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Advances in Mobile Sensing for Smart Healthcare
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Advances in Mobile Sensing for Smart Healthcare

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

Deadline for manuscript submissions: 25 February 2025 | Viewed by 13039

Special Issue Editor

Dr. Maria Valero

E-Mail Website
Guest Editor
Department of Information Technology, Kennesaw State University, Marietta, GA 30060, USA
Interests: IoT for smart healthcare; distributed computing; signal processing; wireless sensor networks; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intersection of mobile sensing technologies and healthcare promises a revolution in the way we monitor, diagnose, and manage our health. "Advances in Mobile Sensing for Smart Healthcare" is a Special Issue dedicated to exploring the cutting-edge advancements at this intersection. Mobile devices, from smartphones to wearables, have become indispensable tools in healthcare, enabling real-time data collection, analysis, and personalized interventions. This Special Issue will provide a platform to showcase the latest research, innovations, and breakthroughs that harness mobile sensing for smart healthcare applications.

Potential topics include but for are not limited to:

  • Wearable Health Devices;
  • Remote patient monitoring;
  • Disease management;
  • Preventive healthcare;
  • Telemedicine and Telehealth;
  • mHealth applications;
  • Sensor technologies, data analytics, and the ethical implications of mobile sensing in healthcare;
  • Personalized Health and Wellness;
  • Clinical applications;
  • Healthcare access and equity;
  • Regulatory and Industry Developments.

With an emphasis on the rapid dissemination of cutting-edge developments, this Special Issue serves as a bridge between sensor technology and healthcare applications, underscoring the journal's commitment to advancements in sensor design, technology, applications, and proof of concept.

Dr. Maria Valero
Guest Editor

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
  • remote patient monitoring
  • disease management
  • preventive healthcare
  • telemedicine and telehealth
  • mHealth applications

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Published Papers (7 papers)

