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5 pages, 200 KiB

Open AccessEditorial

Smart Sensors for Healthcare and Medical Applications

by Domenico Formica and Emiliano Schena

Sensors 2021, 21(2), 543; https://doi.org/10.3390/s21020543 - 14 Jan 2021

Cited by 19 | Viewed by 6324

Abstract

This special issue on “Smart Sensors for Healthcare and Medical Applications” focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare [...] Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

Research

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14 pages, 4492 KiB

Open AccessArticle

Non-Contact Respiration Monitoring and Body Movements Detection for Sleep Using Thermal Imaging

by Prasara Jakkaew and Takao Onoye

Sensors 2020, 20(21), 6307; https://doi.org/10.3390/s20216307 - 5 Nov 2020

Cited by 42 | Viewed by 5132

Abstract

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring [...] Read more.

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of

1.82 \pm 0.75

bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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20 pages, 8308 KiB

Open AccessArticle

Tracking and Characterization of Spinal Cord-Injured Patients by Means of RGB-D Sensors

by Filippo Colombo Zefinetti, Andrea Vitali, Daniele Regazzoni, Caterina Rizzi and Guido Molinero

Sensors 2020, 20(21), 6273; https://doi.org/10.3390/s20216273 - 4 Nov 2020

Cited by 4 | Viewed by 2359

Abstract

In physical rehabilitation, motion capture solutions are well-known but not as widespread as they could be. The main limit to their diffusion is not related to cost or usability but to the fact that the data generated when tracking a person must be [...] Read more.

In physical rehabilitation, motion capture solutions are well-known but not as widespread as they could be. The main limit to their diffusion is not related to cost or usability but to the fact that the data generated when tracking a person must be elaborated according to the specific context and aim. This paper proposes a solution including customized motion capture and data elaboration with the aim of supporting medical personnel in the assessment of spinal cord-injured (SCI) patients using a wheelchair. The configuration of the full-body motion capturing system is based on an asymmetric 3 Microsoft Kinect v2 sensor layout that provides a path of up to 6 m, which is required to properly track the wheelchair. Data elaboration is focused on the automatic recognition of the pushing cycles and on plotting any kinematic parameter that may be interesting in the assessment. Five movements have been considered to evaluate the wheelchair propulsion: the humeral elevation, the horizontal abduction of the humerus, the humeral rotation, the elbow flexion and the trunk extension along the sagittal plane. More than 60 volunteers with a spinal cord injury were enrolled for testing the solution. To evaluate the reliability of the data computed with SCI APPlication (APP) for the pushing cycle analysis, the patients were subdivided in four groups according to the level of the spinal cord injury (i.e., high paraplegia, low paraplegia, C7 tetraplegia and C6 tetraplegia). For each group, the average value and the standard deviation were computed and a comparison with similar acquisitions performed with a high-end solution is shown. The measurements computed by the SCI-APP show a good reliability for analyzing the movements of SCI patients’ propulsion wheelchair. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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11 pages, 860 KiB

Open AccessArticle

The Danger of Walking with Socks: Evidence from Kinematic Analysis in People with Progressive Multiple Sclerosis

by Su-Chun Huang, Gloria Dalla Costa, Marco Pisa, Lorenzo Gregoris, Giulia Leccabue, Martina Congiu, Giancarlo Comi and Letizia Leocani

Sensors 2020, 20(21), 6160; https://doi.org/10.3390/s20216160 - 29 Oct 2020

Cited by 3 | Viewed by 2533

Abstract

Multiple sclerosis (MS) is characterized by gait impairments and severely impacts the quality of life. Technological advances in biomechanics offer objective assessments of gait disabilities in clinical settings. Here we employed wearable sensors to measure electromyography (EMG) and body acceleration during walking and [...] Read more.

Multiple sclerosis (MS) is characterized by gait impairments and severely impacts the quality of life. Technological advances in biomechanics offer objective assessments of gait disabilities in clinical settings. Here we employed wearable sensors to measure electromyography (EMG) and body acceleration during walking and to quantify the altered gait pattern between people with progressive MS (PwPMS) and healthy controls (HCs). Forty consecutive patients attending our department as in-patients were examined together with fifteen healthy controls. All subjects performed the timed 10 min walking test (T10MW) using a wearable accelerator and 8 electrodes attached to bilateral thighs and legs so that body acceleration and EMG activity were recorded. The T10MWs were recorded under three conditions: standard (wearing shoes), reduced grip (wearing socks) and increased cognitive load (backward-counting dual-task). PwPMS showed worse kinematics of gait and increased muscle coactivation than controls at both the thigh and leg levels. Both reduced grip and increased cognitive load caused a reduction in the cadence and velocity of the T10MW, which were correlated with one another. A higher coactivation index at the thigh level of the more affected side was positively correlated with the time of the T10MW (r = 0.5, p < 0.01), Expanded Disability Status Scale (EDSS) (r = 0.4, p < 0.05), and negatively correlated with the cadence (r = −0.6, p < 0.001). Our results suggest that excessive coactivation at the thigh level is the major determinant of the gait performance as the disease progresses. Moreover, demanding walking conditions do not influence gait in controls but deteriorate walking performances in PwPMS, thus those conditions should be prevented during hospital examinations as well as in homecare environments. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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16 pages, 3115 KiB

