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Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 20282

Special Issue Editors

Prof. Dr. Michał Strzelecki

E-Mail Website
Guest Editor
Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Łódź, Poland
Interests: medical imaging; analysis of biomedical images; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adam Wojciechowski

E-Mail
Guest Editor
Institute of Information Technology, Lodz University of Technology, Wolczanska 215, 90-924 Łódź, Poland
Interests: human-computer interaction, biomedical engineering, computer games, machine learning, computer graphics

Special Issue Information

Dear Colleague,

Biomedical signals sensors and physical behavior in remote sensing convey digital data for intelligent analysis and detection and classification of living organism states, behaviors or physiological processes describing their nature or activity. This Special Issue covers multidisciplinary works in the field of biomedical engineering, computer science, human–computer interaction, electronics, and partly also medicine, sport, and psychology, aiming at monitoring and improving living organisms’ quality of life. The Special Issue addresses the application of sensor data processing and analysis, with special interest in, but not limited to, the following list of aspects:

  • Application of artificial intelligence for biomedical signal analysis and classification;
  • Efficiency and efficacy in multimodal data processing, synchronization, and fusion;
  • The problem of a small amount of data in method and system profiling, teaching, adaptation, and calibration;
  • Signal pattern and behavioral pattern recognition in monitoring and diagnosis systems;
  • Human–computer interaction in therapy, training, control, activity monitoring, and rehabilitation systems;
  • Physical and mental health monitoring, assistive living, and wellbeing-oriented systems;
  • Sensing challenges for cognitive and physical aging diagnosis and treatment;
  • Sensing challenges for elderly people and people with disabilities.

Prof. Dr. Michał Strzelecki
Prof. Dr. Adam Wojciechowski
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.

Published Papers (7 papers)

