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13 pages, 1600 KiB

Open AccessArticle

An Investigation of Surface EMG Shorts-Derived Training Load during Treadmill Running

by Kurtis Ashcroft, Tony Robinson, Joan Condell, Victoria Penpraze, Andrew White and Stephen P. Bird

Sensors 2023, 23(15), 6998; https://doi.org/10.3390/s23156998 - 7 Aug 2023

Cited by 1 | Viewed by 1577

Abstract

The purpose of this study was two-fold: (1) to determine the sensitivity of the sEMG shorts-derived training load (sEMG-TL) during different running speeds; and (2) to investigate the relationship between the oxygen consumption, heart rate (HR), rating of perceived exertion (RPE), accelerometry-based PlayerLoad [...] Read more.

The purpose of this study was two-fold: (1) to determine the sensitivity of the sEMG shorts-derived training load (sEMG-TL) during different running speeds; and (2) to investigate the relationship between the oxygen consumption, heart rate (HR), rating of perceived exertion (RPE), accelerometry-based PlayerLoad^TM (PL), and sEMG-TL during a running maximum oxygen uptake (

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O_2max) test. The study investigated ten healthy participants. On day one, participants performed a three-speed treadmill test at 8, 10, and 12 km·h⁻¹ for 2 min at each speed. On day two, participants performed a

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O_2max test. Analysis of variance found significant differences in sEMG-TL at all three speeds (p < 0.05). A significantly weak positive relationship between sEMG-TL and %

\dot{V}

O_2max (r = 0.31, p < 0.05) was established, while significantly strong relationships for 8 out of 10 participants at the individual level (r = 0.72–0.97, p < 0.05) were found. Meanwhile, the accelerometry PL was not significantly related to %

\dot{V}

O_2max (p > 0.05) and only demonstrated significant correlations in 3 out of 10 participants at the individual level. Therefore, the sEMG shorts-derived training load was sensitive in detecting a work rate difference of at least 2 km·h⁻¹. sEMG-TL may be an acceptable metric for the measurement of internal loads and could potentially be used as a surrogate for oxygen consumption. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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Figure 1
AthosTM unit anterior view (a) and posterior view (set of contacts) (b). Exterior right leg (c) and interior left leg (d) view of sEMG shorts. Note: sEMG dry electrodes and electrode leads are composed of an inkjet-printed conductive polymer comprising an ether-based conductive thermoplastic polyurethane material. The electrodes are overlaid with a soft conductive silicone, which increases the stability of the electrode–skin interface. Full article ">Figure 2
Movements for the sEMG calibration protocol to establish sEMG amplitude thresholds. Movements include prone knee flexion (a), prone hip extension (b), seated knee extension (c), and supine leg raise (d). Full article ">Figure 3
Boxplot showing the distribution of sEMG-TL across different running speeds. sEMG-TL = surface electromyography training Load; a.u. = arbitrary units; low = 8 km⋅h−1; mod = 10 km⋅h−1; high = 12 km⋅h−1; black line = median, and black dots = 1 participant with a very high sEMG-TL. Full article ">Figure 4
Scatterplot matrix of associations between variables: %<math display="inline"><semantics><mrow><mover accent="true"><mrow><mi mathvariant="normal">V</mi></mrow><mo mathvariant="normal">˙</mo></mover></mrow></semantics></math>O2max = percentage of maximum oxygen uptake; sEMG-TL = surface electromyography training load; RPE = rating of perceived exertion; HR = heart rate; PL = PlayerLoad. Each blue dot corresponds to individual measurements at each one-minute stage during the treadmill running test. Solid lines are the least-squares derived best-fitting lines. * p < 0.05; ** p < 0.01. Full article ">

23 pages, 1890 KiB

Open AccessArticle

A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation

by Kapil Singh Rathore, Sricharan Vijayarangan, Preejith SP and Mohanasankar Sivaprakasam

Sensors 2023, 23(3), 1599; https://doi.org/10.3390/s23031599 - 1 Feb 2023

Cited by 1 | Viewed by 2225

Abstract

Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due [...] Read more.

Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model’s parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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15 pages, 1834 KiB

Open AccessArticle

An eDiary App Approach for Collecting Physiological Sensor Data from Wearables together with Subjective Observations and Emotions

by Andreas Petutschnig, Steffen Reichel, Kristýna Měchurová and Bernd Resch

Sensors 2022, 22(16), 6120; https://doi.org/10.3390/s22166120 - 16 Aug 2022

Cited by 5 | Viewed by 2405

Abstract

Field measurement campaigns with traffic participants using wearable sensors and questionnaires can be challenging to carry out because of inconsistent interfaces across manufacturers for accessing sensor data and campaign-specific questionnaire contents bear large potential for errors. We present an app able to consolidate [...] Read more.

