Sensors

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14 pages, 1667 KiB

Open AccessArticle

Performance of the FreeStyle Libre Flash Glucose Monitoring System during an Oral Glucose Tolerance Test and Exercise in Healthy Adolescents

by Sahar Afeef, Keith Tolfrey, Julia K. Zakrzewski-Fruer and Laura A. Barrett

Sensors 2023, 23(9), 4249; https://doi.org/10.3390/s23094249 - 25 Apr 2023

Cited by 3 | Viewed by 3143

Abstract

This study’s aim was to assess FreeStyle Libre Flash glucose monitoring (FGM) performance during an oral glucose tolerance test (OGTT) and treadmill exercise in healthy adolescents. This should advance the feasibility and utility of user-friendly technologies for metabolic assessments in adolescents. Seventeen healthy [...] Read more.

This study’s aim was to assess FreeStyle Libre Flash glucose monitoring (FGM) performance during an oral glucose tolerance test (OGTT) and treadmill exercise in healthy adolescents. This should advance the feasibility and utility of user-friendly technologies for metabolic assessments in adolescents. Seventeen healthy adolescents (nine girls aged 12.8 ± 0.9 years) performed an OGTT and submaximal and maximal treadmill exercise tests in a laboratory setting. The scanned interstitial fluid glucose concentration ([ISFG]) obtained by FGM was compared against finger-prick capillary plasma glucose concentration ([CPG]) at 0 (pre-OGTT), −15, −30, −60, −120 min post-OGTT, pre-, mid-, post- submaximal exercise, and pre- and post- maximal exercise. Overall mean absolute relative difference (MARD) was 13.1 ± 8.5%, and 68% (n = 113) of the paired glucose data met the ISO 15197:2013 criteria. For clinical accuracy, 84% and 16% of FGM readings were within zones A and B in the Consensus Error Grid (CEG), respectively, which met the ISO 15197:2013 criteria of having at least 99% of results within these zones. Scanned [ISFG] were statistically lower than [CPG] at 15 (−1.16 mmol∙L⁻¹, p < 0.001) and 30 min (−0.74 mmol∙L⁻¹, p = 0.041) post-OGTT. Yet, post-OGTT glycaemic responses assessed by total and incremental areas under the curve (AUCs) were not significantly different, with trivial to small effect sizes (p ≥ 0.084, d = 0.14–0.45). Further, [ISFGs] were not different from [CPGs] during submaximal and maximal exercise tests (interaction p ≥ 0.614). FGM can be a feasible alternative to reflect postprandial glycaemia (AUCs) in healthy adolescents who may not endure repeated finger pricks. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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19 pages, 5486 KiB

Open AccessArticle

Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals

by Jordan Ortega-Rodríguez, José Francisco Gómez-González and Ernesto Pereda

Sensors 2023, 23(9), 4239; https://doi.org/10.3390/s23094239 - 24 Apr 2023

Cited by 4 | Viewed by 2462

Abstract

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security [...] Read more.

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain–machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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14 pages, 2241 KiB

Open AccessArticle

Laboratory-Based Examination of the Reliability and Validity of Kinematic Measures of Wrist and Finger Function Collected by a Telerehabilitation System in Persons with Chronic Stroke

by Ashley MontJohnson, Amanda Cronce, Qinyin Qiu, Jigna Patel, Mee Eriksson, Alma Merians, Sergei Adamovich and Gerard Fluet

Sensors 2023, 23(5), 2656; https://doi.org/10.3390/s23052656 - 28 Feb 2023

Cited by 3 | Viewed by 2346

Abstract

We have developed the New Jersey Institute of Technology—Home Virtual Rehabilitation System (NJIT—HoVRS) to facilitate intensive, hand-focused rehabilitation in the home. We developed testing simulations with the goal of providing richer information for clinicians performing remote assessments. This paper presents the results of [...] Read more.

We have developed the New Jersey Institute of Technology—Home Virtual Rehabilitation System (NJIT—HoVRS) to facilitate intensive, hand-focused rehabilitation in the home. We developed testing simulations with the goal of providing richer information for clinicians performing remote assessments. This paper presents the results of reliability testing examining differences between in-person and remote testing as well as discriminatory and convergent validity testing of a battery of six kinematic measures collected with NJIT—HoVRS. Two different groups of persons with upper extremity impairments due to chronic stroke participated in two separate experiments. Data Collection: All data collection sessions included six kinematic tests collected with the Leap Motion Controller. Measurements collected include hand opening range, wrist extension range, pronation-supination range, hand opening accuracy, wrist extension accuracy, and pronation-supination accuracy. The system usability was evaluated by therapists performing the reliability study using the System Usability Scale. When comparing the in-laboratory collection and the first remote collection, the intra-class correlation coefficients (ICC) for three of the six measurements were above 0.900 and the other three were between 0.500 and 0.900. Two of the first remote collection/second remote collection ICCs were above 0.900, and the other four were between 0.600 and 0.900. The 95% confidence intervals for these ICC were broad, suggesting that these preliminary analyses need to be confirmed by studies with larger samples. The therapist’s SUS scores ranged from 70 to 90. The mean was 83.1 (SD = 6.4), which is consistent with industry adoption. There were statistically significant differences in the kinematic scores when comparing unimpaired and impaired UE for all six measures. Five of six impaired hand kinematic scores and five of six impaired/unimpaired hand difference scores demonstrated correlations between 0.400 and 0.700 with UEFMA scores. Reliability for all measures was acceptable for clinical practice. Discriminant and convergent validity testing suggest that scores on these tests may be meaningful and valid. Further testing in a remote setting is necessary to validate this process. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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23 pages, 6803 KiB

