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Portable Systems for Diagnostics and Monitoring Applications
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Portable Systems for Diagnostics and Monitoring Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 24734

Special Issue Editors

Prof. Dr. Zaccaria Del Prete

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, SAPIENZA University of Rome, 00184 Rome, Italy
Interests: biomechanics; biomedical materials and tissues; industrial and biomedical measurement
Dr. Emanuele Piuzzi

E-Mail Website
Guest Editor
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy
Interests: permittivity measurement; electrical and electronic instrumentation
Special Issues, Collections and Topics in MDPI journals
Dr. Eduardo Palermo

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, SAPIENZA University of Rome, 00184 Rome, Italy
Interests: experimental mechanics; robotic rehabilitation; biomedical and biomechanical measurements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rise of small and low-cost sensing technologies in recent years has promoted the possibility to realize portable or field deployment of complex measurement systems, applicable in numerous contexts ranging from industry, healthcare, and environment to very specialized fields such as the personal safety sector.

For example, the possibility to use portable and not bulky or even wearable devices allows for monitoring of individual health in non-face-to-face conditions, air and water quality remotely, or the degradation rate of historic buildings and artifacts.  

We invite the submission of original papers dealing with sensors and measurements concerning small portable instruments, emphasizing mechanical and electrical performances and possible new enhanced qualities of the measurement devices as well as specific measurement signal manipulation and data analysis using the most recent digital and computer science techniques.

Technologies and studies of interest include but are not limited to biomedical and health sensor systems, environmental sensors, mechatronics devices, optical sensors, sensor networks, sensor system integration, smart sensor systems, machine learning for sensor data processing.

Prof. Dr. Zaccaria Del Prete
Prof. Dr. Emanuele Piuzzi
Dr. Eduardo Palermo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Portable sensors
  • Wearable sensors
  • Wireless sensors
  • Sensor networks
  • Health/Healthcare monitoring
  • Air and water quality
  • Environmental monitoring
  • Cultural heritage preservation
  • Structural health monitoring

Published Papers (7 papers)

