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Sensors and Digital Solutions for Human Health and Health Risk Monitoring

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 22123

Special Issue Editors

Prof. Dr. Mikael Forsman

E-Mail Website
Guest Editor
Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
Interests: ergonomics; human factors; electromyography; inclinometry; biomedical signal processing; ergonomics risk assessment methods
Dr. Farhad Abtahi

E-Mail Website1 Website2
Guest Editor
Department of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
Interests: system analysis; biomedical signal processing; instrumentation; gamification; biofeedback; digital health; ergonomics and occupational healthcare; wearable technologies

Special Issue Information

Dear Colleagues,

Advances in microelectronics have resulted in a burst of compact sensors and wearable systems for motion tracking and measurement of biosignals. In addition to monitoring human movements and physiological conditions, the popularity of the internet of things (IoT) sensors provides improved access to environmental data, physical activity and movement habits. The popularity of such systems with relatively low cost and acceptable usability is opening the door to novel healthcare applications. In particular, sensors, systems, mobile apps for chronic diseases management, digital and remote consultancy, ergonomic risk assessment, work-technique training, athletes’ performance training and virtual coaching are being developed by different research groups and startups.

In this issue, we invite researchers to contribute original research papers or comprehensive reviews to this Special Issue on “Sensors and Digital Solutions for Human Health and Health Risk Monitoring”. Your contributions will help improve and advance methodologies to develop sensors, processes and analyses of biosignals and corresponding health-related and health-risk-related data, and to use artificial intelligence (AI) and machine learning techniques to strengthen and complement traditional health and risk assessment systems.

Prof. Dr. Mikael Forsman
Dr. Farhad Abtahi
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

  • Precision medicine
  • Preventive healthcare
  • Occupational healthcare
  • Prevention of musculoskeletal disorders
  • Ergonomic risk assessment
  • Precision ergonomics
  • Ergonomic work-technique training
  • Physical activity
  • Artificial intelligence
  • Chronic diseases management
  • Wearable sensors
  • IoT sensors