Open AccessArticle

Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson’s First Skewness Coefficient Extracted from EEG Data

by Joshua J. J. Davis, Robert Kozma and Florian Schübeler

Sensors 2023, 23(3), 1293; https://doi.org/10.3390/s23031293 - 23 Jan 2023

Cited by 3 | Viewed by 2447

Abstract

It has been proposed that meditative states show different brain dynamics than other more engaged states. It is known that when people sit with closed eyes instead of open eyes, they have different brain dynamics, which may be associated with a combination of [...] Read more.

It has been proposed that meditative states show different brain dynamics than other more engaged states. It is known that when people sit with closed eyes instead of open eyes, they have different brain dynamics, which may be associated with a combination of deprived sensory input and more relaxed inner psychophysiological and cognitive states. Here, we study such states based on a previously established experimental methodology, with the aid of an electro-encephalography (EEG) array with 128 electrodes. We derived the Shannon Entropy (H) and Pearson’s 1st Skewness Coefficient (PSk) from the power spectrum for the modalities of meditation and video watching, including 20 participants, 11 meditators and 9 non-meditators. The discriminating performance of the indices H and PSk was evaluated using Student’s t-test. The results demonstrate a statistically significant difference between the mean H and PSk values during meditation and video watch modes. We show that the H index is useful to discriminate between Meditator and Non-Meditator participants during meditation over both the prefrontal and occipital areas, while the PSk index is useful to discriminate Meditators from Non-Meditators based on the prefrontal areas for both meditation and video modes. Moreover, we observe episodes of anti-correlation between the prefrontal and occipital areas during meditation, while there is no evidence for such anticorrelation periods during video watching. We outline directions of future studies incorporating further statistical indices for the characterization of brain states. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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12 pages, 2070 KiB

Open AccessArticle

Evaluation of a Smart Knee Brace for Range of Motion and Velocity Monitoring during Rehabilitation Exercises and an Exergame

by Michelle Riffitts, Harold Cook, Michael McClincy and Kevin Bell

Sensors 2022, 22(24), 9965; https://doi.org/10.3390/s22249965 - 17 Dec 2022

Cited by 2 | Viewed by 2867

Abstract

Anterior cruciate ligament (ACL) injuries often require a lengthy duration of rehabilitation for patients to return to their prior level of function. Adherence to rehabilitation during this prolonged period can be subpar due to the treatment duration and poor adherence to home exercises. [...] Read more.

Anterior cruciate ligament (ACL) injuries often require a lengthy duration of rehabilitation for patients to return to their prior level of function. Adherence to rehabilitation during this prolonged period can be subpar due to the treatment duration and poor adherence to home exercises. This work evaluates whether a smart instrumented knee brace system is capable of monitoring knee range of motion and velocity during a series of common knee rehabilitation exercises and an exergame. A total of 15 healthy participants completed a series of common knee rehabilitation exercises and played an exergame while wearing a smart instrumented knee brace. The range of motion (ROM) and velocity of the knee recorded by the knee brace was compared to a reference optoelectronic system. The results show good agreement between the knee brace system and the reference system for all exercises performed. Participants were able to quickly learn how to play the exergame and scored well within the game. The system investigated in this study has the potential to allow rehabilitation to occur outside of the clinic with the use of remote monitoring, and improve adherence and outcomes through the use of an exergame. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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12 pages, 394 KiB

Open AccessArticle

Identifying the Optimal Parameters to Express the Capacity–Activity Interrelationship of Community-Dwelling Older Adults Using Wearable Sensors

by Emily Wright, Victoria Chester and Usha Kuruganti

Sensors 2022, 22(24), 9648; https://doi.org/10.3390/s22249648 - 9 Dec 2022

Cited by 2 | Viewed by 1539

Abstract

Mobility is the primary indicator of quality of life among older adults. Physical capacity (PC) and physical activity (PA) are two determinants of mobility; however, PC and PA are complex constructs represented by numerous parameters. This research sought to identify the optimal parameters [...] Read more.