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Research

14 pages, 880 KiB  
Article
Estimating Regional Methane Emission Factors from Energy and Agricultural Sector Sources Using a Portable Measurement System: Case Study of the Denver–Julesburg Basin
by Stuart N. Riddick, Fancy Cheptonui, Kexin Yuan, Mercy Mbua, Rachel Day, Timothy L. Vaughn, Aidan Duggan, Kristine E. Bennett and Daniel J. Zimmerle
Sensors 2022, 22(19), 7410; https://doi.org/10.3390/s22197410 - 29 Sep 2022
Cited by 4 | Viewed by 2163
Abstract
Methane (CH4), a powerful greenhouse gas (GHG), has been identified as a key target for emission reduction in the Paris agreement, but it is not currently clear where efforts should be focused to make the greatest impact. Currently, activity data and [...] Read more.
Methane (CH4), a powerful greenhouse gas (GHG), has been identified as a key target for emission reduction in the Paris agreement, but it is not currently clear where efforts should be focused to make the greatest impact. Currently, activity data and standard emission factors (EF) are used to generate GHG emission inventories. Many of the EFs are globally uniform and do not account for regional variability in industrial or agricultural practices and/or regulation. Regional EFs can be derived from top–down emissions measurements and used to make bespoke regional GHG emission inventories that account for geopolitical and social variability. However, most large-scale top–down approaches campaigns require significant investment. To address this, lower-cost driving surveys (DS) have been identified as a viable alternative to more established methods. DSs can take top–down measurements of many emission sources in a relatively short period of time, albeit with a higher uncertainty. To investigate the use of a portable measurement system, a 2260 km DS was conducted throughout the Denver–Julesburg Basin (DJB). The DJB covers an area of 8000 km2 north of Denver, CO and is densely populated with CH4 emission sources, including oil and gas (O and G) operations, agricultural operations (AGOs), lakes and reservoirs. During the DS, 157 individual CH4 emission sources were detected; 51%, 43% and 4% of sources were AGOs, O and G operations, and natural sources, respectively. Methane emissions from each source were quantified using downwind concentration and meteorological data and AGOs and O and G operations represented nearly all the CH4 emissions in the DJB, accounting for 54% and 37% of the total emission, respectively. Operations with similar emission sources were grouped together and average facility emission estimates were generated. For agricultural sources, emissions from feedlot cattle, dairy cows and sheep were estimated at 5, 31 and 1 g CH4 head−1 h−1, all of which agreed with published values taken from focused measurement campaigns. Similarly, for O and G average emissions for well pads, compressor stations and gas processing plants (0.5, 14 and 110 kg CH4 facility−1 h−1) were in reasonable agreement with emission estimates from intensive measurement campaigns. A comparison of our basin wide O and G emissions to measurements taken a decade ago show a decrease of a factor of three, which can feasibly be explained by changes to O and G regulation over the past 10 years, while emissions from AGOs have remained constant over the same time period. Our data suggest that DSs could be a low-cost alternative to traditional measurement campaigns and used to screen many emission sources within a region to derive representative regionally specific and time-sensitive EFs. The key benefit of the DS is that many regions can be screened and emission reduction targets identified where regional EFs are noticeably larger than the regional, national or global averages. Full article
(This article belongs to the Special Issue Portable Systems for Diagnostics and Monitoring Applications)
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23 pages, 11344 KiB  
Article
Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings
by David Zambrana-Vinaroz, Jose Maria Vicente-Samper and Jose Maria Sabater-Navarro
Sensors 2022, 22(8), 2900; https://doi.org/10.3390/s22082900 - 09 Apr 2022
Cited by 6 | Viewed by 3165
Abstract
Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, [...] Read more.
Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients’ health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages. Full article
(This article belongs to the Special Issue Portable Systems for Diagnostics and Monitoring Applications)
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16 pages, 5335 KiB  
Article
Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment
by Alessandro Bile, Hamed Tari, Andreas Grinde, Francesca Frasca, Anna Maria Siani and Eugenio Fazio
Sensors 2022, 22(2), 615; https://doi.org/10.3390/s22020615 - 13 Jan 2022
Cited by 7 | Viewed by 2243
Abstract
The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based [...] Read more.
The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based on data collected at Rosenborg Castle (Copenhagen), housing the Royal Danish Collection. Specifically, this study applied the NAR (Nonlinear Autoregressive) and NARX (Nonlinear Autoregressive with Exogenous) models to the Rosenborg microclimate time series. Even if the two models were applied to small datasets, they have shown a good adaptive capacity predicting short-time future values. This work explores the use of AI in very short forecasting of microclimate variables in museums as a potential tool for decision-support systems to limit the climate-induced damages of artworks within the scope of their preventive conservation. The proposed model could be a useful support tool for the management of the museums. Full article
(This article belongs to the Special Issue Portable Systems for Diagnostics and Monitoring Applications)
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15 pages, 2081 KiB  
Article
Sensor-Based Indices for the Prediction and Monitoring of Anterior Cruciate Ligament Injury: Reliability Analysis and a Case Study in Basketball
by Luca Molinaro, Juri Taborri, Adriano Santospagnuolo, Mario Vetrano, Maria Chiara Vulpiani and Stefano Rossi
Sensors 2021, 21(16), 5341; https://doi.org/10.3390/s21165341 - 07 Aug 2021
Cited by 2 | Viewed by 2633
Abstract
The possibility of measuring predictive factors to discriminate athletes at higher risk of anterior cruciate ligament (ACL) injury still represents an open research question. We performed an observational study with thirteen female basketball players who performed monopodalic jumps and single-leg squat tests. One [...] Read more.
The possibility of measuring predictive factors to discriminate athletes at higher risk of anterior cruciate ligament (ACL) injury still represents an open research question. We performed an observational study with thirteen female basketball players who performed monopodalic jumps and single-leg squat tests. One of them suffered from an ACL injury after the first test session. Data gathered from twelve participants, who did not suffer from ACL injury, were used for a reliability analysis. Parameters related to leg stability, load absorption capability and leg mobility showed good-to-excellent reliability. Path length, root mean square of the acceleration and leg angle with respect to the vertical axis revealed themselves as possible predictive factors to identify athletes at higher risk. Results confirm that six months after reconstruction represents the correct time for these athletes to return to playing. Furthermore, the training of leg mobility and load absorption capability could allow athletes to reduce the probability of new injuries. Full article