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Research

16 pages, 319 KiB  
Article
Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning
by Maggie Stark, Haikun Huang, Lap-Fai Yu, Rebecca Martin, Ryan McCarthy, Emily Locke, Chelsea Yager, Ahmed Ali Torad, Ahmed Mahmoud Kadry, Mostafa Ali Elwan, Matthew Lee Smith, Dylan Bradley and Ali Boolani
Sensors 2022, 22(9), 3163; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093163 - 20 Apr 2022
Cited by 3 | Viewed by 3646
Abstract
Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious [...] Read more.
Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed. Full article
(This article belongs to the Special Issue Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life)
16 pages, 4132 KiB  
Article
High-Performance Image Acquisition and Processing for Stereoscopic Diagnostic Systems with the Application of Graphical Processing Units
by Piotr Perek, Aleksander Mielczarek and Dariusz Makowski
Sensors 2022, 22(2), 471; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020471 - 08 Jan 2022
Cited by 3 | Viewed by 1679
Abstract
In recent years, cinematography and other digital content creators have been eagerly turning to Three-Dimensional (3D) imaging technology. The creators of movies, games, and augmented reality applications are aware of this technology’s advantages, possibilities, and new means of expression. The development of electronic [...] Read more.
In recent years, cinematography and other digital content creators have been eagerly turning to Three-Dimensional (3D) imaging technology. The creators of movies, games, and augmented reality applications are aware of this technology’s advantages, possibilities, and new means of expression. The development of electronic and IT technologies enables the achievement of a better and better quality of the recorded 3D image and many possibilities for its correction and modification in post-production. However, preparing a correct 3D image that does not cause perception problems for the viewer is still a complex and demanding task. Therefore, planning and then ensuring the correct parameters and quality of the recorded 3D video is essential. Despite better post-production techniques, fixing errors in a captured image can be difficult, time consuming, and sometimes impossible. The detection of errors typical for stereo vision related to the depth of the image (e.g., depth budget violation, stereoscopic window violation) during the recording allows for their correction already on the film set, e.g., by different scene layouts and/or different camera configurations. The paper presents a prototype of an independent, non-invasive diagnostic system that supports the film crew in the process of calibrating stereoscopic cameras, as well as analysing the 3D depth while working on a film set. The system acquires full HD video streams from professional cameras using Serial Digital Interface (SDI), synchronises them, and estimates and analyses the disparity map. Objective depth analysis using computer tools while recording scenes allows stereographers to immediately spot errors in the 3D image, primarily related to the violation of the viewing comfort zone. The paper also describes an efficient method of analysing a 3D video using Graphics Processing Unit (GPU). The main steps of the proposed solution are uncalibrated rectification and disparity map estimation. The algorithms selected and implemented for the needs of this system do not require knowledge of intrinsic and extrinsic camera parameters. Thus, they can be used in non-cooperative environments, such as a film set, where the camera configuration often changes. Both of them are implemented with the use of a GPU to improve the data processing efficiency. The paper presents the evaluation results of the algorithms’ accuracy, as well as the comparison of the performance of two implementations—with and without the GPU acceleration. The application of the described GPU-based method makes the system efficient and easy to use. The system can process a video stream with full HD resolution at a speed of several frames per second. Full article
(This article belongs to the Special Issue Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life)
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14 pages, 33328 KiB  
Article
Orthorectification of Skin Nevi Images by Means of 3D Model of the Human Body
by Piotr M. Szczypiński and Katarzyna Sprawka
Sensors 2021, 21(24), 8367; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248367 - 15 Dec 2021
Cited by 3 | Viewed by 1779
Abstract
Melanoma is the most lethal form of skin cancer, and develops from mutation of pigment-producing cells. As it becomes malignant, it usually grows in size, changes proportions, and develops an irregular border. We introduce a system for early detection of such changes, which [...] Read more.
Melanoma is the most lethal form of skin cancer, and develops from mutation of pigment-producing cells. As it becomes malignant, it usually grows in size, changes proportions, and develops an irregular border. We introduce a system for early detection of such changes, which enables whole-body screening, especially useful in patients with atypical mole syndrome. The paper proposes a procedure to build a 3D model of the patient, relate the high-resolution skin images with the model, and orthorectify these images to enable detection of size and shape changes in nevi. The novelty is in the application of image encoding indices and barycentric coordinates of the mesh triangles. The proposed procedure was validated with a set of markers of a specified geometry. The markers were attached to the body of a volunteer and analyzed by the system. The results of quantitative comparison of original and corrected images confirm that the orthorectification allows for more accurate estimation of size and proportions of skin nevi. Full article
(This article belongs to the Special Issue Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life)
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18 pages, 3074 KiB  
Article
Skin Lesion Detection Algorithms in Whole Body Images
by Michał H. Strzelecki, Maria Strąkowska, Michał Kozłowski, Tomasz Urbańczyk, Dorota Wielowieyska-Szybińska and Marcin Kociołek
Sensors 2021, 21(19), 6639; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196639 - 06 Oct 2021
Cited by 15 | Viewed by 3280
Abstract
Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection [...] Read more.
Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection of neoplastic skin lesions based on the analysis of optical images of individual moles. Recently, full-body systems have gained attention because they enable the analysis of the patient’s entire body based on a set of photos. This paper presents a prototype of such a system, focusing mainly on assessing the effectiveness of algorithms developed for the detection and segmentation of lesions. Three detection algorithms (and their fusion) were analyzed, one implementing deep learning methods and two classic approaches, using local brightness distribution and a correlation method. For fusion of algorithms, detection sensitivity = 0.95 and precision = 0.94 were obtained. Moreover, the values of the selected geometric parameters of segmented lesions were calculated and compared for all algorithms. The obtained results showed a high accuracy of the evaluated parameters (error of area estimation <10%), especially for lesions with dimensions greater than 3 mm, which are the most suspected of being neoplastic lesions. Full article
(This article belongs to the Special Issue Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life)
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20 pages, 7315 KiB  
Article
Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
by Haksu Kim
Sensors 2021, 21(12), 3951; https://0-doi-org.brum.beds.ac.uk/10.3390/s21123951 - 08 Jun 2021
Cited by 2 | Viewed by 2285
Abstract
As worldwide vehicle CO2 emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal [...] Read more.
As worldwide vehicle CO2 emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input. Full article
(This article belongs to the Special Issue Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life)
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14 pages, 1353 KiB  
Article
Validity of the Favero Assioma Duo Power Pedal System for Measuring Power Output and Cadence
by Almudena Montalvo-Pérez, Lidia B. Alejo, Pedro L. Valenzuela, Mario Castellanos, Jaime Gil-Cabrera, Eduardo Talavera, Alejandro Lucia and David Barranco-Gil
Sensors 2021, 21(7), 2277; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072277 - 24 Mar 2021
Cited by 12 | Viewed by 4442
Abstract
Cycling power meters enable monitoring external loads and performance changes. We aimed to determine the concurrent validity of the novel Favero Assioma Duo (FAD) pedal power meter compared with the crank-based SRM system (considered as gold standard). Thirty-three well-trained male cyclists were assessed [...] Read more.
Cycling power meters enable monitoring external loads and performance changes. We aimed to determine the concurrent validity of the novel Favero Assioma Duo (FAD) pedal power meter compared with the crank-based SRM system (considered as gold standard). Thirty-three well-trained male cyclists were assessed at different power output (PO) levels (100–500 W and all-out 15-s sprints), pedaling cadences (75–100 rpm) and cycling positions (seating and standing) to compare the FAD device vs. SRM. No significant differences were found between devices for cadence nor for PO during all-out efforts (p > 0.05), although significant but small differences were found for efforts at lower PO values (p < 0.05 for 100–500 W, mean bias 3–8 W). A strong agreement was observed between both devices for mean cadence (ICC > 0.87) and PO values (ICC > 0.81) recorded in essentially all conditions and for peak cadence (ICC > 0.98) and peak PO (ICC > 0.99) during all-out efforts. The coefficient of variation for PO values was consistently lower than 3%. In conclusion, the FAD pedal-based power meter can be considered an overall valid system to record PO and cadence during cycling, although it might present a small bias compared with power meters placed on other locations such as SRM. Full article
(This article belongs to the Special Issue Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life)
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17 pages, 2741 KiB  
Article
Fully Automatic Fall Risk Assessment Based on a Fast Mobility Test
by Wojciech Tylman, Rafał Kotas, Marek Kamiński, Paweł Marciniak, Sebastian Woźniak, Jan Napieralski, Bartosz Sakowicz, Magdalena Janc, Magdalena Józefowicz-Korczyńska and Ewa Zamysłowska-Szmytke
Sensors 2021, 21(4), 1338; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041338 - 13 Feb 2021
Cited by 6 | Viewed by 2289
Abstract
This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data [...] Read more.
This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject’s body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test. Full article
(This article belongs to the Special Issue Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life)
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