Mobility is the primary indicator of quality of life among older adults. Physical capacity (PC) and physical activity (PA) are two determinants of mobility; however, PC and PA are complex constructs represented by numerous parameters. This research sought to identify the optimal parameters that may be used to represent PC and PA of older adults, while exploring the interrelationship of these two constructs. Participants were 76 community-dwelling older adults (M age = 74.05 ± 5.15 yrs.). The McRoberts MoveTest was used to objectively measure PC in the laboratory with the following tests: the Short Physical Performance Battery, the Sway test, Sit to Stands, and the Timed Up and Go. PA was then measured at home for one week using the McRoberts USB Dynaport. Correlation analyses resulted in 55% and 65% reductions of PC and PA parameters, respectively. Clustering identified five representative PC parameters and five representative PA parameters. Canonical correlation analysis identified a non-significant correlation between the two sets of parameters. A novel approach was used to define PC and PA by systematically reducing these constructs into representative parameters that provide clinically relevant information, suggesting that they are an accurate representation of one’s PC and PA. A non-significant correlation between PC and PA suggests that there is no relationship between the two in this sample of community-dwelling older adults. The research provided insight into two important determinants of older adult mobility, and how they influence each other. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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13 pages, 9120 KiB

Open AccessArticle

ANN-Based Discernment of Septic and Inflammatory Synovial Fluid: A Novel Method Using Viscosity Data from a QCR Sensor

by Andrés Miranda-Martínez, Berta Sufrate-Vergara, Belén Fernández-Puntero, María José Alcaide-Martin, Antonio Buño-Soto and José Javier Serrano-Olmedo

Sensors 2022, 22(23), 9413; https://doi.org/10.3390/s22239413 - 2 Dec 2022

Viewed by 1628

Abstract

The synovial fluid (SF) analysis involves a series of chemical and physical studies that allow opportune diagnosing of septic, inflammatory, non-inflammatory, and other pathologies in joints. Among the variety of analyses to be performed on the synovial fluid, the study of viscosity can [...] Read more.

The synovial fluid (SF) analysis involves a series of chemical and physical studies that allow opportune diagnosing of septic, inflammatory, non-inflammatory, and other pathologies in joints. Among the variety of analyses to be performed on the synovial fluid, the study of viscosity can help distinguish between these conditions, since this property is affected in pathological cases. The problem with viscosity measurement is that it usually requires a large sample volume, or the necessary instrumentation is bulky and expensive. This study compares the viscosity of normal synovial fluid samples with samples with infectious and inflammatory pathologies and classifies them using an ANN (Artificial Neural Network). For this purpose, a low-cost, portable QCR-based sensor (10 MHz) was used to measure the viscous responses of the samples by obtaining three parameters:

Δ f

,

Δ Γ

(parameters associated with the viscoelastic properties of the fluid), and viscosity calculation. These values were used to train the algorithm. Different versions of the ANN were compared, along with other models, such as SVM and random forest. Thirty-three samples of SF were analyzed. Our study suggests that the viscosity characterized by our sensor can help distinguish infectious synovial fluid, and that implementation of ANN improves the accuracy of synovial fluid classification. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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19 pages, 6321 KiB

Open AccessArticle

A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals

by Hisham ElMoaqet, Mohammad Eid, Mutaz Ryalat and Thomas Penzel

Sensors 2022, 22(22), 8826; https://doi.org/10.3390/s22228826 - 15 Nov 2022

Cited by 15 | Viewed by 3970

Abstract

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep [...] Read more.

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis. This paper presents an end-to-end deep transfer learning framework for automatic feature extraction and sleep stage scoring based on a single-channel EEG. The proposed framework was evaluated over the three primary signals recommended by the American Academy of Sleep Medicine (C4-M1, F4-M1, O2-M1) from two data sets that have different properties and are recorded with different hardware. Different Time–Frequency (TF) imaging approaches were evaluated to generate TF representations for the 30 s EEG sleep epochs, eliminating the need for complex EEG signal pre-processing or manual feature extraction. Several training and detection scenarios were investigated using transfer learning of convolutional neural networks (CNN) and combined with recurrent neural networks. Generating TF images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained GoogLeNet CNN followed by a bidirectional long short-term memory (BiLSTM) network showed the best scoring performance among all tested scenarios. Using 20-fold cross-validation applied on the C4-M1 channel, the proposed framework achieved an average per-class accuracy of 91.2%, sensitivity of 77%, specificity of 94.1%, and precision of 75.9%. Our results demonstrate that without changing the model architecture and the training algorithm, our model could be applied to different single-channel EEGs from different data sets. Most importantly, the proposed system receives a single EEG epoch as an input at a time and produces a single corresponding output label, making it suitable for real time monitoring outside sleep labs as well as to help sleep lab specialists arrive at a more accurate diagnoses. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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13 pages, 1700 KiB

Open AccessArticle

Pleasantness Recognition Induced by Different Odor Concentrations Using Olfactory Electroencephalogram Signals

by Hui-Rang Hou, Rui-Xue Han, Xiao-Nei Zhang and Qing-Hao Meng

Sensors 2022, 22(22), 8808; https://doi.org/10.3390/s22228808 - 15 Nov 2022

Cited by 4 | Viewed by 2384

Abstract

Olfactory-induced emotion plays an important role in communication, decision-making, multimedia, and disorder treatment. Using electroencephalogram (EEG) technology, this paper focuses on (1) exploring the possibility of recognizing pleasantness induced by different concentrations of odors, (2) finding the EEG rhythm wave that is most [...] Read more.