Field measurement campaigns with traffic participants using wearable sensors and questionnaires can be challenging to carry out because of inconsistent interfaces across manufacturers for accessing sensor data and campaign-specific questionnaire contents bear large potential for errors. We present an app able to consolidate data from multiple technical sensors and questionnaires. The functionality includes providing feedback for correct sensor platform mounting, accessing and storing all sensor and questionnaire data in a uniform data structure. To do this, the app implements a sensor data bus class that unifies data from technical sensors and questionnaires. The app can also be extended to accommodate other sensor platforms provided they have a suitable API. We also describe a database structure holding the data from multiple campaigns and test subjects in a privacy preserving fashion. Finally, we identify some potential issues and hints that practitioners may encounter when conducting a measurement campaign. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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20 pages, 1203 KiB

Open AccessArticle

A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data

by Maximilian Ehrhart, Bernd Resch, Clemens Havas and David Niederseer

Sensors 2022, 22(16), 5969; https://doi.org/10.3390/s22165969 - 10 Aug 2022

Cited by 18 | Viewed by 5621

Abstract

Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure physiological signals and are, [...] Read more.

Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure physiological signals and are, therefore, suitable for detecting body reactions to stress. The field of machine learning, or more precisely, deep learning, has been able to produce outstanding results. However, in order to produce these good results, large amounts of labeled data are needed, which, in the context of physiological data related to stress detection, are a great challenge to collect, as they usually require costly experiments or expert knowledge. This usually results in an imbalanced and small dataset, which makes it difficult to train a deep learning algorithm. In recent studies, this problem is tackled with data augmentation via a Generative Adversarial Network (GAN). Conditional GANs (cGAN) are particularly suitable for this as they provide the opportunity to feed auxiliary information such as a class label into the training process to generate labeled data. However, it has been found that during the training process of GANs, different problems usually occur, such as mode collapse or vanishing gradients. To tackle the problems mentioned above, we propose a Long Short-Term Memory (LSTM) network, combined with a Fully Convolutional Network (FCN) cGAN architecture, with an additional diversity term to generate synthetic physiological data, which are used to augment the training dataset to improve the performance of a binary classifier for stress detection. We evaluated the methodology on our collected physiological measurement dataset, and we were able to show that using the method, the performance of an LSTM and an FCN classifier could be improved. Further, we showed that the generated data could not be distinguished from the real data any longer. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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The label distribution of our physiological measurement dataset. The left bar is the Moment Of Stress (MOS) class, and the right one is the non-MOS class. Full article ">Figure 2
Prepare and preprocess raw signals for the cGAN and the stress classifier. The red line indicates ST and the blue line indicates GSR. The dotted line in the raw signals and in the filtered signals indicates induced MOS. In the window plot the dotted line indicates split index. Full article ">Figure 3
cGAN workflow. Full article ">Figure 4
LSTM cell. Full article ">Figure 5
The architecture of our conditional GAN. In the input and output figure in (a,b), the blue line indicates GSR and the red line indicates ST, which shows a prototypical MOS. Full article ">Figure 6
Visual comparison of real and generated samples. The red line shows a standardized and filtered 16 s ST signal. The blue line shows a standardized and filtered 16 s GSR signal. There are always two generated and two real signal samples arranged in a 2 × 2 grid. Full article ">Figure 7
The two figures show the results from the t-sne. The red points are the generated points, and the blue points are the real data points. Full article ">

11 pages, 2574 KiB

Open AccessArticle

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

by Bertram Richter, Zachary Mace, Megan E. Hays, Santosh Adhikari, Huy Q. Pham, Robert J. Sclabassi, Benedict Kolber, Saigopalakrishna S. Yerneni, Phil Campbell, Boyle Cheng, Nestor Tomycz, Donald M. Whiting, Trung Q. Le, Toby L. Nelson and Saadyah Averick

Sensors 2021, 21(22), 7581; https://doi.org/10.3390/s21227581 - 15 Nov 2021

Cited by 2 | Viewed by 2604

Abstract

Advancements in electrode technologies to both stimulate and record the central nervous system’s electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging [...] Read more.

Advancements in electrode technologies to both stimulate and record the central nervous system’s electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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11 pages, 2153 KiB

Open AccessArticle

Wearable Cardioverter–Defibrillator-Measured Step Count for the Surveillance of Physical Fitness during Cardiac Rehabilitation

by Boldizsar Kovacs, Flavia Müller, David Niederseer, Nazmi Krasniqi, Ardan M. Saguner, Firat Duru and Matthias Hermann

Sensors 2021, 21(21), 7054; https://doi.org/10.3390/s21217054 - 25 Oct 2021

Cited by 1 | Viewed by 2521

Abstract

Background: The wearable cardioverter–defibrillator (WCD) has a built-in accelerometer, which allows tracking of patients’ physical activity by remote monitoring. It is unclear whether WCD-measured physical activity, step count, and heart rate correlate with established tools for the assessment of cardiopulmonary fitness such as [...] Read more.