(This article belongs to the Special Issue Portable Systems for Diagnostics and Monitoring Applications)
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16 pages, 2357 KiB  
Article
Atypical Gait Cycles in Parkinson’s Disease
by Marco Ghislieri, Valentina Agostini, Laura Rizzi, Marco Knaflitz and Michele Lanotte
Sensors 2021, 21(15), 5079; https://doi.org/10.3390/s21155079 - 27 Jul 2021
Cited by 6 | Viewed by 2786
Abstract
It is important to find objective biomarkers for evaluating gait in Parkinson’s Disease (PD), especially related to the foot and lower leg segments. Foot-switch signals, analyzed through Statistical Gait Analysis (SGA), allow the foot-floor contact sequence to be characterized during a walking session [...] Read more.
It is important to find objective biomarkers for evaluating gait in Parkinson’s Disease (PD), especially related to the foot and lower leg segments. Foot-switch signals, analyzed through Statistical Gait Analysis (SGA), allow the foot-floor contact sequence to be characterized during a walking session lasting five-minutes, which includes turnings. Gait parameters were compared between 20 PD patients and 20 age-matched controls. PDs showed similar straight-line speed, cadence, and double-support compared to controls, as well as typical gait-phase durations, except for a small decrease in the flat-foot contact duration (−4% of the gait cycle, p = 0.04). However, they showed a significant increase in atypical gait cycles (+42%, p = 0.006), during both walking straight and turning. A forefoot strike, instead of a “normal” heel strike, characterized the large majority of PD’s atypical cycles, whose total percentage was 25.4% on the most-affected and 15.5% on the least-affected side. Moreover, we found a strong correlation between the atypical cycles and the motor clinical score UPDRS-III (r = 0.91, p = 0.002), in the subset of PD patients showing an abnormal number of atypical cycles, while we found a moderate correlation (r = 0.60, p = 0.005), considering the whole PD population. Atypical cycles have proved to be a valid biomarker to quantify subtle gait dysfunctions in PD patients. Full article
(This article belongs to the Special Issue Portable Systems for Diagnostics and Monitoring Applications)
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26 pages, 2897 KiB  
Article
Laboratory Evaluation of Low-Cost Optical Particle Counters for Environmental and Occupational Exposures
by Sinan Sousan, Swastika Regmi and Yoo Min Park
Sensors 2021, 21(12), 4146; https://doi.org/10.3390/s21124146 - 17 Jun 2021
Cited by 36 | Viewed by 5262
Abstract
Low-cost optical particle counters effectively measure particulate matter (PM) mass concentrations once calibrated. Sensor calibration can be established by deriving a linear regression model by performing side-by-side measurements with a reference instrument. However, calibration differences between environmental and occupational settings have not been [...] Read more.
Low-cost optical particle counters effectively measure particulate matter (PM) mass concentrations once calibrated. Sensor calibration can be established by deriving a linear regression model by performing side-by-side measurements with a reference instrument. However, calibration differences between environmental and occupational settings have not been demonstrated. This study evaluated four commercially available, low-cost PM sensors (OPC-N3, SPS30, AirBeam2, and PMS A003) in both settings. The mass concentrations of three aerosols (salt, Arizona road dust, and Poly-alpha-olefin-4 oil) were measured and compared with a reference instrument. OPC-N3 and SPS30 were highly correlated (r = 0.99) with the reference instrument for all aerosol types in environmental settings. In occupational settings, SPS30, AirBeam2, and PMS A003 exhibited high correlation (>0.96), but the OPC-N3 correlation varied (r = 0.88–1.00). Response significantly (p < 0.001) varied between environmental and occupational settings for most particle sizes and aerosol types. Biases varied by particle size and aerosol type. SPS30 and OPC-N3 exhibited low bias for environmental settings, but all of the sensors showed a high bias for occupational settings. For intra-instrumental precision, SPS30 exhibited high precision for salt for both settings compared to the other low-cost sensors and aerosol types. These findings suggest that SPS30 and OPC-N3 can provide a reasonable estimate of PM mass concentrations if calibrated differently for environmental and occupational settings using site-specific calibration factors. Full article
(This article belongs to the Special Issue Portable Systems for Diagnostics and Monitoring Applications)
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14 pages, 3524 KiB  
Article
GeoAir—A Novel Portable, GPS-Enabled, Low-Cost Air-Pollution Sensor: Design Strategies to Facilitate Citizen Science Research and Geospatial Assessments of Personal Exposure
by Yoo Min Park, Sinan Sousan, Dillon Streuber and Kai Zhao
Sensors 2021, 21(11), 3761; https://doi.org/10.3390/s21113761 - 28 May 2021
Cited by 18 | Viewed by 5214
Abstract
The rapid evolution of air sensor technologies has offered enormous opportunities for community-engaged research by enabling citizens to monitor the air quality at any time and location. However, many low-cost portable sensors do not provide sufficient accuracy or are designed only for technically [...] Read more.
The rapid evolution of air sensor technologies has offered enormous opportunities for community-engaged research by enabling citizens to monitor the air quality at any time and location. However, many low-cost portable sensors do not provide sufficient accuracy or are designed only for technically capable individuals by requiring pairing with smartphone applications or other devices to view/store air quality data and collect location data. This paper describes important design considerations for portable devices to ensure effective citizen engagement and reliable data collection for the geospatial analysis of personal exposure. It proposes a new, standalone, portable air monitor, GeoAir, which integrates a particulate matter (PM) sensor, volatile organic compound (VOC) sensor, humidity and temperature sensor, LTE-M and GPS module, Wi-Fi, long-lasting battery, and display screen. The preliminary laboratory test results demonstrate that the PM sensor shows strong performance when compared to a reference instrument. The VOC sensor presents reasonable accuracy, while further assessments with other types of VOC are needed. The field deployment and geo-visualization of the field data illustrate that GeoAir collects fine-grained, georeferenced air pollution data. GeoAir can be used by all citizens regardless of their technical proficiency and is widely applicable in many fields, including environmental justice and health disparity research. Full article
(This article belongs to the Special Issue Portable Systems for Diagnostics and Monitoring Applications)
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