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Research

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11 pages, 498 KiB  
Article
Motion Tape Strain During Trunk Muscle Engagement in Young, Healthy Participants
by Spencer Spiegel, Elijah Wyckoff, Jay Barolo, Audrey Lee, Emilia Farcas, Job Godino, Kevin Patrick, Kenneth J. Loh and Sara P. Gombatto
Sensors 2024, 24(21), 6933; https://doi.org/10.3390/s24216933 - 29 Oct 2024
Viewed by 545
Abstract
Background: Motion Tape (MT) is a low-profile, disposable, self-adhesive wearable sensor that measures skin strain. Preliminary studies have validated MT for measuring lower back movement. However, further analysis is needed to determine if MT can be used to measure lower back muscle engagement. [...] Read more.
Background: Motion Tape (MT) is a low-profile, disposable, self-adhesive wearable sensor that measures skin strain. Preliminary studies have validated MT for measuring lower back movement. However, further analysis is needed to determine if MT can be used to measure lower back muscle engagement. The purpose of this study was to measure differences in MT strain between conditions in which the lower back muscles were relaxed versus maximally activated. Methods: Ten participants without low back pain were tested. A matrix of six MTs was placed on the lower back, and strain data were captured under a series of conditions. The first condition was a baseline trial, in which participants lay prone and the muscles of the lower back were relaxed. The subsequent trials were maximum voluntary isometric contractions (MVICs), in which participants did not move, but resisted the examiner force in extension or rotational directions to maximally engage their lower back muscles. The mean MT strain was calculated for each condition. A repeated measures ANOVA was conducted to analyze the effects of conditions (baseline, extension, right rotation, and left rotation) and MT position (1–6) on the MT strain. Post hoc analyses were conducted for significant effects from the overall analysis. Results: The results of the ANOVA revealed a significant main effect of condition (p < 0.001) and a significant interaction effect of sensor and condition (p = 0.01). There were significant differences in MT strain between the baseline condition and the extension and rotation MVIC conditions, respectively, for sensors 4, 5, and 6 (p = 0.01–0.04). The largest differences in MT strain were observed between baseline and rotation conditions for sensors 4, 5, and 6. Conclusions: MT can capture maximal lower back muscle engagement while the trunk remains in a stationary position. Lower sensors are better able to capture muscle engagement than upper sensors. Furthermore, MT captured muscle engagement during rotation conditions better than during extension. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
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15 pages, 4001 KiB  
Article
Validity of Wearable Gait Analysis System for Measuring Lower-Limb Kinematics during Timed Up and Go Test
by Yoshiaki Kataoka, Tomoya Ishida, Satoshi Osuka, Ryo Takeda, Shigeru Tadano, Satoshi Yamada and Harukazu Tohyama
Sensors 2024, 24(19), 6296; https://doi.org/10.3390/s24196296 - 29 Sep 2024
Viewed by 931
Abstract
Few studies have reported on the validity of a sensor-based lower-limb kinematics evaluation during the timed up and go (TUG) test. This study aimed to determine the validity of a wearable gait sensor system for measuring lower-limb kinematics during the TUG test. Ten [...] Read more.
Few studies have reported on the validity of a sensor-based lower-limb kinematics evaluation during the timed up and go (TUG) test. This study aimed to determine the validity of a wearable gait sensor system for measuring lower-limb kinematics during the TUG test. Ten young healthy participants were enrolled, and lower-limb kinematics during the TUG test were assessed using a wearable gait sensor system and a standard optical motion analysis system. The angular velocities of the hip, knee, and ankle joints in sit-to-stand and turn-to-sit phases were significantly correlated between the two motion analysis systems (R = 0.612–0.937). The peak angles and ranges of motion of hip, knee, and ankle joints in the walking-out and walking-in phases were also correlated in both systems (R = 0.528–0.924). These results indicate that the wearable gait sensor system is useful for evaluating lower-limb kinematics not only during gait, but also during the TUG test. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
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20 pages, 272 KiB  
Article
Advancing mHealth Research in Low-Resource Settings: Young Women’s Insights and Implementation Challenges with Wearable Smartwatch Devices in Uganda
by Monica H. Swahn, Kevin B. Gittner, Matthew J. Lyons, Karen Nielsen, Kate Mobley, Rachel Culbreth, Jane Palmier, Natalie E. Johnson, Michael Matte and Anna Nabulya
Sensors 2024, 24(17), 5591; https://doi.org/10.3390/s24175591 - 29 Aug 2024
Cited by 1 | Viewed by 1339
Abstract
In many regions globally, including low-resource settings, there is a growing trend towards using mHealth technology, such as wearable sensors, to enhance health behaviors and outcomes. However, adoption of such devices in research conducted in low-resource settings lags behind use in high-resource areas. [...] Read more.
In many regions globally, including low-resource settings, there is a growing trend towards using mHealth technology, such as wearable sensors, to enhance health behaviors and outcomes. However, adoption of such devices in research conducted in low-resource settings lags behind use in high-resource areas. Moreover, there is a scarcity of research that specifically examines the user experience, readiness for and challenges of integrating wearable sensors into health research and community interventions in low-resource settings specifically. This study summarizes the reactions and experiences of young women (N = 57), ages 18 to 24 years, living in poverty in Kampala, Uganda, who wore Garmin vívoactive 3 smartwatches for five days for a research project. Data collected from the Garmins included participant location, sleep, and heart rate. Through six focus group discussions, we gathered insights about the participants’ experiences and perceptions of the wearable devices. Overall, the wearable devices were met with great interest and enthusiasm by participants. The findings were organized across 10 domains to highlight reactions and experiences pertaining to device settings, challenges encountered with the device, reports of discomfort/comfort, satisfaction, changes in daily activities, changes to sleep, speculative device usage, community reactions, community dynamics and curiosity, and general device comfort. The study sheds light on the introduction of new technology in a low-resource setting and also on the complex interplay between technology and culture in Kampala’s slums. We also learned some insights into how wearable devices and perceptions may influence behaviors and social dynamics. These practical insights are shared to benefit future research and applications by health practitioners and clinicians to advance and enhance the implementation and effectiveness of wearable devices in similar contexts and populations. These insights and user experiences, if incorporated, may enhance device acceptance and data quality for those conducting research in similar settings or seeking to address population-specific needs and health issues. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
17 pages, 3932 KiB  
Article
Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments
by Kee S. Moon, John S. Kang, Sung Q. Lee, Jeff Thompson and Nicholas Satterlee
Sensors 2024, 24(13), 4125; https://doi.org/10.3390/s24134125 - 25 Jun 2024
Viewed by 1579
Abstract
This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for [...] Read more.
This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
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13 pages, 778 KiB  
Article
Smartphone-Based Cognitive Telerehabilitation: A Usability and Feasibility Study Focusing on Mild Cognitive Impairment
by Caterina Formica, Mirjam Bonanno, Chiara Sorbera, Angelo Quartarone, Fabio Mauro Giambò, Angela Marra and Rocco Salvatore Calabrò
Sensors 2024, 24(2), 525; https://doi.org/10.3390/s24020525 - 15 Jan 2024
Cited by 1 | Viewed by 1757
Abstract
The implementation of cognitive health apps in patients with mild cognitive impairment (MCI) is challenging because of their cognitive, age, and other clinical characteristics. In this project, we aimed to evaluate the usability and feasibility of the Rehastart app tested in MCI patients. [...] Read more.
The implementation of cognitive health apps in patients with mild cognitive impairment (MCI) is challenging because of their cognitive, age, and other clinical characteristics. In this project, we aimed to evaluate the usability and feasibility of the Rehastart app tested in MCI patients. Eighteen subjects affected by MCI due to neurodegenerative disorders (including Parkinson’s disease, multiple sclerosis, and amnestic/multidomain MCI) and eighteen healthcare professionals were recruited to this study. Patients were registered on the app by clinicians and they were assigned a protocol of specific cognitive exercises. The recruitment was conducted in the period between March and June 2023. The trial testing of the app consisted of three sessions per week for three weeks, with each session lasting about 30 min. After three weeks, the participants as well as medical personnel were invited to rate the usability and feasibility of the Rehastart mobile application. The instruments employed to evaluate the usability and feasibility of the app were the System Usability Scale (SUS), The Intrinsic Motivation Inventory (IMI) and the Client Satisfaction Questionnaire (CSQ). We did not find statistically significant differences on the SUS (p = 0.07) between healthcare professionals and patients. In addition, we found promising results on subscales of the Intrinsic Motivation Inventory, suggesting high levels of interest and enjoyment when using the Rehastart app. Our study demonstrated that smartphone-based telerehabilitation could be a suitable tool for people with MCI due to neurodegenerative disorders, since the Rehastart app was easy to use and motivating for both patients and healthy people. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
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23 pages, 29967 KiB  
Article
Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine
by Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson and Vamsy P. Chodavarapu
Sensors 2024, 24(1), 268; https://doi.org/10.3390/s24010268 - 2 Jan 2024
Cited by 8 | Viewed by 4782
Abstract
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately [...] Read more.
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients’ privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
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Review

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22 pages, 9945 KiB  
Review
Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation
by Afnan Ahmed Crystal, Maria Valero, Valentina Nino and Katherine H. Ingram
Sensors 2024, 24(13), 4089; https://doi.org/10.3390/s24134089 - 24 Jun 2024
Viewed by 1080
Abstract
Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and [...] Read more.
Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
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