Olfactory-induced emotion plays an important role in communication, decision-making, multimedia, and disorder treatment. Using electroencephalogram (EEG) technology, this paper focuses on (1) exploring the possibility of recognizing pleasantness induced by different concentrations of odors, (2) finding the EEG rhythm wave that is most suitable for the recognition of different odor concentrations, (3) analyzing recognition accuracies with concentration changes, and (4) selecting a suitable classifier for this classification task. To explore these issues, first, emotions induced by five different concentrations of rose or rotten odors are divided into five kinds of pleasantness by averaging subjective evaluation scores. Then, the power spectral density features of EEG signals and support vector machine (SVM) are used for classification tasks. Classification results on the EEG signals collected from 13 participants show that for pleasantness recognition induced by pleasant or disgusting odor concentrations, considerable average classification accuracies of 93.5% or 92.2% are obtained, respectively. The results indicate that (1) using EEG technology, pleasantness recognition induced by different odor concentrations is possible; (2) gamma frequency band outperformed other EEG rhythm-based frequency bands in terms of classification accuracy, and as the maximum frequency of the EEG spectrum increases, the pleasantness classification accuracy gradually increases; (3) for both rose and rotten odors, the highest concentration obtains the best classification accuracy, followed by the lowest concentration. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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12 pages, 4931 KiB

Open AccessArticle

Measuring Suite for Vascular Response Monitoring during Hyperbaric Oxygen Therapy by Means of Pulse Transit Time (PTT) Analysis

by Theresa Wandel, Daniel Pascal Hausherr and Dirk Berben

Sensors 2022, 22(21), 8295; https://doi.org/10.3390/s22218295 - 29 Oct 2022

Cited by 1 | Viewed by 1794

Abstract

The efficacy of hyperbaric oxygen therapy in treating wound healing disorders is well established. The obvious explanation is the presence of elevated oxygen tissue tensions during the high-pressure oxygen exposure. This explanation omits that the effective agent, elevated oxygen tension, is only present [...] Read more.

The efficacy of hyperbaric oxygen therapy in treating wound healing disorders is well established. The obvious explanation is the presence of elevated oxygen tissue tensions during the high-pressure oxygen exposure. This explanation omits that the effective agent, elevated oxygen tension, is only present for 6.25% of the time. To investigate possible prevailing vascular changes caused by HBOT, the presented device monitors the vascular response during therapy by Pulse-Transit-Time analysis. The device allows synchronous 1 kHz ECG and PPG measurements. The data are stored in a 1 GBit flash drive and retrieved post-therapy. Normoxic measurements on the authors with and without nicotine validate the device’s functionality. Measurements during HBO therapy have been successfully performed. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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20 pages, 13893 KiB

Open AccessArticle

User-Centered Design Methodologies for the Prototype Development of a Smart Harness and Related System to Provide Haptic Cues to Persons with Parkinson’s Disease

by Silvia Imbesi, Mattia Corzani, Giovanna Lopane, Giuseppe Mincolelli and Lorenzo Chiari

Sensors 2022, 22(21), 8095; https://doi.org/10.3390/s22218095 - 22 Oct 2022

Cited by 7 | Viewed by 2612

Abstract

This paper describes the second part of the PASSO (Parkinson smart sensory cues for older users) project, which designs and tests an innovative haptic biofeedback system based on a wireless body sensor network using a smartphone and different smartwatches specifically designed to rehabilitate [...] Read more.

This paper describes the second part of the PASSO (Parkinson smart sensory cues for older users) project, which designs and tests an innovative haptic biofeedback system based on a wireless body sensor network using a smartphone and different smartwatches specifically designed to rehabilitate postural disturbances in persons with Parkinson’s disease. According to the scientific literature on the use of smart devices to transmit sensory cues, vibrotactile feedback (particularly on the trunk) seems promising for improving people’s gait and posture performance; they have been used in different environments and are well accepted by users. In the PASSO project, we designed and developed a wearable device and a related system to transmit vibrations to a person’s body to improve posture and combat impairments like Pisa syndrome and camptocormia. Specifically, this paper describes the methodologies and strategies used to design, develop, and test wearable prototypes and the mHealth system. The results allowed a multidisciplinary comparison among the solutions, which led to prototypes with a high degree of usability, wearability, accessibility, and effectiveness. This mHealth system is now being used in pilot trials with subjects with Parkinson’s disease to verify its feasibility among patients. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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13 pages, 2003 KiB

Open AccessArticle

Simplified Attachable EEG Revealed Child Development Dependent Neurofeedback Brain Acute Activities in Comparison with Visual Numerical Discrimination Task and Resting

by Kazuyuki Oda, Ricki Colman and Mamiko Koshiba

Sensors 2022, 22(19), 7207; https://doi.org/10.3390/s22197207 - 23 Sep 2022

Viewed by 2719

Abstract

The development of an easy-to-attach electroencephalograph (EEG) would enable its frequent use for the assessment of neurodevelopment and clinical monitoring. In this study, we designed a two-channel EEG headband measurement device that could be used safely and was easily attachable and removable without [...] Read more.

