) situations, where there is a lack of medical staff and available resources. This study aimed to develop an automated remote decision-making algorithm that remotely categorize a patient's emergency level using clinical parameters that can be measured with a wearable device. APPROACH: The algorithm was developed according to the following procedures. First, we used the National Trauma Data Bank data set, a large open trauma patient data set assembled by the American College of Surgeons (ACS). In addition, we performed pre-processing to exclude data when the vital sign or consciousness indicator value was missing or physiologically in an abnormal range. Second, we selected the T-RTS method, which classifies emergency levels into four classes (Delayed, Urgent, Immediate and Dead), as the primary outcome. Third, three machine learning methods widely used in the medical field, logistic regression, random forest, and deep neural network (DNN), were applied to build the algorithm. Finally, each method was evaluated using quantitative performance indicators including the macro-averaged f1 score, macro-averaged mean absolute error (MMAE), and the area under the receiver operating characteristic curve (AUC). MAIN RESULTS: For total sets, the logistic regression had a macro-averaged f1 score of 0.673, an MMAE of 0.387 and an AUC value of 0.844 (95% CI, 0.843-0.845), while the random forest and DNN had macro-averaged f1 scores of 0.783 and 0.784, MMAEs of 0.297 and 0.298 and AUC values of 0.882 (95% CI, 0.881-0.883) and 0.883(95% CI, 0.881-0.884), respectively. SIGNIFICANCE: In a comprehensive analysis of these results, our algorithm demonstrated a viable approach that could be practically adopted in an MCI. In addition, it can be employed to transfer patients and to redistribute available resources according to their priorities.

Effects of knee osteoarthritis severity on inter-joint coordination and gait variability as measured by hip-knee cyclograms.

Inter-joint coordination and gait variability in knee osteoarthritis (KOA) has not been well investigated. Hip-knee cyclograms can visualize the relationship between the hip and knee joint simultaneously. The aim of this study was to elucidate differences in inter-joint coordination and gait variability with respect to KOA severity using hip-knee cyclograms. Fifty participants with KOA (early KOA, n = 20; advanced KOA, n = 30) and 26 participants (≥ 50 years) without KOA were recruited. We analyzed inter-joint coordination by hip-knee cyclogram parameters including range of motion (RoM), center of mass (CoM), perimeter, and area. Gait variability was assessed by the coefficient of variance (CV) of hip-knee cyclogram parameters. Knee RoM was significantly reduced and total perimeter tended to be decreased with KOA progression. KOA patients (both early and advanced) had reduced stance phase perimeter, swing phase area, and total area than controls. Reduced knee CoM and swing phase perimeter were observed only in advanced KOA. Both KOA groups had a greater CV for CoM, knee RoM, perimeter (stance phase, swing phase and total) and swing phase area than the controls. Increased CV of hip RoM was only observed in advanced KOA. These results demonstrate that hip-knee cyclograms can provide insights into KOA patient gait.

Can Mobile Videocall Assist Laypersons' Use of Automated External Defibrillators? A Randomized Simulation Study and Qualitative Analysis.

OBJECTIVE: To investigate the feasibility of mobile videocall guidance to facilitate AED use by laypersons. Design, setting, and participants. A total of 90 laypersons were randomized into three groups: the mobile video call-guided, voice call-guided, and non-guided groups. Participants were exposed to simulated cardiac arrest to use an AED, and guided by video calls, voice calls, or were not guided. We recorded the simulation experiments as a videoclip, and other researchers who were blinded to the simulation assessed the performance according to the prespecified checklist after simulations. Outcomes measure and analysis. We compared the performance score and time intervals from AED arrival to defibrillation among the three groups and analyzed the common errors. RESULTS: There was no significant difference among the three groups in terms of baseline characteristics. Performance scores in the checklist for using AED were higher in the mobile video call-guided group, especially in the category of "Power on AED" and "Correctly attaches pads" than in the other groups. However, the time interval to defibrillation was significantly longer in the mobile video call-guided group. CONCLUSIONS: Mobile video call guidance might be an alternative method to facilitate AED use by laypersons. Therefore, further well-designed research is needed to evaluate the feasibility of this approach in OHCA.

Clip-On IMU System for Assessing Age-Related Changes in Hand Functions.

Hand functions affect the instrumental activities of daily living. While functional outcome measures, such as a targeted box and block test, have been widely used in clinical settings and provide a useful measure of overall performance, the advent of a wearable Inertial Measurement Unit(IMU)-based system enables the examination of the specific performance and kinematic parameters of hand movements. This study proposed a novel clip-on IMU system to facilitate the clinically fitted measurements of fine-motor finger and wrist joint movements. Clinical validation was conducted with the aim of characterising age-related changes in hand functions, namely grasping, transporting, and releasing blocks. Eighteen young (age 20-31) and sixteen healthy older adults (age 75-89) were evaluated during the box and block test. The results demonstrated that an older age was characterized by slower movements and higher variations and kinematic alterations in the hand functions, such as a larger range of motions at the fingers as well as kinematic trajectories. The proposed IMU system and subsequent validations highlight the value of the performance and kinematics parameters for a more comprehensive understanding of fine-motor finger and wrist movements that could shed light on further implementations in clinical and practical settings.

Patterns of enhancement in paretic shoulder kinematics after stroke with musical cueing.

