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Personal Sensor Technologies for Physiological, Physical Activity and Air Pollution Exposure Assessments

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 13978

Special Issue Editors

Prof. Dr. Michael S. Breen

E-Mail Website
Guest Editor
Center for Public Health & Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Interests: development of air pollution exposure models; integrated with novel personal sensor technologies; to improve exposure and risk assessments for individuals in epidemiology studies
Special Issues, Collections and Topics in MDPI journals
Dr. Miyuki Breen

E-Mail Website
Guest Editor
Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Interests: computational modeling; chemical exposure; toxicology; epidemiology
Dr. Vlad Isakov

E-Mail Website
Guest Editor
Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Interests: air quality; modeling; meteorology; inhalation exposure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The widespread use of advanced personal and wearable sensor technologies, including smartphones and watches, are enabling a broad range of applications for researchers and clinicians. These applications include physiological parameter estimation, physical activity monitoring, location and motion tracking, inhaled air pollution dose assessments, and health monitoring. However, the large, multi-dimensional sensor data must be integrated with models and data analysis systems to advance the field of sensor applications.

In this context, this Special Issue aims to connect researchers in the field of quantitative physiology, public health, and environmental engineering for the use of personal and wearable sensors. This issue will provide state-of-the-art method development and applications in the field of sensor-based measurements for supporting monitoring and assessments of physiological parameters, physical activity levels, athletic performance, air pollution exposures, and health outcomes. We will accept full-length research articles and reviews focused on this research topic. Topics of interests include, but are not limited to, the following:

  • Wearable sensors technologies
  • Physiological biometrics
  • Physical activity
  • Motion analysis
  • Microenvironment modeling and monitoring
  • Air pollution exposure assessments
  • Health monitoring

Prof. Dr. Michael S. Breen
Dr. Miyuki Breen
Dr. Vlad Isakov
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

  • Wearable sensors technologies 
  • Physiological biometrics 
  • Physical activity 
  • Motion analysis 
  • Microenvironment modeling and monitoring 
  • Air pollution exposure assessments 
  • Health monitoring

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Published Papers (5 papers)