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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38 pages, 5456 KiB  
Article
Online Fall Detection Using Wrist Devices
by João Marques and Plinio Moreno
Sensors 2023, 23(3), 1146; https://doi.org/10.3390/s23031146 - 19 Jan 2023
Cited by 7 | Viewed by 3952
Abstract
More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in [...] Read more.
More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in their mobility level and impacting their overall quality of life. Previous wrist-based datasets revealed limitations such as unrealistic recording set-ups, lack of proper documentation and, most importantly, the absence of elderly people’s movements. Therefore, this work proposes a new wrist-based dataset to tackle this problem. With this dataset, exhaustive research is carried out with the low computational FS-1 feature set (maximum, minimum, mean and variance) with various machine learning methods. This work presents an accelerometer-only fall detector streaming data at 50 Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean distance, with a window size of 9 s. This work had battery and memory limitations in mind. It also developed a learning version that boosts the fall detector’s performance over time, achieving no single false positives or false negatives over four days. Full article
(This article belongs to the Special Issue Sensors and Digital Solutions for Human Health and Health Risk Monitoring)
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16 pages, 3207 KiB  
Article
Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System
by Jannis Holtkötter, Rita Amaral, Rute Almeida, Cristina Jácome, Ricardo Cardoso, Ana Pereira, Mariana Pereira, Ki H. Chon and João Almeida Fonseca
Sensors 2022, 22(8), 2958; https://doi.org/10.3390/s22082958 - 12 Apr 2022
Cited by 6 | Viewed by 3225
Abstract
Long-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools [...] Read more.
Long-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools able to track compliance levels are necessary. A tool to monitor pill intake that can be implemented in mobile health solutions without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the Circular Hough Transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In the counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills needs to be further improved, in particular for the detection of pills with non-oval shapes. Full article
(This article belongs to the Special Issue Sensors and Digital Solutions for Human Health and Health Risk Monitoring)
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10 pages, 1954 KiB  
Article
The Use of Microsensors to Assess the Daily Wear Time of Removable Orthodontic Appliances: A Prospective Cohort Study
by Marek Nahajowski, Joanna Lis and Michał Sarul
Sensors 2022, 22(7), 2435; https://doi.org/10.3390/s22072435 - 22 Mar 2022
Cited by 2 | Viewed by 5196
Abstract
Orthodontic treatment with removable appliances is still common in children and adolescents. However, their effectiveness depends primarily on the patients’ compliance. Currently, it is possible to check the daily wear time (DWT) of the removable appliances using special microsensors. The aim of this [...] Read more.
Orthodontic treatment with removable appliances is still common in children and adolescents. However, their effectiveness depends primarily on the patients’ compliance. Currently, it is possible to check the daily wear time (DWT) of the removable appliances using special microsensors. The aim of this prospective cohort study was to assess the degree of patients’ compliance depending on the type of removable appliance used. In total, 167 patients (87 F, 80 M) were enrolled in the study and were treated with block appliances (Klammt, Twin-Block), Schwarz plates, and block appliances in combination with headgear. All patients were followed up for 6 months with the mean daily wear time checked at followup visits using TheraMon® microsensors fitted in the appliances. It has been shown that the type of appliance influences the patients’ compliance. The DWT for the Twin Block was significantly longer compared to the DWT for the other appliances. Girls have been shown to wear removable appliances better than boys. It has been proven that the majority of patients do not follow the orthodontist’s recommendations, wearing removable appliances for just over half of the recommended time. Microsensors can be used for objective verification of patients’ compliance, which allows for a reliable assessment of the effectiveness of treatment with removable appliances. Full article
(This article belongs to the Special Issue Sensors and Digital Solutions for Human Health and Health Risk Monitoring)
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21 pages, 4141 KiB  
Article
Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach
by Ramon E. Diaz-Ramos, Daniela A. Gomez-Cravioto, Luis A. Trejo, Carlos Figueroa López and Miguel Angel Medina-Pérez
Sensors 2021, 21(24), 8293; https://doi.org/10.3390/s21248293 - 11 Dec 2021
Cited by 4 | Viewed by 4639
Abstract
This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate, peripheral temperature, and skin conductance. We [...] Read more.
This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate, peripheral temperature, and skin conductance. We measured the data with a biofeedback device from 71 individuals subjected to a 10-min psychophysiological stress test. The data exploration revealed that features’ variability among test phases could be observed in a two-dimensional space with Principal Components Analysis (PCA). In this work, we demonstrate that the values of each feature within a phase are well organized in clusters. The new index we propose, Resilience to Stress Index (RSI), is based on this observation. To compute the index, we used non-supervised machine learning methods to calculate the inter-cluster distances, specifically using the following four methods: Euclidean Distance of PCA, Mahalanobis Distance, Cluster Validity Index Distance, and Euclidean Distance of Kernel PCA. While there was no statistically significant difference (p>0.01) among the methods, we recommend using Mahalanobis, since this method provides higher monotonic association with the Resilience in Mexicans (RESI-M) scale. Results are encouraging since we demonstrated that the computation of a reliable RSI is possible. To validate the new index, we undertook two tasks: a comparison of the RSI against the RESI-M, and a Spearman correlation between phases one and five to determine if the behavior is resilient or not. The computation of the RSI of an individual has a broader scope in mind, and it is to understand and to support mental health. The benefits of having a metric that measures resilience to stress are multiple; for instance, to the extent that individuals can track their resilience to stress, they can improve their everyday life. Full article
(This article belongs to the Special Issue Sensors and Digital Solutions for Human Health and Health Risk Monitoring)
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Review

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40 pages, 9490 KiB  
Review
Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies
by Muhammad Ali Shiwani, Timothy J. A. Chico, Fabio Ciravegna and Lyudmila Mihaylova
Sensors 2023, 23(12), 5752; https://doi.org/10.3390/s23125752 - 20 Jun 2023
Cited by 12 | Viewed by 3837
Abstract
Cardiovascular diseases kill 18 million people each year. Currently, a patient’s health is assessed only during clinical visits, which are often infrequent and provide little information on the person’s health during daily life. Advances in mobile health technologies have allowed for the continuous [...] Read more.
Cardiovascular diseases kill 18 million people each year. Currently, a patient’s health is assessed only during clinical visits, which are often infrequent and provide little information on the person’s health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring. Full article
(This article belongs to the Special Issue Sensors and Digital Solutions for Human Health and Health Risk Monitoring)
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