Background: The wearable cardioverter–defibrillator (WCD) has a built-in accelerometer, which allows tracking of patients’ physical activity by remote monitoring. It is unclear whether WCD-measured physical activity, step count, and heart rate correlate with established tools for the assessment of cardiopulmonary fitness such as the 6-min walk test (6MWT). Objective: To correlate measurements of patient physical activity through the WCD with a supervised 6MWT during in-patient cardiac rehabilitation (CR) and to allow their use as surrogate parameters of cardiopulmonary fitness in an out-patient setting. Methods: Consecutive patients with a history of WCD use treated at our center and an in-patient CR following an index hospitalization were included. Baseline characteristics, measurements of WCD accelerometer (median daily step count, median daily activity level), median daily heart rate, and clinically supervised 6MWT at admission and discharge of CR were obtained. Results: Forty-one patients with a mean age of 55.5 (±11.5) years were included. Thirty-five patients (85.4%) were male and 28 patients (68%) had a primary prophylactic WCD-indication. The most common underlying heart diseases were ischemic heart disease (24 patients 58.6%) and dilated cardiomyopathy (13 patients, 31.7%). Median CR duration was 20 (IQR 19.75–26.25) days. 6MWT distance increased from a mean of 329 m (±107) to 470 m (±116) during CR (p < 0.0001). The median daily step count and activity level increased significantly, from 5542 steps (IQR 3718–7055) to 8778 (IQR 6229–12,920, p < 0.0001) and median 117 × 10⁶ (IQR 96 × 10⁶–142 × 10⁶) threshold value exceedance (TVE) to 146 × 10⁶ TVE (IQR 110 × 10⁶–169 × 10⁶, p < 0.0001), respectively. The median heart rate was 74.9 bpm (IQR 65.8–84.5) and 70.2 (IQR 64.1–77.3, p = 0.09) at admission and discharge, respectively. Of all three parameters, median daily step count showed the best correlation to the results of the 6MWT at admission and discharge (r = 0.32, p = 0.04 and 0.37, p = 0.02, respectively). Conclusions: Remote monitoring of median daily step count as assessed by the WCD’s accelerometer showed positive correlation with the 6MWT and could serve as a surrogate for cardiopulmonary exercise capacity. Assessment of daily step count and activity level measured remotely by the WCD could help to tailor optimal exercise instruction for patients not attending CR. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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Median daily step count measured by the WCD at the time of admission and discharge to the CR clinic (Increase from median 5542 (IQR 3718–7055) to median 8778 (IQR 6229–12,920), p < 0.001). Full article ">Figure 2
Median daily activity level measured by the WCD at the time of admission and discharge to the CR clinic (Increased from a median 117 × 106 (IQR 96 × 106–142 × 106) TVE to 146 × 106 (110 × 106–169 × 106), p < 0.001). Full article ">Figure 3
Median daily heart rate measured by the WCD at the time of admission and discharge to the CR clinic (Decreased from a median 74.9 bpm (IQR 65.8–84.5) to 70.2 (IQR 64.1–77.3), p = 0.09). Full article ">Figure 4
Scatterplot of 6MWT and step count at admission and discharge with respective correlation coefficients. The relationship between the 6MWT and the step count at admission (blue) and discharge (orange) of the CR stay are illustrated. The graph visualizes their respective correlation coefficients. Blue dots represent patients at admission, whereas orange dots represent patients at discharge. The lines depict the correlation coefficient between the 6MWT and the step count at admission in blue and at discharge in orange. The rectangles represent the mean confidence intervals of the respective means (r = 0.32, p = 0.04 and 0.37, p = 0.02, respectively). Full article ">Figure 5
Scatterplot of 6MWT and activity level at admission and discharge with respective correlation coefficients. The relationship between the 6MWT and the activity level at admission (blue) and discharge (orange) of the CR stay are illustrated. The graph visualizes their respective correlation coefficients. Blue dots represent patients at admission, whereas orange dots represent patients at discharge. The lines depict the correlation coefficient between the 6MWT and the activity level at admission in blue and at discharge in orange. The rectangles represent the mean confidence intervals of the respective means (r = 0.02, p = 0.90 and r = 0.15, p = 0.35, respectively). Full article ">Figure 6
Scatterplot of 6MWT and heart rate at admission and discharge with respective correlation coefficients. The relationship between the 6MWT and the heart rate at admission (blue) and discharge (orange) of the CR stay are illustrated. The graph visualizes their respective correlation coefficients. Blue dots represent patients at admission, whereas orange dots represent patients at discharge. The lines depict the correlation coefficient between the 6MWT and the heart rate at admission in blue and at discharge in orange. The rectangles represent the mean confidence intervals of the respective means (r = −0.33, p = 0.03 and r = −0.13, p = 0.41, respectively). Full article ">

11 pages, 1125 KiB

Open AccessCommunication

Validity of Peripheral Oxygen Saturation Measurements with the Garmin Fēnix^® 5X Plus Wearable Device at 4559 m

by Lisa M. Schiefer, Gunnar Treff, Franziska Treff, Peter Schmidt, Larissa Schäfer, Josef Niebauer, Kai E. Swenson, Erik R. Swenson, Marc M. Berger and Mahdi Sareban

Sensors 2021, 21(19), 6363; https://doi.org/10.3390/s21196363 - 23 Sep 2021

Cited by 12 | Viewed by 4296

Abstract

Decreased oxygen saturation (SO₂) at high altitude is associated with potentially life-threatening diseases, e.g., high-altitude pulmonary edema. Wearable devices that allow continuous monitoring of peripheral oxygen saturation (SpO₂), such as the Garmin Fēnix^® 5X Plus (GAR), might provide [...] Read more.