The development of an easy-to-attach electroencephalograph (EEG) would enable its frequent use for the assessment of neurodevelopment and clinical monitoring. In this study, we designed a two-channel EEG headband measurement device that could be used safely and was easily attachable and removable without the need for restraint or electrode paste or gel. Next, we explored the use of this device for neurofeedback applications relevant to education or neurocognitive development. We developed a prototype visual neurofeedback game in which the size of a familiar local mascot changes in the PC display depending on the user’s brain wave activity. We tested this application at a local children’s play event. Children at the event were invited to experience the game and, upon agreement, were provided with an explanation of the game and support in attaching the EEG device. The game began with a consecutive number visual discrimination task which was followed by an open-eye resting condition and then a neurofeedback task. Preliminary linear regression analyses by the least-squares method of the acquired EEG and age data in 30 participants from 5 to 20 years old suggested an age-dependent left brain lateralization of beta waves at the neurofeedback stage (p = 0.052) and of alpha waves at the open-eye resting stage (p = 0.044) with potential involvement of other wave bands. These results require further validation. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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9 pages, 2053 KiB

Open AccessArticle

Electrochemical Biosensing of Glucose Based on the Enzymatic Reduction of Glucose

by Thomas Soranzo, Awatef Ben Tahar, Ayman Chmayssem, Marc Zelsmann, Pankaj Vadgama, Jean-Luc Lenormand, Phillipe Cinquin, Donald K. Martin and Abdelkader Zebda

Sensors 2022, 22(19), 7105; https://doi.org/10.3390/s22197105 - 20 Sep 2022

Cited by 8 | Viewed by 2508

Abstract

In this work, the enzyme aldehyde reductase, also known as aldose reductase, was synthesized and cloned from a human gene. Spectrophotometric measurements show that in presence of the nicotinamide adenine dinucleotide phosphate cofactor (NADPH), the aldehyde reductase catalyzed the reduction of glucose to [...] Read more.

In this work, the enzyme aldehyde reductase, also known as aldose reductase, was synthesized and cloned from a human gene. Spectrophotometric measurements show that in presence of the nicotinamide adenine dinucleotide phosphate cofactor (NADPH), the aldehyde reductase catalyzed the reduction of glucose to sorbitol. Electrochemical measurements performed on an electrodeposited poly(methylene green)-modified gold electrode showed that in the presence of the enzyme aldehyde reductase, the electrocatalytic oxidation current of NADPH decreased drastically after the addition of glucose. These results demonstrate that aldehyde reductase is an enzyme that allows the construction of an efficient electrochemical glucose biosensor based on glucose reduction. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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10 pages, 271 KiB

Open AccessArticle

Consequences of Patellar Tendinopathy on Isokinetic Knee Strength and Jumps in Professional Volleyball Players

by Marie Chantrelle, Pierre Menu, Marie Gernigon, Bastien Louguet, Marc Dauty and Alban Fouasson-Chailloux

Sensors 2022, 22(9), 3590; https://doi.org/10.3390/s22093590 - 9 May 2022

Cited by 4 | Viewed by 3574

Abstract

Patellar tendinopathy (PT) in professional volleyball players can have an impact on their careers. We evaluated the impact of this pathology in this specific population in terms of isokinetic strength and jumping performances. Thirty-six professional male volleyball players (mean age: 24.8 ± 5.2) [...] Read more.

Patellar tendinopathy (PT) in professional volleyball players can have an impact on their careers. We evaluated the impact of this pathology in this specific population in terms of isokinetic strength and jumping performances. Thirty-six professional male volleyball players (mean age: 24.8 ± 5.2) performed isokinetic knee assessments, single-leg countermovement jumps and one leg hop test. They filled out the Victorian Institute of Sport Assessment-Patella (VISA-P) score. Two groups were assessed: “PT group” (n = 15) and “control group” (n = 21). The VISA-P score was lower in the PT group (p < 0.0001). No difference was found between the isokinetic strength limb symmetry index and the jump performance limb symmetry index. The healthy legs of the control group were compared with the affected (PT+) and the unaffected legs (PT−) of the PT group. Compared with the healthy legs, both PT+ and PT− legs showed decreased values of quadriceps and hamstring strengths. Only PT+ legs scored lower than healthy legs in countermovement jumps and hop tests. No differences were found between PT+ and PT− legs for muscle strengths and jumps. A low correlation existed between quadriceps strength and jumping performances (r > 0.3; p < 0.001). Volleyball players with PT showed a decrease in the isokinetic knee strength. This strength deficit was found both on the symptomatic legs and the asymptomatic ones. Jumps were only significantly altered on the pathological legs. Highlighting that the unaffected limbs were also impaired in addition to the affected limbs may help provide a better adaptation of the rehabilitation management. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

7 pages, 888 KiB

Open AccessCommunication

Accuracy of Real Time Continuous Glucose Monitoring during Different Liquid Solution Challenges in Healthy Adults: A Randomized Controlled Cross-Over Trial

by Janis R. Schierbauer, Svenja Günther, Sandra Haupt, Rebecca T. Zimmer, Beate E. M. Zunner, Paul Zimmermann, Nadine B. Wachsmuth, Max L. Eckstein, Felix Aberer, Harald Sourij and Othmar Moser

Sensors 2022, 22(9), 3104; https://doi.org/10.3390/s22093104 - 19 Apr 2022

Cited by 8 | Viewed by 2898

Abstract

Continuous glucose monitoring (CGM) represents an integral of modern diabetes management, however, there is still a lack of sensor performance data when rapidly consuming different liquids and thus changing total body water. 18 healthy adults (ten females, age: 23.1 ± 1.8 years, BMI [...] Read more.