Open AccessArticle

Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

by Hung-Chi Chang, Hau-Tieng Wu, Po-Chiun Huang, Hsi-Pin Ma, Yu-Lun Lo and Yuan-Hao Huang

Sensors 2020, 20(21), 6067; https://doi.org/10.3390/s20216067 - 25 Oct 2020

Cited by 25 | Viewed by 3726

Abstract

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the [...] Read more.

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO₂) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was

5.0 \pm 4.5

. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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16 pages, 5571 KiB

Open AccessArticle

An fMRI Compatible Smart Device for Measuring Palmar Grasping Actions in Newborns

by Daniela Lo Presti, Sofia Dall’Orso, Silvia Muceli, Tomoki Arichi, Sara Neumane, Anna Lukens, Riccardo Sabbadini, Carlo Massaroni, Michele Arturo Caponero, Domenico Formica, Etienne Burdet and Emiliano Schena

Sensors 2020, 20(21), 6040; https://doi.org/10.3390/s20216040 - 23 Oct 2020

Cited by 11 | Viewed by 3514

Abstract

Grasping is one of the first dominant motor behaviors that enable interaction of a newborn infant with its surroundings. Although atypical grasping patterns are considered predictive of neuromotor disorders and injuries, their clinical assessment suffers from examiner subjectivity, and the neuropathophysiology is poorly [...] Read more.

Grasping is one of the first dominant motor behaviors that enable interaction of a newborn infant with its surroundings. Although atypical grasping patterns are considered predictive of neuromotor disorders and injuries, their clinical assessment suffers from examiner subjectivity, and the neuropathophysiology is poorly understood. Therefore, the combination of technology with functional magnetic resonance imaging (fMRI) may help to precisely map the brain activity associated with grasping and thus provide important insights into how functional outcomes can be improved following cerebral injury. This work introduces an MR-compatible device (i.e., smart graspable device (SGD)) for detecting grasping actions in newborn infants. Electromagnetic interference immunity (EMI) is achieved using a fiber Bragg grating sensor. Its biocompatibility and absence of electrical signals propagating through the fiber make the safety profile of the SGD particularly favorable for use with fragile infants. Firstly, the SGD design, fabrication, and metrological characterization are described, followed by preliminary assessments on a preterm newborn infant and an adult during an fMRI experiment. The results demonstrate that the combination of the SGD and fMRI can safely and precisely identify the brain activity associated with grasping behavior, which may enable early diagnosis of motor impairment and help guide tailored rehabilitation programs. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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22 pages, 5703 KiB

Open AccessArticle

A Novel Fetal Movement Simulator for the Performance Evaluation of Vibration Sensors for Wearable Fetal Movement Monitors

by Abhishek Kumar Ghosh, Sonny F. Burniston, Daniel Krentzel, Abhishek Roy, Adil Shoaib Sheikh, Talha Siddiq, Paula Mai Phuong Trinh, Marta Mambrilla Velazquez, Hei-Ting Vielle, Niamh C. Nowlan and Ravi Vaidyanathan

Sensors 2020, 20(21), 6020; https://doi.org/10.3390/s20216020 - 23 Oct 2020

Cited by 12 | Viewed by 6241

Abstract

Fetal movements (FM) are an important factor in the assessment of fetal health. However, there is currently no reliable way to monitor FM outside clinical environs. While extensive research has been carried out using accelerometer-based systems to monitor FM, the desired accuracy of [...] Read more.