Decreased oxygen saturation (SO₂) at high altitude is associated with potentially life-threatening diseases, e.g., high-altitude pulmonary edema. Wearable devices that allow continuous monitoring of peripheral oxygen saturation (SpO₂), such as the Garmin Fēnix^® 5X Plus (GAR), might provide early detection to prevent hypoxia-induced diseases. We therefore aimed to validate GAR-derived SpO₂ readings at 4559 m. SpO₂ was measured with GAR and the medically certified Covidien Nellcor SpO₂ monitor (COV) at six time points in 13 healthy lowlanders after a rapid ascent from 1130 m to 4559 m. Arterial blood gas (ABG) analysis served as the criterion measure and was conducted at four of the six time points with the Radiometer ABL 90 Flex. Validity was assessed by intraclass correlation coefficients (ICCs), mean absolute percentage error (MAPE), and Bland–Altman plots. Mean (±SD) SO₂, including all time points at 4559 m, was 85.2 ± 6.2% with GAR, 81.0 ± 9.4% with COV, and 75.0 ± 9.5% with ABG. Validity of GAR was low, as indicated by the ICC (0.549), the MAPE (9.77%), the mean SO₂ difference (7.0%), and the wide limits of agreement (−6.5; 20.5%) vs. ABG. Validity of COV was good, as indicated by the ICC (0.883), the MAPE (6.15%), and the mean SO₂ difference (0.1%) vs. ABG. The GAR device demonstrated poor validity and cannot be recommended for monitoring SpO₂ at high altitude. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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19 pages, 2827 KiB

Open AccessArticle

An Integrated Individual Environmental Exposure Assessment System for Real-Time Mobile Sensing in Environmental Health Studies

by Jue Wang, Lirong Kou, Mei-Po Kwan, Rebecca Marie Shakespeare, Kangjae Lee and Yoo Min Park

Sensors 2021, 21(12), 4039; https://doi.org/10.3390/s21124039 - 11 Jun 2021

Cited by 25 | Viewed by 5534

Abstract

The effects of environmental exposure on human health have been widely explored by scholars in health geography for decades. However, recent advances in geospatial technologies, especially the development of mobile approaches to collecting real-time and high-resolution individual data, have enabled sophisticated methods for [...] Read more.

The effects of environmental exposure on human health have been widely explored by scholars in health geography for decades. However, recent advances in geospatial technologies, especially the development of mobile approaches to collecting real-time and high-resolution individual data, have enabled sophisticated methods for assessing people’s environmental exposure. This study proposes an individual environmental exposure assessment system (IEEAS) that integrates objective real-time monitoring devices and subjective sensing tools to provide a composite way for individual-based environmental exposure data collection. With field test data collected in Chicago and Beijing, we illustrate and discuss the advantages of the proposed IEEAS and the composite analysis that could be applied. Data collected with the proposed IEEAS yield relatively accurate measurements of individual exposure in a composite way, and offer new opportunities for developing more sophisticated ways to measure individual environmental exposure. With the capability to consider both the variations in environmental risks and human mobility in high spatial and temporal resolutions, the IEEAS also helps mitigate some uncertainties in environmental exposure assessment and thus enables a better understanding of the relationship between individual environmental exposure and health outcomes. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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18 pages, 2030 KiB

Open AccessArticle

Evaluation of a Low-Cost Commercial Actigraph and Its Potential Use in Detecting Cultural Variations in Physical Activity and Sleep

by Pavlos Topalidis, Cristina Florea, Esther-Sevil Eigl, Anton Kurapov, Carlos Alberto Beltran Leon and Manuel Schabus

Sensors 2021, 21(11), 3774; https://doi.org/10.3390/s21113774 - 29 May 2021

Cited by 21 | Viewed by 5342

Abstract

The purpose of the present study was to evaluate the performance of a low-cost commercial smartwatch, the Xiaomi Mi Band (MB), in extracting physical activity and sleep-related measures and show its potential use in addressing questions that require large-scale real-time data and/or intercultural [...] Read more.