Continuous glucose monitoring (CGM) represents an integral of modern diabetes management, however, there is still a lack of sensor performance data when rapidly consuming different liquids and thus changing total body water. 18 healthy adults (ten females, age: 23.1 ± 1.8 years, BMI 22.2 ± 2.1 kg·m⁻²) performed four trial visits consisting of oral ingestion (12 mL per kg body mass) of either a 0.9% sodium chloride, 5% glucose or Ringer’s solution and a control visit, in which no liquid was administered (control). Sensor glucose levels (Dexcom G6, Dexcom Inc., San Diego, CA, USA) were obtained at rest and in 10-min intervals for a period of 120 min after solution consumption and compared against reference capillary blood glucose measurements. The overall MedARD [IQR] was 7.1% [3.3–10.8]; during control 5.9% [2.7–10.8], sodium chloride 5.0% [2.7–10.2], 5% glucose 11.0% [5.3–21.6] and Ringer’s 7.5% [3.1–13.2] (p < 0.0001). The overall bias [95% LoA] was 4.3 mg·dL⁻¹ [−19 to 28]; during control 3.9 mg·dL⁻¹ [−11 to 18], sodium chloride 4.8 mg·dL⁻¹ [−9 to 19], 5% glucose 3.6 mg·dL⁻¹ [−33 to 41] and Ringer’s solution 4.9 mg·dL⁻¹ [−13 to 23]. The Dexcom G6 CGM system detects glucose with very good accuracy during liquid solution challenges in normoglycemic individuals, however, our data suggest that in people without diabetes, sensor performance is influenced by different solutions. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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16 pages, 1815 KiB

Open AccessArticle

Feelings from the Heart Part II: Simulation and Validation of Static and Dynamic HRV Decrease-Trigger Algorithms to Detect Stress in Firefighters

by Christian Rominger and Andreas R. Schwerdtfeger

Sensors 2022, 22(8), 2925; https://doi.org/10.3390/s22082925 - 11 Apr 2022

Cited by 9 | Viewed by 2272

Abstract

Several mobile devices have multiple sensors on board and interact with smartphones. This allows for a complex online evaluation of physiological data, important for interactive psychophysiological assessments, which targets the triggering of psychological states based on physiological data such as heart rate variability [...] Read more.

Several mobile devices have multiple sensors on board and interact with smartphones. This allows for a complex online evaluation of physiological data, important for interactive psychophysiological assessments, which targets the triggering of psychological states based on physiological data such as heart rate variability (HRV). However, algorithms designed to trigger meaningful physiological processes are rare. One exception is the concept of additional HRV reduction (AddHRVr), which aims to control for metabolic-related changes in cardiac activity. In this study we present an approach, based on data of a previous study, which allows algorithm settings to be derived that could be used to automatically trigger the assessment of psychosocial states by online-analysis of transient HRV changes in a sample of 38 firefighters. Settings of a static and a dynamic AddHRVr algorithm were systematically manipulated and quantified by binary triggers. These triggers were subjected to multilevel models predicting increases of objective stress during a period of 24 h. Effect estimates (i.e., odds) and bootstrap power simulations were calculated to inform about the most robust algorithm settings. This study delivers evidence that a dynamic AddHRVr algorithm can trigger transitions of stress, which should be further validated in future interactive psychophysiological assessments. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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17 pages, 5765 KiB

Open AccessArticle

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

by Syed Yaseen Shah, Hadi Larijani, Ryan M. Gibson and Dimitrios Liarokapis

Sensors 2022, 22(7), 2466; https://doi.org/10.3390/s22072466 - 23 Mar 2022

Cited by 12 | Viewed by 2583

Abstract

Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One [...] Read more.

Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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15 pages, 1896 KiB

Open AccessArticle

The Validity and Reliability of Two Commercially Available Load Sensors for Clinical Strength Assessment

by Kohle Merry, Christopher Napier, Vivian Chung, Brett C. Hannigan, Megan MacPherson, Carlo Menon and Alex Scott

Sensors 2021, 21(24), 8399; https://doi.org/10.3390/s21248399 - 16 Dec 2021

Cited by 6 | Viewed by 4460

Abstract

Objective: Handheld dynamometers are common tools for assessing/monitoring muscular strength and endurance. Health/fitness Bluetooth load sensors may provide a cost-effective alternative; however, research is needed to evaluate the validity and reliability of such devices. This study assessed the validity and reliability of two [...] Read more.