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Research

11 pages, 2911 KiB  
Article
The Relationship between Indoor and Outdoor Fine Particulate Matter in a High-Rise Building in Chicago Monitored by PurpleAir Sensors
by Megan M. Wenner, Anna Ries-Roncalli, Mena C. R. Whalen and Ping Jing
Sensors 2024, 24(8), 2493; https://doi.org/10.3390/s24082493 - 12 Apr 2024
Viewed by 1062
Abstract
In urban areas like Chicago, daily life extends above ground level due to the prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 [...] Read more.
In urban areas like Chicago, daily life extends above ground level due to the prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between 8 April and 7 May 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 m away. The results show that outdoor PM2.5 concentrations peak at 14 m height, and then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 a.m. under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings. Full article
(This article belongs to the Special Issue Personal Sensor Technologies for Physiological, Physical Activity and Air Pollution Exposure Assessments)
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16 pages, 3072 KiB  
Article
A Stress Test for Robustness of Photo Response Nonuniformity (Camera Sensor Fingerprint) Identification on Smartphones
by Fernando Martín-Rodríguez, Fernando Isasi-de-Vicente and Mónica Fernández-Barciela
Sensors 2023, 23(7), 3462; https://doi.org/10.3390/s23073462 - 25 Mar 2023
Cited by 2 | Viewed by 2216
Abstract
In the field of forensic imaging, it is important to be able to extract a camera fingerprint from one or a small set of images known to have been taken by the same camera (or image sensor). Note that we are using the [...] Read more.
In the field of forensic imaging, it is important to be able to extract a camera fingerprint from one or a small set of images known to have been taken by the same camera (or image sensor). Note that we are using the word fingerprint because it is a piece of information extracted from images that can be used to identify an individual source camera. This technique is very important for certain security and digital forensic situations. Camera fingerprint is based on a certain kind of random noise present in all image sensors that is due to manufacturing imperfections and is, thus, unique and impossible to avoid. Photo response nonuniformity (PRNU) has become the most widely used method for source camera identification (SCI). In this paper, a set of attacks is designed and applied to a PRNU-based SCI system, and the success of each method is systematically assessed both in the case of still images and in the case of video. An attack method is defined as any processing that minimally alters image quality and is designed to fool PRNU detectors or, in general, any camera fingerprint detector. The success of an attack is assessed as the increment in the error rate of the SCI system. The PRNU-based SCI system was taken from an outstanding reference that is publicly available. Among the results of this work, the following are remarkable: the use of a systematic and extensive procedure to test SCI methods, very thorough testing of PRNU with more than 2000 test images, and the finding of some very effective attacks on PRNU-based SCI. Full article
(This article belongs to the Special Issue Personal Sensor Technologies for Physiological, Physical Activity and Air Pollution Exposure Assessments)
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10 pages, 488 KiB  
Article
Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study
by Stefan R. Wagner, Rasmus R. Gregersen, Line Henriksen, Ellen-Margrethe Hauge and Kresten K. Keller
Sensors 2022, 22(23), 9396; https://doi.org/10.3390/s22239396 - 2 Dec 2022
Cited by 3 | Viewed by 2540
Abstract
Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The [...] Read more.
Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge. Full article
(This article belongs to the Special Issue Personal Sensor Technologies for Physiological, Physical Activity and Air Pollution Exposure Assessments)
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10 pages, 2233 KiB  
Communication
Using Inertial and Physiological Sensors to Investigate the Effects of a High-Intensity Interval Training and Plyometric Program on the Performance of Young Judokas
by Adrián Mañas-Paris, José M. Muyor and José M. Oliva-Lozano
Sensors 2022, 22(22), 8759; https://doi.org/10.3390/s22228759 - 12 Nov 2022
Cited by 2 | Viewed by 2862
Abstract
The use of inertial and physiological sensors in a sport such as judo is scarce to date. The information provided by these sensors would allow practitioners to have a better understanding of sports performance, which is necessary for an accurate training prescription. The [...] Read more.
The use of inertial and physiological sensors in a sport such as judo is scarce to date. The information provided by these sensors would allow practitioners to have a better understanding of sports performance, which is necessary for an accurate training prescription. The purpose of this study was to use inertial and physiological sensors in order to investigate the effect of a plyometric and high-intensity interval training (HIIT) training program on Special Judo Fitness Test (SJFT) performance and speed of execution of throws in young judokas. A total of 32 participants were divided into two groups: experimental and control. The intervention consisted of six sessions with a duration of 60 min for 3 weeks. Physiological sensors collected heart rate data to assess the Special Judo Fitness Test, and inertial sensors collected angular velocity. The results show a significant decrease in the SJFT index (Score pre: 22.27 ± 2.73; Score post: 19.65 ± 1.70; p ≤ 0.05; d = 0.61) and a significant increase in the angular velocity of the X-axis (Pre: 320.87 ± 51.15°/s; Post: 356.50 ± 40.47°/s; p ≤ 0.05; d = 0.45) and Y-axis (Pre: 259.40 ± 41.99°/s; Post: 288.02 ± 65.12°/s; p ≤ 0.05; d = 0.31) in the experimental group. In conclusion, this study demonstrates that using inertial and physiological sensors allowed us to analyze the effect that a high-intensity interval training program and plyometrics had on the performance of young judokas. Strength and conditioning coaches should consider these results because including plyometric training and HIIT in judokas’ workout programming can be especially positive for eliciting increases in performance. However, future training interventions should investigate the training adaptations to longer interventions. Full article
(This article belongs to the Special Issue Personal Sensor Technologies for Physiological, Physical Activity and Air Pollution Exposure Assessments)
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13 pages, 2139 KiB  
Article
Integrating Wearable Sensors and Video to Determine Microlocation-Specific Physiologic and Motion Biometrics-Method Development for Competitive Climbing
by Miyuki Breen, Taylor Reed, Hannah M. Breen, Charles T. Osborne and Michael S. Breen
Sensors 2022, 22(16), 6271; https://doi.org/10.3390/s22166271 - 20 Aug 2022
Cited by 7 | Viewed by 3850
Abstract
Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location [...] Read more.
Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location on climbing wall with spatially varying characteristics (e.g., wall inclinations, position of foot/hand holds). However, this response is not well understood due to the limited capabilities of climbing-specific measurement and assessment tools. In this study, we developed a novel method to examine time-resolved sensor-based measurements of multiple personal biometrics at different microlocations (finely spaced positions; MLs) along a climbing route. For the ML-specific biometric system (MLBS), we integrated continuous data from wearable biometric sensors and smartphone-based video during climbing, with a customized visualization and analysis system to determine three physiologic parameters (heart rate, breathing rate, ventilation rate) and one body movement parameter (hip acceleration), which are automatically time-matched to the corresponding video frame to determine ML-specific biometrics. Key features include: (1) biometric sensors that are seamlessly embedded in the fabric of an athletic compression shirt, and do not interfere with climbing performance, (2) climbing video, and (3) an interactive graphical user interface to rapidly visualize and analyze the time-matched biometrics and climbing video, determine timing sequence between the biometrics at key events, and calculate summary statistics. To demonstrate the capabilities of MLBS, we examined the relationship between changes in ML-specific climbing characteristics and changes in the physiologic parameters. Our study demonstrates the ability of MLBS to determine multiple time-resolved biometrics at different MLs, in support of developing and assessing different climbing strategies and training methods to help improve performance. Full article
(This article belongs to the Special Issue Personal Sensor Technologies for Physiological, Physical Activity and Air Pollution Exposure Assessments)
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