The purpose of the present study was to evaluate the performance of a low-cost commercial smartwatch, the Xiaomi Mi Band (MB), in extracting physical activity and sleep-related measures and show its potential use in addressing questions that require large-scale real-time data and/or intercultural data including low-income countries. We evaluated physical activity and sleep-related measures and discussed the potential application of such devices for large-scale step and sleep data acquisition. To that end, we conducted two separate studies. In Study 1, we evaluated the performance of MB by comparing it to the GT3X (ActiGraph, wGT3X-BT), a scientific actigraph used in research, as well as subjective sleep reports. In Study 2, we distributed the MB across four countries (Austria, Germany, Cuba, and Ukraine) and investigated physical activity and sleep among these countries. The results of Study 1 indicated that MB step counts correlated highly with the scientific GT3X device, but did display biases. In addition, the MB-derived wake-up and total-sleep-times showed high agreement with subjective reports, but partly deviated from GT3X predictions. Study 2 revealed similar MB step counts across countries, but significant later wake-up and bedtimes for Ukraine than the other countries. We hope that our studies will stimulate future large-scale sensor-based physical activity and sleep research studies, including various cultures. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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Correlation and agreement analysis between the GT3X and MB on step counts. (A) Spearman’s correlation between the GT3X and MB step counts for each day and participant. (B) The Bland–Altman plot shows the difference between per-day step count as extracted by the GT3X and MB (y axis) and their average (x axis). Note the high agreement between the two devices (A), although there is a clear bias of the MB device to underestimate steps especially in cases with fewer average steps per day (B). The dashed blue line illustrates the mean difference (i.e., bias) between the two measurements; the dotted lines represent the 95% CI limits of the mean difference; and the black solid line represents the point of equality (where the difference between the two devices is equal to 0). Full article ">Figure 2
Agreement between the objective and subjective sleep measures: MB vs. subjective (A), GT3X vs.subjective (B), and the two objective methods MB and GT3X (C) as visualized by Bland–Altman plots for wake-up-times, bedtimes, and total-sleep-time. Note the high bias and increased deviations between the GT3X and both subjective and MB total-sleep-times. The dashed lines represent the mean difference (i.e., bias) between the two measurements. The dotted blue lines mark the 95% CI limits of the mean difference, and the black solid line represents the point of equality (where the difference between the two devices is equal to 0). Full article ">Figure 3
Subjective physical activity and objective step count differences across countries. (A) Mean step count for each country (Austria and Germany, Cuba, and Ukraine) and day (work or free), as extracted by MB. Note that it was indicative that in Austria there were more steps during free compared to workdays. (B) Mean subjective physical activity (1 = not at all, 4 = a lot) for each country and day. (C) Mean MB step count for in and out of lockdown participants, separately for work and free days. Note that there was no significant effect between in and out of lockdown. Error bars indicate the standard deviation of the mean. Bonferroni-corrected pairwise marginal mean comparisons indicate significance at <0.1+, <0.05 *, <0.01 **, <0.001 ***, and non-significant (NS) levels. Full article ">Figure 4
Objective sleep measures across countries. Average wake-up-times (A), bedtimes (B), and total-sleep-times (C) for each country (Austria and Germany, Cuba, and Ukraine) and day (work or free), as extracted by MB. Note that Ukraine had later wake-up and bedtimes than both Austria and Cuba. Austria had later wake-up-times and higher total-sleep-times than Cuba. Error bars indicate the standard deviation of the mean. Bonferroni-corrected pairwise marginal mean comparisons indicate significance at <0.1 +, <0.05 *, <0.01 **, <0.001 ***, and non-significant (NS) levels. Full article ">Figure A1
Exemplary step counts as extracted by MB and the GT3X for two participants (A,B). Note that on many occasions, the GT3X (right) counted steps when MB (left) did not. Note that the GT3X marked some periods as active, whereas for the same periods, MB indicated 0 activity. Full article ">Figure A1 Cont.
Exemplary step counts as extracted by MB and the GT3X for two participants (A,B). Note that on many occasions, the GT3X (right) counted steps when MB (left) did not. Note that the GT3X marked some periods as active, whereas for the same periods, MB indicated 0 activity. Full article ">Figure A2
Impact of actigraphy wrist position (further vs. closer to the wrist) on the MB (A) and GT3X (B) sleep measures (top: wake-up-times, middle: bedtimes, bottom: total-sleep-times). The limits of the boxplots represent the 95% CI, while the horizontal middle line represents the median. Full article ">Figure A2 Cont.
Impact of actigraphy wrist position (further vs. closer to the wrist) on the MB (A) and GT3X (B) sleep measures (top: wake-up-times, middle: bedtimes, bottom: total-sleep-times). The limits of the boxplots represent the 95% CI, while the horizontal middle line represents the median. Full article ">

21 pages, 1586 KiB

Open AccessArticle

The Cardiovascular Response to Interval Exercise Is Modified by the Contraction Type and Training in Proportion to Metabolic Stress of Recruited Muscle Groups

by Benedikt Gasser, Daniel Fitze, Martino Franchi, Annika Frei, David Niederseer, Christian M. Schmied, Silvio Catuogno, Walter Frey and Martin Flück

Sensors 2021, 21(1), 173; https://doi.org/10.3390/s21010173 - 29 Dec 2020

Cited by 3 | Viewed by 3323

Abstract

Background: Conventional forms of endurance training based on shortening contractions improve aerobic capacity but elicit a detriment of muscle strength. We hypothesized that eccentric interval training, loading muscle during the lengthening phase of contraction, overcome this interference and potentially adverse cardiovascular reactions, enhancing [...] Read more.