Musical cueing has been widely utilised in post-stroke motor rehabilitation; however, the kinematic evidence on the effects of musical cueing is sparse. Further, the element-specific effects of musical cueing on upper-limb movements have rarely been investigated. This study aimed to kinematically quantify the effects of no auditory, rhythmic auditory, and melodic auditory cueing on shoulder abduction, holding, and adduction in patients who had experienced hemiparetic stroke. Kinematic data were obtained using inertial measurement units embedded in wearable bands. During the holding phase, melodic auditory cueing significantly increased the minimum Euler angle and decreased the range of motion compared with the other types of cueing. Further, the root mean square error in the angle measurements was significantly smaller and the duration of movement execution was significantly shorter during the holding phase when melodic auditory cueing was provided than when the other types of cueing were used. These findings indicated the important role of melodic auditory cueing for enhancing movement positioning, variability, and endurance. This study provides the first kinematic evidence on the effects of melodic auditory cueing on kinematic enhancement, thus suggesting the potential use of pitch-related elements in psychomotor rehabilitation.

Remote blood pressure monitoring and behavioral intensification for stroke: A randomized controlled feasibility trial.

Measuring blood pressure (BP) at home and remote monitoring can improve the patient's adherence to BP control and vascular outcomes. This study evaluated the feasibility of a trial regarding the effects of an intensive mobile BP management strategy versus usual care in acute ischemic stroke patients. A feasibility-testing, randomized, open-labeled controlled trial was conducted. Remote BP measurement, data transmission, storage, and centralized monitoring system were organized through a Bluetooth-equipped sphygmomanometer paired to the participants' smartphones. Participants were randomized equally into intensive management (behavioral intensification to measure BP at home by texting, direct telephone call, or breakthrough visit) and control (usual care) groups. The primary feasibility outcomes were: 1) recruitment time for the pre-specified number of participants, 2) retention of participants, 3) frequency of breakthrough visit calls, 4) response to breakthrough visit call, and 5) proportions satisfying BP measurement criteria. Sixty participants were randomly assigned to the intensive management (n = 31) and control (n = 29) groups, of which 57 participants were included in the primary analysis with comparable baseline characteristics. Recruitment time from the first to the last participant was 350 days, and 95% of randomized participants completed the final visit (intensive, 94%; control, 98%). Eight breakthrough visit calls were made to 7 participants (23%), with complete and immediate responses within 3 ± 4 days. The median of half-day blocks fulfilling the BP measurement criteria per patient were 91% in the intensive group and 83% in the control group (difference, 12.2; 95% confidence interval, 2.2-22.2). No adverse events related to the trial procedures were reported. The intensive monitoring, including remote BP measurement, data transfer, and centralized monitoring system, engaged with behavioral intensification was feasible if the patients complied with the intervention. However, the device utilized would need further improvement prior to a large trial.

Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths.

Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running.

A data-driven artificial intelligence model for remote triage in the prehospital environment.

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction.

Altered Heart Rate Variability During Gameplay in Internet Gaming Disorder: The Impact of Situations During the Game.

Internet gaming disorder (IGD) is characterized by a loss of control over gaming and a decline in psychosocial functioning derived from excessive gameplay. We hypothesized that individuals with IGD would show different autonomic nervous system (ANS) responses to the games than those without IGD. In this study, heart rate variability (HRV) was assessed in 21 young males with IGD and 27 healthy controls while playing their favorite Internet game. The subjects could examine the game logs to identify the most and least concentrated periods of the game. The changes in HRV during specific 5-min periods of the game (first, last, and high- and low-attention) were compared between groups via a repeated measures analysis of variance. Significant predictors of HRV patterns during gameplay were determined from stepwise multiple linear regression analyses. Subjects with IGD showed a significant difference from controls in the patterns of vagally mediated HRV, such that they showed significant reductions in high-frequency HRV, particularly during the periods of high attention and the last 5 min, compared with baseline values. A regression analysis showed that the IGD symptom scale score was a significant predictor of this reduction. These results suggest that an altered HRV response to specific gaming situations is related to addictive patterns of gaming and may reflect the diminished executive control of individuals with IGD while playing Internet games.

Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures.

Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS.

Comparing the standards of one metabolic equivalent of task in accurately estimating physical activity energy expenditure based on acceleration.

The purpose of the study is to analyse how the standard of resting metabolic rate (RMR) affects estimation of the metabolic equivalent of task (MET) using an accelerometer. In order to investigate the effect on estimation according to intensity of activity, comparisons were conducted between the 3.5 ml O2 · kg-1 · min-1 and individually measured resting VO2 as the standard of 1 MET. MET was estimated by linear regression equations that were derived through five-fold cross-validation using 2 types of MET values and accelerations; the accuracy of estimation was analysed through cross-validation, Bland and Altman plot, and one-way ANOVA test. There were no significant differences in the RMS error after cross-validation. However, the individual RMR-based estimations had as many as 0.5 METs of mean difference in modified Bland and Altman plots than RMR of 3.5 ml O2 · kg-1 · min-1. Finally, the results of an ANOVA test indicated that the individual RMR-based estimations had less significant differences between the reference and estimated values at each intensity of activity. In conclusion, the RMR standard is a factor that affects accurate estimation of METs by acceleration; therefore, RMR requires individual specification when it is used for estimation of METs using an accelerometer.