Optimal Metamodeling to Interpret Activity-Based Health Sensor Data

2017 ◽  
Author(s):  
Souma Chowdhury ◽  
Ali Mehmani
Keyword(s):  
Physical Activity ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Medical Diagnostics ◽  
Sensor Data ◽  
Sensitivity Testing ◽  
Data Set ◽  
Functional Relationships ◽  
Median Error ◽  
The Impact

Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features — heart rate, QRS time, and QR ratio in each heartbeat period — models with median error of <25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.

scholarly journals Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020 ◽  
Vol 10 (20) ◽  
pp. 7122
Author(s):  
Ahmad Jalal ◽  
Mouazma Batool ◽  
Kibum Kim
Keyword(s):  
Physical Activity ◽  
Human Health ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Tree Classifier ◽  
Combined Features

The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

scholarly journals The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review (Preprint)

2020 ◽  
Author(s):  
Ulrikke Lyng Beauchamp ◽  
Helle Pappot ◽  
Cecilie Holländer-Mieritz
Keyword(s):  
Physical Activity ◽  
Clinical Trials ◽  
Cancer Treatment ◽  
Controlled Trial ◽  
Severe Disease ◽  
Vital Signs ◽  
Sensor Data ◽  
Special Focus ◽  
Oncological Treatment ◽  
Patient Reported

BACKGROUND Interest in the use of wearables in medical care is increasing. Wearables can be used to monitor different variables, such as vital signs and physical activity. A crucial point for using wearables in oncology is if patients already under the burden of severe disease and oncological treatment can accept and adhere to the device. At present, there are no specific recommendations for the use of wearables in oncology, and little research has examined the purpose of using wearables in oncology. OBJECTIVE The purpose of this review is to explore the use of wearables in clinical trials during cancer treatment, with a special focus on adherence. METHODS PubMed and EMBASE databases were searched prior and up to October 3, 2019, with no limitation in the date of publication. The search strategy was aimed at studies using wearables for monitoring adult patients with cancer during active antineoplastic treatment. Studies were screened independently by 2 reviewers by title and abstract, selected for inclusion and exclusion, and the full-text was assessed for eligibility. Data on study design, type of wearable used, primary outcome, adherence, and device outcome were extracted. Results were presented descriptively. RESULTS Our systematic search identified 1269 studies, of which 25 studies met our inclusion criteria. The types of cancer represented in the studies were breast (7/25), gastrointestinal (4/25), lung (4/25), and gynecologic (1/25); 9 studies had multiple types of cancer. Oncologic treatment was primarily chemotherapy (17/25). The study-type distribution was pilot/feasibility study (12/25), observational study (10/25), and randomized controlled trial (3/25). The median sample size was 40 patients (range 7-180). All studies used a wearable with an accelerometer. Adherence varied across studies, from 60%-100% for patients wearing the wearable/evaluable sensor data and 45%-94% for evaluable days, but was differently measured and reported. Of the 25 studies, the most frequent duration for planned monitoring with a wearable was 8-30 days (13/25). Topics for wearable outcomes were physical activity (19/25), circadian rhythm (8/25), sleep (6/25), and skin temperature (1/25). Patient-reported outcomes (PRO) were used in 17 studies; of the 17 PRO studies, only 9 studies reported correlations between the wearable outcome and the PRO. CONCLUSIONS We found that definitions of outcome measures and adherence varied across studies, and limited consensus among studies existed on which variables to monitor during treatment. Less heterogeneity, better consensus in terms of the use of wearables, and established standards for the definitions of wearable outcomes and adherence would improve comparisons of outcomes from studies using wearables. Adherence, and the definition of such, seems crucial to conclude on data from wearable studies in oncology. Additionally, research using advanced wearable devices and active use of the data are encouraged to further explore the potential of wearables in oncology during treatment. Particularly, randomized clinical studies are warranted to create consensus on when and how to implement in oncological practice.

scholarly journals Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study

10.2196/30863 ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. e30863
Author(s):  
Marjolein E Haveman ◽  
Mathilde C van Rossum ◽  
Roswita M E Vaseur ◽  
Claire van der Riet ◽  
Richte C L Schuurmann ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Concurrent Validity ◽  
Daily Life ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Living Environment ◽  
Mean Difference ◽  
Data Availability ◽  
Daily Life Activities

Background Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients’ baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. Objective The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. Methods Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). Results A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. Conclusions Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring.

scholarly journals Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study (Preprint)

2021 ◽  
Author(s):  
Marjolein E Haveman ◽  
Mathilde C van Rossum ◽  
Roswita M E Vaseur ◽  
Claire van der Riet ◽  
Richte C L Schuurmann ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Concurrent Validity ◽  
Daily Life ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Living Environment ◽  
Mean Difference ◽  
Data Availability ◽  
Daily Life Activities