Fetal movements (FM) are an important factor in the assessment of fetal health. However, there is currently no reliable way to monitor FM outside clinical environs. While extensive research has been carried out using accelerometer-based systems to monitor FM, the desired accuracy of detection is yet to be achieved. A major challenge has been the difficulty of testing and calibrating sensors at the pre-clinical stage. Little is known about fetal movement features, and clinical trials involving pregnant women can be expensive and ethically stringent. To address these issues, we introduce a novel FM simulator, which can be used to test responses of sensor arrays in a laboratory environment. The design uses a silicon-based membrane with material properties similar to that of a gravid abdomen to mimic the vibrations due to fetal kicks. The simulator incorporates mechanisms to pre-stretch the membrane and to produce kicks similar to that of a fetus. As a case study, we present results from a comparative study of an acoustic sensor, an accelerometer, and a piezoelectric diaphragm as candidate vibration sensors for a wearable FM monitor. We find that the acoustic sensor and the piezoelectric diaphragm are better equipped than the accelerometer to determine durations, intensities, and locations of kicks, as they have a significantly greater response to changes in these conditions than the accelerometer. Additionally, we demonstrate that the acoustic sensor and the piezoelectric diaphragm can detect weaker fetal movements (threshold wall displacements are less than 0.5 mm) compared to the accelerometer (threshold wall displacement is 1.5 mm) with a trade-off of higher power signal artefacts. Finally, we find that the piezoelectric diaphragm produces better signal-to-noise ratios compared to the other two sensors in most of the cases, making it a promising new candidate sensor for wearable FM monitors. We believe that the FM simulator represents a key development towards enabling the eventual translation of wearable FM monitoring garments. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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24 pages, 3510 KiB

Open AccessArticle

Automatic and Real-Time Computation of the 30-Seconds Chair-Stand Test without Professional Supervision for Community-Dwelling Older Adults

by Antonio Cobo, Elena Villalba-Mora, Rodrigo Pérez-Rodríguez, Xavier Ferre, Walter Escalante, Cristian Moral and Leocadio Rodriguez-Mañas

Sensors 2020, 20(20), 5813; https://doi.org/10.3390/s20205813 - 14 Oct 2020

Cited by 11 | Viewed by 4326

Abstract

The present paper describes a system for older people to self-administer the 30-s chair stand test (CST) at home without supervision. The system comprises a low-cost sensor to count sit-to-stand (SiSt) transitions, and an Android application to guide older people through the procedure. [...] Read more.

The present paper describes a system for older people to self-administer the 30-s chair stand test (CST) at home without supervision. The system comprises a low-cost sensor to count sit-to-stand (SiSt) transitions, and an Android application to guide older people through the procedure. Two observational studies were conducted to test (i) the sensor in a supervised environment (n = 7; m = 83.29 years old, sd = 4.19; 5 female), and (ii) the complete system in an unsupervised one (n = 7; age 64–74 years old; 3 female). The participants in the supervised test were asked to perform a 30-s CST with the sensor, while a member of the research team manually counted valid transitions. Automatic and manual counts were perfectly correlated (Pearson’s r = 1, p = 0.00). Even though the sample was small, none of the signals around the critical score were affected by harmful noise; p (harmless noise) = 1, 95% CI = (0.98, 1). The participants in the unsupervised test used the system in their homes for a month. None of them dropped out, and they reported it to be easy to use, comfortable, and easy to understand. Thus, the system is suitable to be used by older adults in their homes without professional supervision. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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21 pages, 7020 KiB

Open AccessArticle

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

by Raluca Maria Aileni, Sever Pasca and Adriana Florescu

Sensors 2020, 20(12), 3346; https://doi.org/10.3390/s20123346 - 12 Jun 2020

Cited by 23 | Viewed by 5464

Abstract

Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can [...] Read more.

Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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24 pages, 11694 KiB

Open AccessArticle

A Game-Based Rehabilitation System for Upper-Limb Cerebral Palsy: A Feasibility Study

by Mohammad I. Daoud, Abdullah Alhusseini, Mostafa Z. Ali and Rami Alazrai

Sensors 2020, 20(8), 2416; https://doi.org/10.3390/s20082416 - 24 Apr 2020

Cited by 17 | Viewed by 4424

Abstract

Game-based rehabilitation systems provide an effective tool to engage cerebral palsy patients in physical exercises within an exciting and entertaining environment. A crucial factor to ensure the effectiveness of game-based rehabilitation systems is to assess the correctness of the movements performed by the [...] Read more.

Game-based rehabilitation systems provide an effective tool to engage cerebral palsy patients in physical exercises within an exciting and entertaining environment. A crucial factor to ensure the effectiveness of game-based rehabilitation systems is to assess the correctness of the movements performed by the patient during the game-playing sessions. In this study, we propose a game-based rehabilitation system for upper-limb cerebral palsy that includes three game-based exercises and a computerized assessment method. The game-based exercises aim to engage the participant in shoulder flexion, shoulder horizontal abduction/adduction, and shoulder adduction physical exercises that target the right arm. Human interaction with the game-based rehabilitation system is achieved using a Kinect sensor that tracks the skeleton joints of the participant. The computerized assessment method aims to assess the correctness of the right arm movements during each game-playing session by analyzing the tracking data acquired by the Kinect sensor. To evaluate the performance of the computerized assessment method, two groups of participants volunteered to participate in the game-based exercises. The first group included six cerebral palsy children and the second group included twenty typically developing subjects. For every participant, the computerized assessment method was employed to assess the correctness of the right arm movements in each game-playing session and these computer-based assessments were compared with matching gold standard evaluations provided by an experienced physiotherapist. The results reported in this study suggest the feasibility of employing the computerized assessment method to evaluate the correctness of the right arm movements during the game-playing sessions. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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23 pages, 4191 KiB

Open AccessArticle

Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach

by Daniel Sanchez-Morillo, Pilar Muñoz-Zara, Alejandro Lara-Doña and Antonio Leon-Jimenez

Sensors 2020, 20(4), 1178; https://doi.org/10.3390/s20041178 - 20 Feb 2020

Cited by 7 | Viewed by 17636

Abstract

Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O₂ concentrators (POC) in LTOT is being expanded. However, the issue of [...] Read more.

Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O₂ concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O₂ flow automatically by real-time classifying the intensity of a patient’s physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O₂ flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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20 pages, 6117 KiB

Open AccessArticle

Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory

by Enrico Piovanelli, Davide Piovesan, Shouhei Shirafuji, Becky Su, Natsue Yoshimura, Yousuke Ogata and Jun Ota

Sensors 2020, 20(3), 724; https://doi.org/10.3390/s20030724 - 28 Jan 2020

Cited by 5 | Viewed by 4996

Abstract

Muscle functional MRI (mfMRI) is an imaging technique that assess muscles’ activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle [...] Read more.

Muscle functional MRI (mfMRI) is an imaging technique that assess muscles’ activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of a method our group recently introduced to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method’s validity showing its potential in diagnostic and rehabilitation fields. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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20 pages, 1380 KiB

Open AccessArticle

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

by Chakaveh Ahmadizadeh and Carlo Menon

Sensors 2019, 19(24), 5519; https://doi.org/10.3390/s19245519 - 13 Dec 2019

Cited by 9 | Viewed by 3827

Abstract

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as [...] Read more.

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as reported by multiple studies, accuracies of the FSR-based pressure sensing systems decrease when sensors are bent to be positioned on a limb. This study proposes the use of regression-based methods for sensor calibration to address this problem. A sensor matrix was placed in a pressure chamber as the pressure was increased and decreased in a cyclic manner. Sensors’ responses were assessed when the matrix was placed on a flat surface or on one of five curved surfaces with various curvatures. Three regression algorithms, namely linear regression (LR), general regression neural network (GRNN), and random forest (RF), were assessed. GRNN was selected due to its performance. Various error compensation methods using GRNN were investigated and compared to improve instability of sensors’ responses. All methods showed improvements in results compared to the baseline. Developing a different model for each of the curvatures yielded the best results. This study proved the feasibility of using regression-based error compensation methods to improve the accuracy of mapping sensor readings to pressure values. This can improve the overall accuracy of FSR-based sensory systems used in prosthetic applications. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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14 pages, 3847 KiB

Open AccessArticle

Development of a Sensor to Measure Physician Consultation Times

by Roman Gabl and Florian Stummer

Sensors 2019, 19(24), 5359; https://doi.org/10.3390/s19245359 - 5 Dec 2019

Cited by 4 | Viewed by 3083

Abstract

The duration of patient–physician contact is an important factor for the optimisation of treatment processes in healthcare systems. Available methods can be labour-intensive and the quality is, in many cases, poor. A part of this research project is to develop a sensor system, [...] Read more.

The duration of patient–physician contact is an important factor for the optimisation of treatment processes in healthcare systems. Available methods can be labour-intensive and the quality is, in many cases, poor. A part of this research project is to develop a sensor system, which allows the detection of people passing through a door, including the direction. For this purpose, two time of flight sensors are combined with a door sensor and a motion detection sensor (for redundancy) on one single side of the door frame. The period between two single measurements could be reduced to 50 ms, which allows the measurement of walking speed up to 2 ms

^{- 1}

. The accuracy of the time stamp for each event is less than one second and ensures a precise documentation of the consultation time. This paper presents the development of the sensor system, the miniaturisation of the installation and first measurement results, as well as the measurement’s concept of quality analysis, including multiple door applications. In future steps, the sensor system will be deployed at different medical practices to determine the exact duration of the patient–physician interaction over a longer time period. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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11 pages, 9508 KiB

Open AccessArticle

Measurement of Pulse Wave Signals and Blood Pressure by a Plastic Optical Fiber FBG Sensor

by Yuki Haseda, Julien Bonefacino, Hwa-Yaw Tam, Shun Chino, Shouhei Koyama and Hiroaki Ishizawa

Sensors 2019, 19(23), 5088; https://doi.org/10.3390/s19235088 - 21 Nov 2019

Cited by 61 | Viewed by 6563

Abstract

Fiber Bragg grating (FBG) sensors fabricated in silica optical fiber (Silica-FBG) have been used to measure the strain of human arteries as pulse wave signals. A variety of vital signs including blood pressure can be derived from these signals. However, silica optical fiber [...] Read more.

