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10 pages, 5878 KiB

Open AccessCommunication

Estimating Thoracic Movement with High-Sampling Rate THz Technology

by Christoph Hoog Antink, Romina Schulz, Maurice Rohr, Konstantin Wenzel, Lars Liebermeister, Robert Kohlhaas and Sascha Preu

Sensors 2023, 23(11), 5233; https://doi.org/10.3390/s23115233 - 31 May 2023

Cited by 1 | Viewed by 1127

Abstract

We use a high-sampling rate terahertz (THz) homodyne spectroscopy system to estimate thoracic movement from healthy subjects performing breathing at different frequencies. The THz system provides both the amplitude and phase of the THz wave. From the raw phase information, a motion signal [...] Read more.

We use a high-sampling rate terahertz (THz) homodyne spectroscopy system to estimate thoracic movement from healthy subjects performing breathing at different frequencies. The THz system provides both the amplitude and phase of the THz wave. From the raw phase information, a motion signal is estimated. An electrocardiogram (ECG) signal is recorded with a polar chest strap to obtain ECG-derived respiration information. While the ECG showed sub-optimal performance for the purpose and only provided usable information for some subjects, the signal derived from the THz system showed good agreement with the measurement protocol. Over all the subjects, a root mean square estimation error of 1.40 BPM is obtained. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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15 pages, 2333 KiB

Open AccessArticle

Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling

by Pragathi Priyadharsini Balasubramani, Anuja Walke, Gillian Grennan, Andrew Perley, Suzanna Purpura, Dhakshin Ramanathan, Todd P. Coleman and Jyoti Mishra

Sensors 2022, 22(23), 9242; https://doi.org/10.3390/s22239242 - 28 Nov 2022

Cited by 1 | Viewed by 2493

Abstract

Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related [...] Read more.

Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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17 pages, 994 KiB

Open AccessArticle

A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards

by Lenin-Guillermo Lemus-Zúñiga, Juan M. Félix, Alvaro Fides-Valero, José-Vte. Benlloch-Dualde and Antonio Martinez-Millana

Sensors 2022, 22(4), 1646; https://doi.org/10.3390/s22041646 - 19 Feb 2022

Cited by 9 | Viewed by 3333

Abstract

The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products [...] Read more.

The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people’s health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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24 pages, 8145 KiB

Open AccessArticle

Development of an Integrated EEG/fNIRS Brain Function Monitoring System

by Manal Mohamed, Eunjung Jo, Nourelhuda Mohamed, Minhee Kim, Jeong-dae Yun and Jae Gwan Kim

Sensors 2021, 21(22), 7703; https://doi.org/10.3390/s21227703 - 19 Nov 2021

Cited by 6 | Viewed by 3095

Abstract

In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is [...] Read more.

In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of −115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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19 pages, 7481 KiB

Open AccessArticle

MIMO Antenna System for Modern 5G Handheld Devices with Healthcare and High Rate Delivery

by Saad Hassan Kiani, Ahsan Altaf, Muhammad Rizwan Anjum, Sharjeel Afridi, Zulfiqar Ali Arain, Sadia Anwar, Salahuddin Khan, Mohammad Alibakhshikenari, Ali Lalbakhsh, Muhammad Abbas Khan, Raed A. Abd-Alhameed and Ernesto Limiti

Sensors 2021, 21(21), 7415; https://doi.org/10.3390/s21217415 - 08 Nov 2021

Cited by 34 | Viewed by 3929

Abstract

In this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4–3.6 GHz, and [...] Read more.