Background: Conventional forms of endurance training based on shortening contractions improve aerobic capacity but elicit a detriment of muscle strength. We hypothesized that eccentric interval training, loading muscle during the lengthening phase of contraction, overcome this interference and potentially adverse cardiovascular reactions, enhancing both muscle metabolism and strength, in association with the stress experienced during exercise. Methods: Twelve healthy participants completed an eight-week program of work-matched progressive interval-type pedaling exercise on a soft robot under predominately concentric or eccentric load. Results: Eccentric interval training specifically enhanced the peak power of positive anaerobic contractions (+28%), mitigated the strain on muscle’s aerobic metabolism, and lowered hemodynamic stress during interval exercise, concomitant with a lowered contribution of positive work to the target output. Concentric training alone lowered blood glucose concentration during interval exercise and mitigated heart rate and blood lactate concentration during ramp exercise. Training-induced adjustments for lactate and positive peak power were independently correlated (p < 0.05, |r| > 0.7) with indices of metabolic and mechanical muscle stress during exercise. Discussion: Task-specific improvements in strength and muscle’s metabolic capacity were induced with eccentric interval exercise lowering cardiovascular risk factors, except for blood glucose concentration, possibly through altered neuromuscular coordination. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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Power output and muscle oxygen saturation during workload-matched concentric and eccentric type of interval exercise. (A,B) Example of the power output being produced by one subject during one interval of concentric (A) and eccentric (B) interval exercise, respectively, before training. (C–F) Positive and negative work being performed by the left leg of one subject during all intervals of a session of concentric (C) and eccentric (D) interval exercise and the resulting effects on muscle oxygen saturation and hemoglobin concentration in m. vastus lateralis (E,F) for one leg before training. Full article ">Figure 2
Temporal response of perceived exertion and heart rate during concentric and eccentric type of interval exercise. Line graph with whiskers indicating mean values ± SE for perceived exertion (A,B) and heart rate (C,D) as measured each 2 min during the interval-type pedaling exercise before and after the eight weeks of training of the two groups under the concentric (A,C) or eccentric (B,D) contraction protocol. The rest and pedaling phase of the respective concentric and eccentric interval exercise is exemplarily indicated in panels A and B. *, p < 0.05 vs. 0 min. ‡, p < 0.05 vs. concentric. Repeated-measures ANOVA with a post-hoc test of least significant difference. Full article ">Figure 3
Temporal response of systolic and diastolic blood pressure during concentric and eccentric type of interval exercise. Line graph with whiskers indicating mean values ± SE for diastolic blood pressure (A,B) and systolic blood pressure (C,D) as measured each 2 min during the interval-type pedaling exercise before and after the eight weeks of training under the concentric (A,C) or eccentric (B,D) contraction group. *, p < 0.05 vs. 0 min. ‡, p < 0.05 vs. concentric. $ p < 0.05 vs. pre. Repeated-measures ANOVA with a post-hoc test of least significant difference. Full article ">Figure 4
Temporal response of blood glucose and lactate concentration during concentric and eccentric type of interval exercise. Line graph with whiskers indicating mean values ± SE for blood glucose concentration (A,B) and blood lactate concentration (C,D) as measured each 2 min in the two groups during the interval-type pedaling exercise before and after the eight weeks of training under the concentric (A,C) or eccentric (B,D) contraction protocol. *, p < 0.05 vs. 0 min. ‡, p < 0.05 vs. concentric. $, vs. pre. Repeated-measures ANOVA with post-hoc test of least significant difference. Full article ">Figure 5
Connectivity of correlations between stress during the stimulus of interval exercise and the adjustments with training. Network display of the 131 linear relationships between indices of metabolic and mechanical stress during interval exercise and training-induced adjustments (nodes) for Pearson correlations, which passed a threshold of |r| > 0.70 and p < 0.05. Straight red and stippled blue lines, respectively, indicate positive and negative correlations. The strength of the correlation is indicated by the thickness of the lines connecting two nodes. Red and orange colored nodes highlight stress during the first and last session of interval exercise, respectively. Green colored nodes emphasize training-induced fold changes. Note the high connectivity with parameters demonstrating an interaction effect of interval training × the contraction group with the reddish-indicated indices of stress during interval exercise that define (highlighted) entities. This comprises selective correlations of the green-highlighted fold changes in positive peak power and the corresponding rate of force development (black arrows), the fold changes for the AUC of the blood lactate concentration during the ramp test (red arrow) or during interval exercise (green arrow), with the AUC of the perceived exertion, heart rate, the systolic blood pressure, the hemoglobin accruing in m. gastrocnemius, and the oxygen deficit in m. vastus lateralis, and the average power during interval exercise. For the ramp exercise, only the AUCs over the same duration of exercise before training were considered. Abbreviation code: _A, AUC during exercise; BPdia, diastolic blood pressure; BPsys, systolic blood pressure; bm, body mass; DO2, oxygen deficit; DO2_ave, average oxygen deficit; fold, post vs. pre ratio; gas, m. gastrocnemius; glucose, blood glucose concentration; HR, heart rate; _I, during interval exercise; L, _S, number of intervals (sets); left leg; lactate, blood lactate concentration; nPP, negative peak power; nW, negative work; P_ave, average power; post, after training; PPO, peak power output during the ramp test; pPP, positive peak power; pre, prior to training; pW, positive work; rPP, reactive peak power; R, right leg; _R, during ramp test; pRFD, rate of force development during the development of positive peak power; RPE, rate of perceived exertion; sP, target power per PPO; _t, exercise duration; tHb, concentration of total hemoglobin; vas, m. vastus lateralis; tHb_ave, average concentration of total hemoglobin; VO2peak, peak oxygen uptake. Full article ">

12 pages, 1489 KiB

Open AccessArticle

Minimally Invasive Electrochemical Patch-Based Sensor System for Monitoring Glucose and Lactate in the Human Body—A Survey-Based Analysis of the End-User’s Perspective

by Roman Holzer, Wilhelm Bloch and Christian Brinkmann

Sensors 2020, 20(20), 5761; https://doi.org/10.3390/s20205761 - 11 Oct 2020

Cited by 15 | Viewed by 3848

Abstract

Background: Wearable electrochemical sensors that detect human biomarkers allow a comprehensive analysis of a person’s health condition. The “electronic smart patch system for wireless monitoring of molecular biomarkers for health care and well-being” (ELSAH) project aims to develop a minimally invasive sensor system [...] Read more.