BACKGROUND Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients’ baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. OBJECTIVE The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. METHODS Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO<sub>2</sub>), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). RESULTS A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO<sub>2</sub> measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO<sub>2</sub> (&gt;1%), and overestimated temperature up to 2.9 °C. CONCLUSIONS Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring. CLINICALTRIAL

scholarly journals Wearable Internet-of-Things platform for human activity recognition and health care

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772091156 ◽  
Author(s):  
Asif Iqbal ◽  
Farman Ullah ◽  
Hafeez Anwar ◽  
Ata Ur Rehman ◽  
Kiran Shah ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Wearable Sensors ◽  
Recognition Rate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Set

We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

Secular trends 2013–2017 in overweight and visible dental decay in New Zealand preschool children: influence of ethnicity, deprivation and the Under-5-Energize nutrition and physical activity programme

2018 ◽  
Vol 10 (3) ◽  
pp. 345-352
Author(s):  
Elaine Rush ◽  
Madeline Kirk ◽  
Priya Parmar ◽  
Leanne Young ◽  
Vladimir Obolonkin
Keyword(s):  
Physical Activity ◽  
New Zealand ◽  
Early Life ◽  
Overweight And Obesity ◽  
World Health ◽  
Dental Decay ◽  
Data Set ◽  
Prevalence Of Obesity ◽  
Nutrition And Physical Activity ◽  
The Impact

AbstractEarly-life intervention to reduce obesity and poor dental health through early-life nutrition will improve health outcomes in later life. This study examined the prevalence of overweight and obesity and visual dental decay in 4-year old children in New Zealand between 2013 and 2017, and the impact of a nutrition and physical activity intervention programme, Under-5-Energize (U5E), on prevalence of these conditions within ethnic groups and by deprivation. The data set included 277,963 4-year-old children, including 25,140 from the Waikato region children of whom 8067 attended one of the 121 early childhood centres (ECC) receiving the U5E programme from 2014. Purposively the U5E-ECC selected were attended by higher proportions of indigenous Māori children and children living in higher deprivation areas than non-U5E-ECC. From 2013 to 2017, the overall prevalence of obesity, as defined by World Health Organisation criteria, declined slightly but rates of dental decay did not change. In the Waikato region, the prevalence of obesity declined in non-Māori children from 2015 to 2017 and children attending U5E-ECC had lower rates of dental decay than non-U5E children. Binary logistic regression showed that between 2015 and 2017 visible dental decay was more likely in children who were Māori (3.06×3.17), living in high deprivation (1.54×1.66) and male (1.10) but less likely if attending an U5E-ECC (0.83×0.79). Early-life intervention had efficacy at reducing dental decay, and demonstrated that the origins of disparities in health such as ethnicity and deprivation need to be addressed further to break the intergenerational cycles of poor health.

scholarly journals A multimodal analysis of physical activity, sleep, and work shift in nurses with wearable sensor data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiantian Feng ◽  
Brandon M. Booth ◽  
Brooke Baldwin-Rodríguez ◽  
Felipe Osorno ◽  
Shrikanth Narayanan
Keyword(s):  
Physical Activity ◽  
Life Satisfaction ◽  
Sleep Quality ◽  
Work Performance ◽  
Wearable Sensors ◽  
Night Shift ◽  
Hospital Setting ◽  
Sensor Data ◽  
Large Hospital ◽  
Circadian Misalignment

AbstractNight shift workers are often associated with circadian misalignment and physical discomfort, which may lead to burnout and decreased work performance. Moreover, the irregular work hours can lead to significant negative health outcomes such as poor eating habits, smoking, and being sedentary more often. This paper uses commercial wearable sensors to explore correlates and differences in the level of physical activity, sleep, and circadian misalignment indicators among day shift nurses and night shift nurses. We identify which self-reported assessments of affect, life satisfaction, and sleep quality, are associated with physiological and behavioral signals captured by wearable sensors. The results using data collected from 113 nurses in a large hospital setting, over a period of 10 weeks, indicate that night shift nurses are more sedentary, and report lower levels of life satisfaction than day-shift nurses. Moreover, night shift nurses report poorer sleep quality, which may be correlated with challenges in their attempts to fall asleep on off-days.

scholarly journals The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review

10.2196/22006 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e22006
Author(s):  
Ulrikke Lyng Beauchamp ◽  
Helle Pappot ◽  
Cecilie Holländer-Mieritz
Keyword(s):  
Physical Activity ◽  
Clinical Trials ◽  
Cancer Treatment ◽  
Controlled Trial ◽  
Severe Disease ◽  
Vital Signs ◽  
Sensor Data ◽  
Special Focus ◽  
Oncological Treatment ◽  
Patient Reported