Objective: Handheld dynamometers are common tools for assessing/monitoring muscular strength and endurance. Health/fitness Bluetooth load sensors may provide a cost-effective alternative; however, research is needed to evaluate the validity and reliability of such devices. This study assessed the validity and reliability of two commercially available Bluetooth load sensors (Activ5 by Activbody and Progressor by Tindeq). Methods: Four tests were conducted on each device: stepped loading, stress relaxation, simulated exercise, and hysteresis. Each test type was repeated three times using the Instron ElectroPuls mechanical testing device (a gold-standard system). Test–retest reliability was assessed through intraclass correlations. Agreement with the gold standard was assessed with Pearson’s correlation, interclass correlation, and Lin’s concordance correlation. Results: The Activ5 and Progressor had excellent test–retest reliability across all four tests (ICC(3,1) ≥ 0.999, all p ≤ 0.001). Agreement with the gold standard was excellent for both the Activ5 (ρ ≥ 0.998, ICC(3,1) ≥ 0.971, ρ_c ≥ 0.971, all p’s ≤ 0.001) and Progressor (ρ ≥ 0.999, ICC(3,1) ≥ 0.999, ρ_c ≥ 0.999, all p’s ≤ 0.001). Measurement error increased for both devices as applied load increased. Conclusion: Excellent test–retest reliability was found, suggesting that both devices can be used in a clinical setting to measure patient progress over time; however, the Activ5 consistently had poorer agreement with the gold standard (particularly at higher loads). Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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14 pages, 1599 KiB

Open AccessArticle

Effects of Touchscreen Media Use on Toddlers’ Sleep: Insights from Longtime ECG Monitoring

by Sigrid Hackl-Wimmer, Marina Tanja Waltraud Eglmaier, Lars Eichen, Karoline Rettenbacher, Daniel Macher, Catherine Walter-Laager, Helmut Karl Lackner, Ilona Papousek and Manuela Paechter

Sensors 2021, 21(22), 7515; https://doi.org/10.3390/s21227515 - 12 Nov 2021

Cited by 3 | Viewed by 2719

Abstract

Wearable biomedical sensor technology enables reliable monitoring of physiological data, even in very young children. The purpose of the present study was to develop algorithms for gaining valid physiological indicators of sleep quality in toddlers, using data from an undisturbing and easy-to-use wearable [...] Read more.

Wearable biomedical sensor technology enables reliable monitoring of physiological data, even in very young children. The purpose of the present study was to develop algorithms for gaining valid physiological indicators of sleep quality in toddlers, using data from an undisturbing and easy-to-use wearable device. The study further reports the application of this technique to the investigation of potential impacts of early touchscreen media use. Toddlers’ touchscreen media use is of strong interest for parents, educators, and researchers. Mostly, negative effects of media use are assumed, among them, disturbances of sleep and impairments of learning and development. In 55 toddlers (32 girls, 23 boys; 27.4 ± 4.9 months; range: 16–37 months), ECG monitoring was conducted for a period of 30 (±3) h. Parents were asked about their children’s touchscreen media use and they rated their children’s sleep quality. The use of touchscreen media predicted the physiologically determined quality of sleep but not parent-reported sleep quality (such as sleep onset latency). Greater heart rate differences between restless sleep phases and restful sleep indicated poorer nighttime recovery in children with more frequent use of touchscreen media. The study demonstrates that the expert analysis of the ECG during sleep is a potent tool for the estimation of sleep quality in toddlers. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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Review

Jump to: Research, Other

17 pages, 10423 KiB

Open AccessReview

Accuracy of Intracranial Pressure Monitoring—Single Centre Observational Study and Literature Review

by Adam I. Pelah, Agnieszka Zakrzewska, Leanne A. Calviello, Teodoro Forcht Dagi, Zofia Czosnyka and Marek Czosnyka

Sensors 2023, 23(7), 3397; https://doi.org/10.3390/s23073397 - 23 Mar 2023

Cited by 3 | Viewed by 3303

Abstract

Intracranial hypertension and adequacy of brain blood flow are primary concerns following traumatic brain injury. Intracranial pressure (ICP) monitoring is a critical diagnostic tool in neurocritical care. However, all ICP sensors, irrespective of design, are subject to systematic and random measurement inaccuracies that [...] Read more.

Intracranial hypertension and adequacy of brain blood flow are primary concerns following traumatic brain injury. Intracranial pressure (ICP) monitoring is a critical diagnostic tool in neurocritical care. However, all ICP sensors, irrespective of design, are subject to systematic and random measurement inaccuracies that can affect patient care if overlooked or disregarded. The wide choice of sensors available to surgeons raises questions about performance and suitability for treatment. This observational study offers a critical review of the clinical and experimental assessment of ICP sensor accuracy and comments on the relationship between actual clinical performance, bench testing, and manufacturer specifications. Critically, on this basis, the study offers guidelines for the selection of ICP monitoring technologies, an important clinical decision. To complement this, a literature review on important ICP monitoring considerations was included. This study utilises illustrative clinical and laboratory material from 1200 TBI patients (collected from 1992 to 2019) to present several important points regarding the accuracy of in vivo implementation of contemporary ICP transducers. In addition, a thorough literature search was performed, with sources dating from 1960 to 2021. Sources considered to be relevant matched the keywords: “intraparenchymal ICP sensors”, “fiberoptic ICP sensors”, “piezoelectric strain gauge sensors”, “external ventricular drains”, “CSF reference pressure”, “ICP zero drift”, and “ICP measurement accuracy”. Based on single centre observations and the 76 sources reviewed in this paper, this material reports an overall anticipated measurement accuracy for intraparenchymal transducers of around ± 6.0 mm Hg with an average zero drift of <2.0 mm Hg. Precise ICP monitoring is a key tenet of neurocritical care, and accounting for zero drift is vital. Intraparenchymal piezoelectric strain gauge sensors are commonly implanted to monitor ICP. Laboratory bench testing results can differ from in vivo observations, revealing the shortcomings of current ICP sensors. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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21 pages, 2934 KiB