Fiber Bragg grating (FBG) sensors fabricated in silica optical fiber (Silica-FBG) have been used to measure the strain of human arteries as pulse wave signals. A variety of vital signs including blood pressure can be derived from these signals. However, silica optical fiber presents a safety risk because it is easily fractured. In this research, an FBG sensor fabricated in plastic optical fiber (POF-FBG) was employed to resolve this problem. Pulse wave signals were measured by POF-FBG and silica-FBG sensors for four subjects. After signal processing, a calibration curve was constructed by partial least squares regression, then blood pressure was calculated from the calibration curve. As a result, the POF-FBG sensor could measure the pulse wave signals with an signal to noise (SN) ratio at least eight times higher than the silica-FBG sensor. Further, the measured signals were substantially similar to those of an acceleration plethysmograph (APG). Blood pressure is measured with low error, but the POF-FBG APG correlation is distributed from 0.54 to 0.72, which is not as high as desired. Based on these results, pulse wave signals should be measured under a wide range of reference blood pressures to confirm the reliability of blood pressure measurement uses POF-FBG sensors. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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11 pages, 2680 KiB

Open AccessArticle

Cost-efficient and Custom Electrode-holder Assembly Infrastructure for EEG Recordings

by Yuan-Pin Lin, Ting-Yu Chen and Wei-Jen Chen

Sensors 2019, 19(19), 4273; https://doi.org/10.3390/s19194273 - 2 Oct 2019

Cited by 13 | Viewed by 6849

Abstract

Mobile electroencephalogram (EEG)-sensing technologies have rapidly progressed and made the access of neuroelectrical brain activity outside the laboratory in everyday life more realistic. However, most existing EEG headsets exhibit a fixed design, whereby its immobile montage in terms of electrode density and coverage [...] Read more.

Mobile electroencephalogram (EEG)-sensing technologies have rapidly progressed and made the access of neuroelectrical brain activity outside the laboratory in everyday life more realistic. However, most existing EEG headsets exhibit a fixed design, whereby its immobile montage in terms of electrode density and coverage inevitably poses a great challenge with applicability and generalizability to the fundamental study and application of the brain-computer interface (BCI). In this study, a cost-efficient, custom EEG-electrode holder infrastructure was designed through the assembly of primary components, including the sensor-positioning ring, inter-ring bridge, and bridge shield. It allows a user to (re)assemble a compact holder grid to accommodate a desired number of electrodes only to the regions of interest of the brain and iteratively adapt it to a given head size for optimal electrode-scalp contact and signal quality. This study empirically demonstrated its easy-to-fabricate nature by a low-end fused deposition modeling (FDM) 3D printer and proved its practicability of capturing event-related potential (ERP) and steady-state visual-evoked potential (SSVEP) signatures over 15 subjects. This paper highlights the possibilities for a cost-efficient electrode-holder assembly infrastructure with replaceable montage, flexibly retrofitted in an unlimited fashion, for an individual for distinctive fundamental EEG studies and BCI applications. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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11 pages, 17604 KiB

Open AccessArticle

Active Body Pressure Relief System with Time-of-Flight Optical Pressure Sensors for Pressure Ulcer Prevention

by Kang-Ho Lee, Yeong-Eun Kwon, Hyukjin Lee, Yongkoo Lee, Joonho Seo, Ohwon Kwon, Shin-Won Kang and Dongkyu Lee

Sensors 2019, 19(18), 3862; https://doi.org/10.3390/s19183862 - 6 Sep 2019

Cited by 21 | Viewed by 5577

Abstract

A body pressure relief system was newly developed with optical pressure sensors for pressure ulcer prevention. Unlike a conventional alternating pressure air mattress (APAM), this system automatically regulates air flow into a body supporting mattress with adaptive inflation (or deflation) duration in response [...] Read more.

A body pressure relief system was newly developed with optical pressure sensors for pressure ulcer prevention. Unlike a conventional alternating pressure air mattress (APAM), this system automatically regulates air flow into a body supporting mattress with adaptive inflation (or deflation) duration in response to the pressure level in order to reduce skin stress due to prolonged high pressures. The system continuously quantifies the body pressure distribution using time-of-flight (ToF) optical sensors. The proposed pressure sensor, a ToF optical sensor in the air-filled cell, measures changes in surface height of mattress when pressed under body weight, thereby indirectly indicating the interface pressure. Non-contact measurement of optical sensor usually improves the durability and repeatability of the system. The pressure sensor was successfully identified the 4 different-predefined postures, and quantitatively measured the body pressure distribution of them. Duty cycle of switches in solenoid valves was adjusted to 0–50% for pressure relief, which shows that the interface pressure was lower than 32 mmHg for pressure ulcer prevention. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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14 pages, 3082 KiB

