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

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 Habitual physical activity in daily life correlates positively with markers for mitochondrial capacity

2008 ◽  
Vol 105 (2) ◽  
pp. 561-568 ◽  
Author(s):  
Marcel den Hoed ◽  
Matthijs K. C. Hesselink ◽  
Gerrit P. J. van Kranenburg ◽  
Klaas R. Westerterp
Keyword(s):  
Physical Activity ◽  
Citrate Synthase ◽  
Daily Life ◽  
Vastus Lateralis ◽  
Habitual Physical Activity ◽  
Vastus Lateralis Muscle ◽  
Mitochondrial Density ◽  
Daily Life Activities ◽  
Intensity Physical Activity ◽  
Mitochondrial Capacity

Physical exercise training is a powerful tool to maintain or improve mitochondrial density and function (mitochondrial capacity). This study aims to determine whether mitochondrial capacity is also associated with habitual physical activity in daily life (PADL). The capacity of classic markers for mitochondrial density, i.e., the capacity of citrate synthase (CS) and succinate dehydrogenase (SDH), as well the capacity of cytochrome c oxidase (COX) and β-hydroxyacyl-CoA dehydrogenase (HAD), was determined in homogenized muscle biopsy samples obtained from the vastus lateralis muscle of nonexercising healthy young (age 20 ± 2 yr) subjects (31 women, 7 men). PADL was measured during two periods of 14 days using a triaxial accelerometer for movement registration. CS, SDH, and COX were positively associated with PADL [ P < 0.05, R = 0.36, 95% confidence interval (CI): 1.3·10−4 to 2.2·10−3; P < 0.05, R = 0.39, 95% CI: 1.1·10−5 to 9.9·10−5; and P < 0.05, R = 0.33, 95% CI: 7.5·10−6 to 3.6·10−4, respectively], and HAD tended to correlate positively with PADL ( P = 0.06, R = 0.31, 95% CI: −2.2·10−5 to 1.1·10−3). The population was subsequently stratified based on the intensity of the activities performed. CS was only associated with PADL in subjects spending more time on high-intensity physical activity, whereas HAD was only associated with PADL in subjects spending less time on low intensity physical activity. We are the first to report that even within the range of normal daily life activities, mitochondrial capacity is positively associated with the level of habitual physical activity in daily life. Thus an active lifestyle may help to maintain or improve mitochondrial capacity.

Step Count Validation of a Tri-Axial Accelerometer During Walking and Jogging

2012 ◽  
Author(s):  
Emma Fortune ◽  
Vipul Lugade ◽  
Melissa Morrow ◽  
Kenton Kaufman
Keyword(s):  
Physical Activity ◽  
Wearable Sensors ◽  
Living Environment ◽  
Activity Levels ◽  
Light Weight ◽  
Free Living ◽  
Step Counts ◽  
Step Counting ◽  
High Level ◽  
Very High

Gait analysis is an important tool in assessing the health and activity levels of patients and regular physical activity has been associated with health improvements in a number of populations. Step counting is one of the most commonly used measures of physical activity [1] and many studies have investigated the use of wearable sensors for step counts [2–4]. Their small size and light weight mean that they may be used in a free living environment and are suitable for home deployment. One of the main issues associated with step counts as a measure of physical activity is that a very high level of accuracy in step detection is needed.

scholarly journals Estimating dyskinesia severity in Parkinson’s disease by using a waist-worn sensor: concurrent validity study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Alejandro Rodríguez-Molinero ◽  
Carlos Pérez-López ◽  
Albert Samà ◽  
Daniel Rodríguez-Martín ◽  
Sheila Alcaine ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Correlation Coefficient ◽  
Concurrent Validity ◽  
Rating Scale ◽  
Daily Life ◽  
Sensor Output ◽  
Daily Life Activities ◽  
Sensor Device ◽  
Spearman’S Correlation

Abstract Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson’s disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson’s patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient’s activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician’s assessment and the sensor output was analyzed with the Spearman’s correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33–0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76–0.97: p < 0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.

scholarly journals The benefits of physical activity and exercise on physical, cognitive and daily life activities in aging adults

2019 ◽  
Vol 10 (1) ◽  
pp. 59-71
Author(s):  
Dorjana Zerbo
Keyword(s):  
Physical Activity ◽  
Successful Aging ◽  
Daily Life ◽  
Well Being ◽  
Combined Training ◽  
Daily Life Activities ◽  
Healthy Elderly ◽  
Physical Activity And Exercise ◽  
Aging Adults ◽  
The Impact

Due to increased longevity, degenerative diseases and disabilities have become one of the largest health-care problems. The state of well-being with a low risk of premature health problems is important for successful aging. Even if the impact of physical activity and exercise on performance of daily life activities is still poorly understood, it seems that regular training has important benefits on physical and cognitive functioning in healthy elderly population. Combined training, including strength, balance, flexibility exercises and activities that improve cardiorespiratory fitness, are important to ensure the independency of elderly people.

scholarly journals Daily-life Physical Activity of Young Healthy Adults Associates with Function and Structure of the Hippocampus

2021 ◽  
Author(s):  
Sara Seoane ◽  
Laura Ezama ◽  
Niels Janssen
Keyword(s):  
Physical Activity ◽  
Internal Structure ◽  
Leisure Time ◽  
Daily Life ◽  
Healthy Adults ◽  
Daily Lives ◽  
Daily Life Activities ◽  
Daily Life Physical Activity ◽  
And Function ◽  
The Impact