Background: Wearable electrochemical sensors that detect human biomarkers allow a comprehensive analysis of a person’s health condition. The “electronic smart patch system for wireless monitoring of molecular biomarkers for health care and well-being” (ELSAH) project aims to develop a minimally invasive sensor system that is capable of continuously monitoring glucose and lactate in the dermal interstitial fluid in real time. It is the objective of the present study to compare the intended ELSAH-patch specifications with the expectations and requirements of potential end-users at an early stage during the development phase. Methods: A questionnaire addressing different aspects of the ELSAH-patch was filled out by 383 respondents. Results: The participants stated a high general demand for such a system, and they would use the ELSAH-patch in different health care and physical fitness applications. The preferred terminal device for communication with the sensor would be the smartphone. An operating time of 24 hours would be sufficient for 55.8% of the users (95%-CI: 50.3–61.3%), while 43.5% of them (95%-CI: 38.0–48.9%) would prefer a lifetime of several days or more. The software should have a warning function, especially for critical health conditions. Since the measured personal data would be highly sensitive, the participants called for high standards for data security and privacy. Conclusion: In general, the participants’ responses on their expectations and requirements were well in line with the intended specifications of the ELSAH-patch system. However, certain technical aspects such as the lifetime, data security and accuracy require special attention during its development. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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Review

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18 pages, 3675 KiB

Open AccessReview

Multiparameter Monitoring with a Wearable Cardioverter Defibrillator

by Ursula Rohrer, Martin Manninger, Andreas Zirlik and Daniel Scherr

Sensors 2022, 22(1), 22; https://doi.org/10.3390/s22010022 - 21 Dec 2021

Cited by 6 | Viewed by 5406

Abstract

A wearable cardioverter-defibrillator (WCD) is a temporary treatment option for patients at high risk for sudden cardiac death (SCD) and for patients who are temporarily not candidates for an implantable cardioverter defibrillator (ICD). In addition, the need for telemedical concepts in the detection [...] Read more.

A wearable cardioverter-defibrillator (WCD) is a temporary treatment option for patients at high risk for sudden cardiac death (SCD) and for patients who are temporarily not candidates for an implantable cardioverter defibrillator (ICD). In addition, the need for telemedical concepts in the detection and treatment of heart failure (HF) and its arrhythmias is growing. The WCD has evolved from a shock device detecting malignant ventricular arrhythmias (VA) and treating them with shocks to a heart-failure-monitoring device that captures physical activity and cardioacoustic biomarkers as surrogate parameters for HF to help the treating physician surveil and guide the HF therapy of each individual patient. In addition to its important role in preventing SCD, the WCD could become an important tool in heart failure treatment by helping prevent HF events by detecting imminent decompensation via remote monitoring and monitoring therapy success. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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21 pages, 667 KiB

Open AccessReview

Assessing the Tidal Volume through Wearables: A Scoping Review

by Vito Monaco and Cesare Stefanini

Sensors 2021, 21(12), 4124; https://doi.org/10.3390/s21124124 - 16 Jun 2021

Cited by 19 | Viewed by 3568

Abstract

The assessment of respiratory activity based on wearable devices is becoming an area of growing interest due to the wide range of available sensors. Accordingly, this scoping review aims to identify research evidence supporting the use of wearable devices to monitor the tidal [...] Read more.

The assessment of respiratory activity based on wearable devices is becoming an area of growing interest due to the wide range of available sensors. Accordingly, this scoping review aims to identify research evidence supporting the use of wearable devices to monitor the tidal volume during both daily activities and clinical settings. A screening of the literature (Pubmed, Scopus, and Web of Science) was carried out in December 2020 to collect studies: i. comparing one or more methodological approaches for the assessment of tidal volume with the outcome of a state-of-the-art measurement device (i.e., spirometry or optoelectronic plethysmography); ii. dealing with technological solutions designed to be exploited in wearable devices. From the initial 1031 documents, only 36 citations met the eligibility criteria. These studies highlighted that the tidal volume can be estimated by using different technologies ranging from IMUs to strain sensors (e.g., resistive, capacitive, inductive, electromagnetic, and optical) or acoustic sensors. Noticeably, the relative volumetric error of these solutions during quasi-static tasks (e.g., resting and sitting) is typically ≥10% but it deteriorates during dynamic motor tasks (e.g., walking). As such, additional efforts are required to improve the performance of these devices and to identify possible applications based on their accuracy and reliability. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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20 pages, 1905 KiB

Open AccessReview

The Use of Pulse Oximetry in the Assessment of Acclimatization to High Altitude

by Tobias Dünnwald, Roland Kienast, David Niederseer and Martin Burtscher

Sensors 2021, 21(4), 1263; https://doi.org/10.3390/s21041263 - 10 Feb 2021

Cited by 39 | Viewed by 9635

Abstract

Background: Finger pulse oximeters are widely used to monitor physiological responses to high-altitude exposure, the progress of acclimatization, and/or the potential development of high-altitude related diseases. Although there is increasing evidence for its invaluable support at high altitude, some controversy remains, largely [...] Read more.