In this work, a new prototype of the eight-element MIMO antenna system for 5G communications, internet of things, and networks has been proposed. This system is based on an H-shaped monopole antenna system that offers 200 MHz bandwidth ranges between 3.4–3.6 GHz, and the isolation between any two elements is well below −12 dB without using any decoupling structure. The proposed system is designed on a commercially available 0.8 mm-thick FR4 substrate. One side of the chassis is used to place the radiating elements, while the copper from the other side is being removed to avoid short-circuiting with other components and devices. This also enables space for other systems, sub-systems, and components. A prototype is fabricated and excellent agreement is observed between the experimental and the computed results. It was found that ECC is 0.2 for any two radiating elements which is consistent with the desirable standards, and channel capacity is 38 bps/Hz which is 2.9 times higher than 4 × 4 MIMO configuration. In addition, single hand mode and dual hand mode analysis are conducted to understand the operation of the system under such operations and to identify losses and/or changes in the key performance parameters. Based on the results, the proposed antenna system will find its applications in modern 5G handheld devices and internet of things with healthcare and high rate delivery. Besides that, its design simplicity will make it applicable for mass production to be used in industrial demands. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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15 pages, 7902 KiB

Open AccessArticle

Heart and Lung Sound Measurement Using an Esophageal Stethoscope with Adaptive Noise Cancellation

by Nourelhuda Mohamed, Hyun-Seok Kim, Kyu-Min Kang, Manal Mohamed, Sung-Hoon Kim and Jae Gwan Kim

Sensors 2021, 21(20), 6757; https://doi.org/10.3390/s21206757 - 12 Oct 2021

Cited by 5 | Viewed by 3231

Abstract

In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient’s cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this [...] Read more.

In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient’s cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this device is used in an operating room. In this study, a digital esophageal stethoscope system was designed. A 3D-printed case filled with Polydimethylsiloxane material was designed to hold two electret-type microphones. One of the microphones was placed inside the printed case to collect the heart and lung sound signals coming out from the patient through the esophageal catheter, the other was mounted on the surface of the case to collect the operating room sounds. A developed adaptive noise canceling algorithm was implemented to remove the operating room noise corrupted with the main heart and lung sound signals and the output signal was displayed on software application developed especially for this study. Using the designed case, the noise level of the signal was reduced to some extent, and by adding the adaptive filter, further noise reduction was achieved. The designed system is lightweight and can provide noise-free heart and lung sound signals. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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20 pages, 6225 KiB

Open AccessArticle

Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing

by Marco Caruso, Angelo Maria Sabatini, Marco Knaflitz, Ugo Della Croce and Andrea Cereatti

Sensors 2021, 21(18), 6307; https://doi.org/10.3390/s21186307 - 21 Sep 2021

Cited by 9 | Viewed by 2414

Abstract

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is [...] Read more.

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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Review

Jump to: Research

38 pages, 857 KiB

Open AccessReview

Technological Solutions for Social Isolation Monitoring of the Elderly: A Survey of Selected Projects from Academia and Industry

by Ghazi Bouaziz, Damien Brulin and Eric Campo

Sensors 2022, 22(22), 8802; https://doi.org/10.3390/s22228802 - 14 Nov 2022

Cited by 2 | Viewed by 3399

Abstract

Social isolation is likely to be one of the most serious health outcomes for the elderly due to the COVID-19 pandemic, especially for seniors living alone at home. In fact, two approaches have been used to assess social isolation. The first is a [...] Read more.

Social isolation is likely to be one of the most serious health outcomes for the elderly due to the COVID-19 pandemic, especially for seniors living alone at home. In fact, two approaches have been used to assess social isolation. The first is a self-reported survey designed for research purposes. The second approach is the use of monitoring technology. The objective of this paper is to provide some illustrative publications, works and examples of the current status and future prospects in the field of monitoring systems that focused on two main activities of daily living: meal-taking activity (shopping, cooking, eating and washing dishes) and mobility (inside the home and the act of going out). These two activities combined seem relevant to a potential risk of social isolation in the elderly. Although current research focuses on identifying only ADLs, we propose to use them as a first step to extract daily habits and risk level of social isolation. Moreover, since activity recognition is a recent field, we raise specific problems as well as needed contributions and we propose directions and research opportunities to accelerate advances in this field. Full article

(This article belongs to the Special Issue Sensors and IoT in Modern Healthcare Delivery and Applications)

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