Background Interest in the use of wearables in medical care is increasing. Wearables can be used to monitor different variables, such as vital signs and physical activity. A crucial point for using wearables in oncology is if patients already under the burden of severe disease and oncological treatment can accept and adhere to the device. At present, there are no specific recommendations for the use of wearables in oncology, and little research has examined the purpose of using wearables in oncology. Objective The purpose of this review is to explore the use of wearables in clinical trials during cancer treatment, with a special focus on adherence. Methods PubMed and EMBASE databases were searched prior and up to October 3, 2019, with no limitation in the date of publication. The search strategy was aimed at studies using wearables for monitoring adult patients with cancer during active antineoplastic treatment. Studies were screened independently by 2 reviewers by title and abstract, selected for inclusion and exclusion, and the full-text was assessed for eligibility. Data on study design, type of wearable used, primary outcome, adherence, and device outcome were extracted. Results were presented descriptively. Results Our systematic search identified 1269 studies, of which 25 studies met our inclusion criteria. The types of cancer represented in the studies were breast (7/25), gastrointestinal (4/25), lung (4/25), and gynecologic (1/25); 9 studies had multiple types of cancer. Oncologic treatment was primarily chemotherapy (17/25). The study-type distribution was pilot/feasibility study (12/25), observational study (10/25), and randomized controlled trial (3/25). The median sample size was 40 patients (range 7-180). All studies used a wearable with an accelerometer. Adherence varied across studies, from 60%-100% for patients wearing the wearable/evaluable sensor data and 45%-94% for evaluable days, but was differently measured and reported. Of the 25 studies, the most frequent duration for planned monitoring with a wearable was 8-30 days (13/25). Topics for wearable outcomes were physical activity (19/25), circadian rhythm (8/25), sleep (6/25), and skin temperature (1/25). Patient-reported outcomes (PRO) were used in 17 studies; of the 17 PRO studies, only 9 studies reported correlations between the wearable outcome and the PRO. Conclusions We found that definitions of outcome measures and adherence varied across studies, and limited consensus among studies existed on which variables to monitor during treatment. Less heterogeneity, better consensus in terms of the use of wearables, and established standards for the definitions of wearable outcomes and adherence would improve comparisons of outcomes from studies using wearables. Adherence, and the definition of such, seems crucial to conclude on data from wearable studies in oncology. Additionally, research using advanced wearable devices and active use of the data are encouraged to further explore the potential of wearables in oncology during treatment. Particularly, randomized clinical studies are warranted to create consensus on when and how to implement in oncological practice.

scholarly journals Quantifying Caregiver Movement when Measuring Infant Movement across a Full Day: A Case Report

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2886 ◽  
Author(s):  
Judy Zhou ◽  
Sydney Y. Schaefer ◽  
Beth A. Smith
Keyword(s):  
Physical Activity ◽  
Case Report ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Future Research ◽  
Wearable Sensor ◽  
Sensor Signal ◽  
Background Motion ◽  
Full Day ◽  
Leg Movements

There is interest in using wearable sensors to measure infant movement patterns and physical activity, however, this approach is confounded by caregiver motion. The purpose of this study is to estimate the extent that caregiver motion confounds wearable sensor data in full-day studies of infant leg movements. We used wearable sensors to measure leg movements of a four-month-old infant across 8.5 hours, during which the infant was handled by the caregiver in a typical manner. A researcher mimicked the actions of the caregiver with a doll. We calculated 7744 left and 7107 right leg movements for the infant and 1013 left and 1115 right “leg movements” for the doll. In this case, approximately 15% of infant leg movements can be attributed to background motion of the caregiver. This case report is the first step toward removing caregiver-produced background motion from the infant wearable sensor signal. We have estimated the size of the effect and described the activities that were related to noise in the signal. Future research can characterize the noise in detail and systematically explore different methods to remove it.

scholarly journals Assessment of Fatigue Using Wearable Sensors: A Pilot Study

2020 ◽  
Vol 4 (1) ◽  
pp. 59-72
Author(s):  
Hongyu Luo ◽  
Pierre-Alexandre Lee ◽  
Ieuan Clay ◽  
Martin Jaggi ◽  
Valeria De Luca
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Heart Rate ◽  
Time Series ◽  
Random Forest ◽  
Vital Signs ◽  
Mental Fatigue ◽  
Digital Data ◽  
Sensor Data ◽  
Series Data

<b><i>Background:</i></b> Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. <b><i>Methods:</i></b> After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. <b><i>Results:</i></b> A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. <b><i>Conclusion:</i></b> Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.

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