Abstract Research on the impact of physical activity (PA) has shown that PA produces changes in the structure and function of a brain structure called the hippocampus. There are three main limitations in this research. First, the majority of the work has been carried out in elderly populations and as such, there is a paucity of research on the impact of PA on the brains of healthy young individuals. Second, whereas PA is typically assessed through controlled interventions, changes in the brain due to PA as performed during daily-life activities has not been explored. Finally, the hippocampus has a complex internal structure and the impact of PA on this internal structure is unclear. Here we examined how structural and functional aspects of the hip-pocampus are associated with habitual PA performed during work, leisure time and sports in the daily lives of young healthy adults. We found that PA performed during work time correlated with increased subicular volumes and with changed functional connectivity between a location in middle/posterior hippocampus and regions of the default mode network and between a location in anterior hippocampus and regions of the somatomotor network. No effects of PA performed during leisure time and sports were found. The results generalize the impact of PA to younger populations and show how PA performed in daily-life situations correlates with the precise internal structure of the hippocampus.

scholarly journals Off-Training Levels of Physical Activity and Sedentary Behavior in Young Athletes: Preliminary Results during a Typical Week

Sports ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 141 ◽  
Author(s):  
Juliana Exel ◽  
Nuno Mateus ◽  
Bruno Travassos ◽  
Bruno Gonçalves ◽  
Isabel Gomes ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cluster Analysis ◽  
Sedentary Behavior ◽  
Daily Life ◽  
Young Athletes ◽  
Preliminary Results ◽  
Daily Life Activities ◽  
Quality Of Recovery ◽  
Health Related

The level of physical activity (PA) and sedentary behavior (SED) off-training of young athletes may reveal the quality of recovery from training and highlight health related issues. Thus, the aim was to identify and describe young athletes’ PA and SED off-training, according to daily life activities. Eight athletes (15.7 ± 2 years, 1.72 ± 0.6 m height, 62.9 ± 10.2 kg) of a sport talent program wore on their waist a tri-axial accelerometer (ActiGraph® wGT9X-link, Shalimar, FL, USA) at 30 Hz for 15 consecutive days, and reported their schedule. A two-step cluster analysis classified three groups according to sedentary PA and MVPA. The Sedentary (56.9%), presented the highest sedentary PA (mean [CI], 37.37 [36.45–38.29] min/hour); The Hazardous (19.4%) had the lowest values of sedentary and MVPA (10.07 [9.41–10.36] min/hour and 8.67 [7.64–9.70] min/hour, respectively). Balanced (23.7%) had the highest MVPA (28.61 [27.16–30.07] min/hour). Sedentary had the lowest count of home time associated (20%) and higher school (26%) time when compared to the Hazardous (13%). The Balanced showed the highest count of school (61%) and home time (47%). Different profiles for young athletes revealed alarming behavior in the associations with sedentary PA, sitting and SED breaks, which may influence performance and health.

scholarly journals Estimating Walking Speed in the Wild

2020 ◽  
Vol 2 ◽  
Author(s):  
Loubna Baroudi ◽  
Mark W. Newman ◽  
Elizabeth A. Jackson ◽  
Kira Barton ◽  
K. Alex Shorter ◽  
...  
Keyword(s):  
Physical Activity ◽  
Clinical Decision Making ◽  
Walking Speed ◽  
Wearable Sensors ◽  
Clinical Decision ◽  
Living Environment ◽  
Activity Level ◽  
Measurement Unit ◽  
Battery Life ◽  
Free Living

An individual's physical activity substantially impacts the potential for prevention and recovery from diverse health issues, including cardiovascular diseases. Precise quantification of a patient's level of day-to-day physical activity, which can be characterized by the type, intensity, and duration of movement, is crucial for clinicians. Walking is a primary and fundamental physical activity for most individuals. Walking speed has been shown to correlate with various heart pathologies and overall function. As such, it is often used as a metric to assess health performance. A range of clinical walking tests exist to evaluate gait and inform clinical decision-making. However, these assessments are often short, provide qualitative movement assessments, and are performed in a clinical setting that is not representative of the real-world. Technological advancements in wearable sensing and associated algorithms enable new opportunities to complement in-clinic evaluations of movement during free-living. However, the use of wearable devices to inform clinical decisions presents several challenges, including lack of subject compliance and limited sensor battery life. To bridge the gap between free-living and clinical environments, we propose an approach in which we utilize different wearable sensors at different temporal scales and resolutions. Here, we present a method to accurately estimate gait speed in the free-living environment from a low-power, lightweight accelerometer-based bio-logging tag secured on the thigh. We use high-resolution measurements of gait kinematics to build subject-specific data-driven models to accurately map stride frequencies extracted from the bio-logging system to stride speeds. The model-based estimates of stride speed were evaluated using a long outdoor walk and compared to stride parameters calculated from a foot-worn inertial measurement unit using the zero-velocity update algorithm. The proposed method presents an average concordance correlation coefficient of 0.80 for all subjects, and 97% of the error is within ±0.2m· s−1. The approach presented here provides promising results that can enable clinicians to complement their existing assessments of activity level and fitness with measurements of movement duration and intensity (walking speed) extracted at a week time scale and in the patients' free-living environment.

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