Open AccessArticle

Cardio-Respiratory Monitoring in Archery Using a Smart Textile Based on Flexible Fiber Bragg Grating Sensors

by Daniela Lo Presti, Chiara Romano, Carlo Massaroni, Jessica D’Abbraccio, Luca Massari, Michele Arturo Caponero, Calogero Maria Oddo, Domenico Formica and Emiliano Schena

Sensors 2019, 19(16), 3581; https://doi.org/10.3390/s19163581 - 17 Aug 2019

Cited by 89 | Viewed by 7117

Abstract

In precision sports, the control of breathing and heart rate is crucial to help the body to remain stable in the shooting position. To improve stability, archers try to adopt similar breathing patterns and to have a low heartbeat during each shot. We [...] Read more.

In precision sports, the control of breathing and heart rate is crucial to help the body to remain stable in the shooting position. To improve stability, archers try to adopt similar breathing patterns and to have a low heartbeat during each shot. We proposed an easy-to-use and unobtrusive smart textile (ST) which is able to detect chest wall excursions due to breathing and heart beating. The sensing part is based on two FBGs housed into a soft polymer matrix to optimize the adherence to the chest wall and the system robustness. The ST was assessed on volunteers to figure out its performance in the estimation of respiratory frequency (f_R) and heart rate (HR). Then, the system was tested on two archers during four shooting sessions. This is the first study to monitor cardio-respiratory activity on archers during shooting. The good performance of the ST is supported by the low mean absolute percentage error for f_R and HR estimation (≤1.97% and ≤5.74%, respectively), calculated with respect to reference signals (flow sensor for f_R, photopletismography sensor for HR). Moreover, results showed the capability of the ST to estimate f_R and HR during different phases of shooting action. The promising results motivate future investigations to speculate about the influence of f_R and HR on archers’ performance. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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13 pages, 2761 KiB

Open AccessArticle

In Vivo Measurement of Cervical Elasticity on Pregnant Women by Torsional Wave Technique: A Preliminary Study

by Paloma Massó, Antonio Callejas, Juan Melchor, Francisca S. Molina and Guillermo Rus

Sensors 2019, 19(15), 3249; https://doi.org/10.3390/s19153249 - 24 Jul 2019

Cited by 14 | Viewed by 3875

Abstract

A torsional wave (TW) sensor prototype was employed to quantify stiffness of the cervix in pregnant women. A cross-sectional study in a total of 18 women between 16 weeks and 35 weeks + 5 days of gestation was performed. The potential of TW [...] Read more.

A torsional wave (TW) sensor prototype was employed to quantify stiffness of the cervix in pregnant women. A cross-sectional study in a total of 18 women between 16 weeks and 35 weeks + 5 days of gestation was performed. The potential of TW technique to assess cervical ripening was evaluated by the measurement of stiffness related to gestational age and cervical length. Statistically significant correlations were found between cervical stiffness and gestational age (

R^{2} = 0.370

,

p = 0.0074

, using 1 kHz waves and

R^{2} = 0.445

,

p = 0.0250

, using 1.5 kHz waves). A uniform decrease in stiffness of the cervical tissue was confirmed to happen during the complete gestation. There was no significant correlation between stiffness and cervical length. A stronger association between gestational age and cervical stiffness was found compared to gestational age and cervical length correlation. As a conclusion, TW technique is a feasible approach to objectively quantify the decrease of cervical stiffness related to gestational age. Further research is required to evaluate the application of TW technique in obstetric evaluations, such as prediction of preterm delivery and labor induction failure. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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19 pages, 2974 KiB

Open AccessArticle

Simulating Arbitrary Electrode Reversals in Standard 12-Lead ECG

by Vessela Krasteva, Irena Jekova and Ramun Schmid

Sensors 2019, 19(13), 2920; https://doi.org/10.3390/s19132920 - 1 Jul 2019

Cited by 9 | Viewed by 11231

Abstract

Electrode reversal errors in standard 12-lead electrocardiograms (ECG) can produce significant ECG changes and, in turn, misleading diagnoses. Their detection is important but mostly limited to the design of criteria using ECG databases with simulated reversals, without Wilson’s central terminal (WCT) potential change. [...] Read more.