Open AccessReview

Assessment of Motor Evoked Potentials in Multiple Sclerosis

by Joško Šoda, Sanda Pavelin, Igor Vujović and Maja Rogić Vidaković

Sensors 2023, 23(1), 497; https://doi.org/10.3390/s23010497 - 2 Jan 2023

Cited by 8 | Viewed by 5094

Abstract

Transcranial magnetic stimulation (TMS) is a noninvasive technique mainly used for the assessment of corticospinal tract integrity and excitability of the primary motor cortices. Motor evoked potentials (MEPs) play a pivotal role in TMS studies. TMS clinical guidelines, concerning the use and interpretation [...] Read more.

Transcranial magnetic stimulation (TMS) is a noninvasive technique mainly used for the assessment of corticospinal tract integrity and excitability of the primary motor cortices. Motor evoked potentials (MEPs) play a pivotal role in TMS studies. TMS clinical guidelines, concerning the use and interpretation of MEPs in diagnosing and monitoring corticospinal tract integrity in people with multiple sclerosis (pwMS), were established almost ten years ago and refer mainly to the use of TMS implementation; this comprises the magnetic stimulator connected to a standard EMG unit, with the positioning of the coil performed by using the external landmarks on the head. The aim of the present work was to conduct a narrative literature review on the MEP assessment and outcome measures in clinical and research settings, assessed by TMS Methodological characteristics of different TMS system implementations (TMS without navigation, line-navigated TMS and e-field-navigated TMS); these were discussed in the context of mapping the corticospinal tract integrity in MS. An MEP assessment of two case reports, by using an e-field-navigated TMS, was presented; the results of the correspondence between the e-field-navigated TMS with MRI, and the EDSS classifications were presented. Practical and technical guiding principles for the improvement of TMS studies in MEP assessment for MS are discussed, suggesting the use of e-field TMS assessment in the sense that it can improve the accuracy of corticospinal tract integrity testing by providing a more objective correspondence of the neurophysiological (e-field-navigated TMS) and clinical (Expanded Disability Status Scale—EDSS) classifications. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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61 pages, 4074 KiB

Open AccessEditor’s ChoiceReview

Sensors and Actuation Technologies in Exoskeletons: A Review

by Monica Tiboni, Alberto Borboni, Fabien Vérité, Chiara Bregoli and Cinzia Amici

Sensors 2022, 22(3), 884; https://doi.org/10.3390/s22030884 - 24 Jan 2022

Cited by 65 | Viewed by 13182

Abstract

Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these [...] Read more.

Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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Other

Jump to: Research, Review

28 pages, 3131 KiB

Open AccessSystematic Review

Non-Contact Infrared Thermometers and Thermal Scanners for Human Body Temperature Monitoring: A Systematic Review

by Yuanzhe Zhao and Jeroen H. M. Bergmann

Sensors 2023, 23(17), 7439; https://doi.org/10.3390/s23177439 - 26 Aug 2023

Cited by 13 | Viewed by 7744

Abstract

In recent years, non-contact infrared thermometers (NCITs) and infrared thermography (IRT) have gained prominence as convenient, non-invasive tools for human body temperature measurement. Despite their widespread adoption in a range of settings, there remain questions about their accuracy under varying conditions. This systematic [...] Read more.

In recent years, non-contact infrared thermometers (NCITs) and infrared thermography (IRT) have gained prominence as convenient, non-invasive tools for human body temperature measurement. Despite their widespread adoption in a range of settings, there remain questions about their accuracy under varying conditions. This systematic review sought to critically evaluate the performance of NCITs and IRT in body temperature monitoring, synthesizing evidence from a total of 72 unique settings from 32 studies. The studies incorporated in our review ranged from climate-controlled room investigations to clinical applications. Our primary findings showed that NCITs and IRT can provide accurate and reliable body temperature measurements in specific settings and conditions. We revealed that while both NCITs and IRT displayed a consistent positive correlation with conventional, contact-based temperature measurement tools, NCITs demonstrated slightly superior accuracy over IRT. A total of 29 of 50 settings from NCIT studies and 4 of 22 settings from IRT studies achieved accuracy levels within a range of ±0.3 °C. Furthermore, we found that several factors influenced the performance of these devices. These included the measurement location, the type of sensor, the reference and tool, individual physiological attributes, and the surrounding environmental conditions. Our research underscores the critical need for further studies in this area to refine our understanding of these influential factors and to develop standardized guidelines for the use of NCITs and IRT. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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