Background: Finger pulse oximeters are widely used to monitor physiological responses to high-altitude exposure, the progress of acclimatization, and/or the potential development of high-altitude related diseases. Although there is increasing evidence for its invaluable support at high altitude, some controversy remains, largely due to differences in individual preconditions, evaluation purposes, measurement methods, the use of different devices, and the lacking ability to interpret data correctly. Therefore, this review is aimed at providing information on the functioning of pulse oximeters, appropriate measurement methods and published time courses of pulse oximetry data (peripheral oxygen saturation, (SpO₂) and heart rate (HR), recorded at rest and submaximal exercise during exposure to various altitudes. Results: The presented findings from the literature review confirm rather large variations of pulse oximetry measures (SpO₂ and HR) during acute exposure and acclimatization to high altitude, related to the varying conditions between studies mentioned above. It turned out that particularly SpO₂ levels decrease with acute altitude/hypoxia exposure and partly recover during acclimatization, with an opposite trend of HR. Moreover, the development of acute mountain sickness (AMS) was consistently associated with lower SpO₂ values compared to individuals free from AMS. Conclusions: The use of finger pulse oximetry at high altitude is considered as a valuable tool in the evaluation of individual acclimatization to high altitude but also to monitor AMS progression and treatment efficacy. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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Figure 1
Flow chart of the study selection process. Full article ">Figure 2
Light absorption spectrum of deoxyhemoglobin (HHb) and oxyhemoglobin (O2Hb). Different absorption for HHb and O2Hb at red light (660 nm) compared to infrared light (940 nm) is visible. (This Figure is based on data from Prahl, 1998 [<a href="#B20-sensors-21-01263" class="html-bibr">20</a>]). Full article ">Figure 3
Example for changes of peripheral oxygen saturation (SpO2) when acutely ascending from low (LA) to high altitude (HA) and during the subsequent 11- or 12-day acclimatization period based on 2 studies performed at different altitudes (3810 m and 4300 m) [<a href="#B65-sensors-21-01263" class="html-bibr">65</a>,<a href="#B68-sensors-21-01263" class="html-bibr">68</a>]. At 3800 m, resting SpO2 was measured in a semi-supine position, with head and trunk elevated ~30°, by finger pulse oximetry (Criticare, 504-US pulse oxymeter). At 4300 m, resting SpO2 was measured in a sitting (upright) position for a 4-min period after relaxing for 20 min, by ear oximetry (Hewlett-Packard 47201A ear oximeter, Palo Alto, CA, USA). Full article ">Figure 4
Example for changes of heart rate (HR) when acutely ascending from low (LA) to high altitude (HA1) and during the subsequent day acclimatization period at 3600 m based on a study with young soccer players (16 ± 0.4 years) [<a href="#B51-sensors-21-01263" class="html-bibr">51</a>]. HR data were collected in the morning after awakening with a Polar Team system (Polar Electro Oy, Kempele, Finland). Full article ">Figure 5
Example for changes of peripheral oxygen saturation (SpO2) during submaximal exercise when acutely ascending from low (LA) to high altitude (HA) and during the subsequent 22-day acclimatization period based on a study performed at 4300 m [<a href="#B68-sensors-21-01263" class="html-bibr">68</a>]. Resting SpO2 was measured in a sitting (upright) position for a 4-min period after relaxing for 20 min, by ear oximetry (Hewlett-Packard 47201A ear oximeter, Palo Alto, CA, USA). Full article ">

17 pages, 1023 KiB

Open AccessReview

Mobile Technologies to Promote Physical Activity during Cardiac Rehabilitation: A Scoping Review

by Florian Meinhart, Thomas Stütz, Mahdi Sareban, Stefan Tino Kulnik and Josef Niebauer

Sensors 2021, 21(1), 65; https://doi.org/10.3390/s21010065 - 24 Dec 2020

Cited by 28 | Viewed by 6045

Abstract

Promoting regular physical activity (PA) and improving exercise capacity are the primary goals of cardiac rehabilitation (CR). Mobile technologies (mTechs) like smartphones, smartwatches, and fitness trackers might help patients in reaching these goals. This review aimed to scope current scientific literature on mTechs [...] Read more.

Promoting regular physical activity (PA) and improving exercise capacity are the primary goals of cardiac rehabilitation (CR). Mobile technologies (mTechs) like smartphones, smartwatches, and fitness trackers might help patients in reaching these goals. This review aimed to scope current scientific literature on mTechs in CR to assess the impact on patients’ exercise capacity and to identify gaps and future directions for research. PubMed, CENTRAL, and CDSR were systematically searched for randomized controlled trials (RCTs). These RCTs had to utilize mTechs to objectively monitor and promote PA of patients during or following CR, aim at improvements in exercise capacity, and be published between December 2014 and December 2019. A total of 964 publications were identified, and 13 studies met all inclusion criteria. Home-based CR with mTechs vs. outpatient CR without mTechs and outpatient CR with mTechs vs. outpatient CR without mTechs did not lead to statistically significant differences in exercise capacity. In contrast, outpatient CR followed by home-based CR with mTechs led to significant improvement in exercise capacity as compared to outpatient CR without further formal CR. Supplying patients with mTechs may improve exercise capacity. To ensure that usage of and compliance with mTechs is optimal, a concentrated effort of CR staff has to be achieved. The COVID-19 pandemic has led to an unprecedented lack of patient support while away from institutional CR. Even though mTechs lend themselves as suitable assistants, evidence is lacking that they can fill this gap. Full article

(This article belongs to the Special Issue Wearable Sensors for Health and Physiological Monitoring)

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