Electrode reversal errors in standard 12-lead electrocardiograms (ECG) can produce significant ECG changes and, in turn, misleading diagnoses. Their detection is important but mostly limited to the design of criteria using ECG databases with simulated reversals, without Wilson’s central terminal (WCT) potential change. This is, to the best of our knowledge, the first study that presents an algebraic transformation for simulation of all possible ECG cable reversals, including those with displaced WCT, where most of the leads appear with distorted morphology. The simulation model of ECG electrode swaps and the resultant WCT potential change is derived in the standard 12-lead ECG setup. The transformation formulas are theoretically compared to known limb lead reversals and experimentally proven for unknown limb–chest electrode swaps using a 12-lead ECG database from 25 healthy volunteers (recordings without electrode swaps and with 5 unicolor pairs swaps, including red (right arm—C1), yellow (left arm—C2), green (left leg (LL) —C3), black (right leg (RL)—C5), all unicolor pairs). Two applications of the transformation are shown to be feasible: ‘Forward’ (simulation of reordered leads from correct leads) and ‘Inverse’ (reconstruction of correct leads from an ECG recorded with known electrode reversals). Deficiencies are found only when the ground RL electrode is swapped as this case requires guessing the unknown RL electrode potential. We suggest assuming that potential to be equal to that of the LL electrode. The ‘Forward’ transformation is important for comprehensive training platforms of humans and machines to reliably recognize simulated electrode swaps using the available resources of correctly recorded ECG databases. The ‘Inverse’ transformation can save time and costs for repeated ECG recordings by reconstructing the correct lead set if a lead swap is detected after the end of the recording. In cases when the electrode reversal is unknown but a prior correct ECG recording of the same patient is available, the ‘Inverse’ transformation is tested to detect the exact swapping of the electrodes with an accuracy of (96% to 100%). Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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15 pages, 1048 KiB

Open AccessArticle

Non-Contact Monitoring of Breathing Pattern and Respiratory Rate via RGB Signal Measurement

by Carlo Massaroni, Daniela Lo Presti, Domenico Formica, Sergio Silvestri and Emiliano Schena

Sensors 2019, 19(12), 2758; https://doi.org/10.3390/s19122758 - 19 Jun 2019

Cited by 79 | Viewed by 7713

Abstract

Among all the vital signs, respiratory rate remains the least measured in several scenarios, mainly due to the intrusiveness of the sensors usually adopted. For this reason, all contactless monitoring systems are gaining increasing attention in this field. In this paper, we present [...] Read more.

Among all the vital signs, respiratory rate remains the least measured in several scenarios, mainly due to the intrusiveness of the sensors usually adopted. For this reason, all contactless monitoring systems are gaining increasing attention in this field. In this paper, we present a measuring system for contactless measurement of the respiratory pattern and the extraction of breath-by-breath respiratory rate. The system consists of a laptop’s built-in RGB camera and an algorithm for post-processing of acquired video data. From the recording of the chest movements of a subject, the analysis of the pixel intensity changes yields a waveform indicating respiratory pattern. The proposed system has been tested on 12 volunteers, both males and females seated in front of the webcam, wearing both slim-fit and loose-fit t-shirts. The pressure-drop signal recorded at the level of nostrils with a head-mounted wearable device was used as reference respiratory pattern. The two methods have been compared in terms of mean of absolute error, standard error, and percentage error. Additionally, a Bland–Altman plot was used to investigate the bias between methods. Results show the ability of the system to record accurate values of respiratory rate, with both slim-fit and loose-fit clothing. The measuring system shows better performance on females. Bland–Altman analysis showed a bias of −0.01 breaths

\cdot

min

^{- 1}

, with respiratory rate values between 10 and 43 breaths

\cdot

min

^{- 1}

. Promising performance has been found in the preliminary tests simulating tachypnea. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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12 pages, 1862 KiB

Open AccessArticle

Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

by Shanshan Tian, Mengxuan Li, Yifei Wang and Xi Chen

Sensors 2019, 19(11), 2529; https://doi.org/10.3390/s19112529 - 3 Jun 2019

Cited by 5 | Viewed by 3043

Abstract

Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and [...] Read more.

Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and rehabilitation; however, gait correlation as a gait characteristic is less utilized currently. In this study, a new non-contact electrostatic field sensing method was used to obtain the electrostatic gait signals of hemiplegic patients and healthy control subjects, and an improved Detrended Cross-Correlation Analysis cross-correlation coefficient method was proposed to analyze the obtained electrostatic gait signals. The results show that the improved method can better obtain the dynamic changes of the scaling index under the multi-scale structure, which makes up for the shortcomings of the traditional Detrended Cross-Correlation Analysis cross-correlation coefficient method when calculating the electrostatic gait signal of the same kind of subjects, such as random and incomplete similarity in the trend of the scaling index spectrum change. At the same time, it can effectively quantify the correlation of electrostatic gait signals in subjects. The proposed method has the potential to be a powerful tool for extracting the gait correlation features and identifying the electrostatic gait of hemiplegic patients. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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13 pages, 2936 KiB

Open AccessArticle

A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements

by Luca Massari, Andrea Bulletti, Sahana Prasanna, Marina Mazzoni, Francesco Frosini, Elena Vicari, Marcello Pantano, Fabio Staderini, Gastone Ciuti, Fabio Cianchi, Luca Messerini, Lorenzo Capineri, Arianna Menciassi and Calogero Maria Oddo

Sensors 2019, 19(11), 2512; https://doi.org/10.3390/s19112512 - 31 May 2019

Cited by 4 | Viewed by 3915

Abstract

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanical and acoustical properties of tissues, because of [...] Read more.

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanical and acoustical properties of tissues, because of changes in stiffness and density. A force sensor and an ultrasound probe were combined to detect such alterations during force-regulated indentations. To explore the specimens, regardless of their orientation and shape, a scanned area of the test sample was defined using shape recognition applying optical background subtraction to the images captured by a camera. The motorized platform was validated using seven phantom tissues, simulating the mechanical and acoustical properties of ex-vivo diseased tissues, including stiffer nodules that can be encountered in pathological conditions during histological analyses. Results demonstrated the platform’s ability to automatically explore and identify the inclusions in the phantom. Overall, the system was able to correctly identify up to 90.3% of the inclusions by means of stiffness in combination with ultrasound measurements, paving pathways towards robotic palpation during intraoperative examinations. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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28 pages, 2880 KiB

Open AccessReview

A Review: Photonic Devices Used for Dosimetry in Medical Radiation

by Edrine Damulira, Muhammad Nur Salihin Yusoff, Ahmad Fairuz Omar and Nur Hartini Mohd Taib

Sensors 2019, 19(10), 2226; https://doi.org/10.3390/s19102226 - 14 May 2019

Cited by 54 | Viewed by 7394

Abstract

Numerous instruments such as ionization chambers, hand-held and pocket dosimeters of various types, film badges, thermoluminescent dosimeters (TLDs) and optically stimulated luminescence dosimeters (OSLDs) are used to measure and monitor radiation in medical applications. Of recent, photonic devices have also been adopted. This [...] Read more.

Numerous instruments such as ionization chambers, hand-held and pocket dosimeters of various types, film badges, thermoluminescent dosimeters (TLDs) and optically stimulated luminescence dosimeters (OSLDs) are used to measure and monitor radiation in medical applications. Of recent, photonic devices have also been adopted. This article evaluates recent research and advancements in the applications of photonic devices in medical radiation detection primarily focusing on four types; photodiodes – including light-emitting diodes (LEDs), phototransistors—including metal oxide semiconductor field effect transistors (MOSFETs), photovoltaic sensors/solar cells, and charge coupled devices/charge metal oxide semiconductors (CCD/CMOS) cameras. A comprehensive analysis of the operating principles and recent technologies of these devices is performed. Further, critical evaluation and comparison of their benefits and limitations as dosimeters is done based on the available studies. Common factors barring photonic devices from being used as radiation detectors are also discussed; with suggestions on possible solutions to overcome these barriers. Finally, the potentials of these devices and the challenges of realizing their applications as quintessential dosimeters are highlighted for future research and improvements. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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Other

Jump to: Editorial, Research, Review

11 pages, 1996 KiB

Open AccessLetter

A Dual-Padded, Protrusion-Incorporated, Ring-Type Sensor for the Measurement of Food Mass and Intake

by Wonki Hong, Jungmin Lee and Won Gu Lee

Sensors 2020, 20(19), 5623; https://doi.org/10.3390/s20195623 - 1 Oct 2020

Cited by 6 | Viewed by 3554

Abstract

Dietary monitoring is vital in healthcare because knowing food mass and intake (FMI) plays an essential role in revitalizing a person’s health and physical condition. In this study, we report the development of a highly sensitive ring-type biosensor for the detection of FMI [...] Read more.

Dietary monitoring is vital in healthcare because knowing food mass and intake (FMI) plays an essential role in revitalizing a person’s health and physical condition. In this study, we report the development of a highly sensitive ring-type biosensor for the detection of FMI for dietary monitoring. To identify lightweight food on a spoon, we enhance the sensing system’s sensitivity with three components: (1) a first-class lever mechanism, (2) a dual pad sensor, and (3) a force focusing structure using a ring surface having protrusions. As a result, we confirmed that, as the food arm’s length increases, the force detected at the sensor is amplified by the first-class lever mechanism. Moreover, we obtained 1.88 and 1.71 times amplification using the dual pad sensor and the force focusing structure, respectively. Furthermore, the ring-type biosensor showed significant potential as a diagnostic indicator because the ring sensor signal was linearly proportional to the food mass delivered in a spoon, with R² = 0.988, and an average F₁ score of 0.973. Therefore, we believe that this approach is potentially beneficial for developing a dietary monitoring platform to support the prevention of obesity, which causes several adult diseases, and to keep the FMI data collection process automated in a quantitative, network-controlled manner. Full article

(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)

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