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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Point-of-Care Diagnostics and Devices".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 36834

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Special Issue Editors

Dr. Carlo Ricciardi

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Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: machine learning; statistics; gait analysis; health technology assessment; lean six sigma; biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Francesco Amato

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Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
Interests: mathematical modeling; signal and image processing; radiomics; systems and synthetic biology; physiological control systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mario Cesarelli

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1. Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
2. Lab of Biomedical Signal Processing, Scientific Clinical Institute Maugeri, Telese Terme (BN), Italy
Interests: biomedical engineering; biomedical signals; gait analysis; biomedical imaging; healthcare management

Special Issue Information

Dear Colleagues,

Gait analysis and, more generally, motion analysis have been at the center of scientific research in recent decades for several purposes and in several forms: gait analysis labs have been used to analyze gait patterns, inertial measurement units have been employed to perform analysis in non-hospital settings, and there are also researchers who have designed new wearable systems known as “e-textile”. Indeed, these types of measurements allow clinicians to obtain a quantitative evaluation of motion of patients, supporting them in so-called clinical decision making since there are things that cannot be assessed through medical visits and clinical scales. There is proof in literature that the data acquired by these systems can be used to study diagnosis/prognosis with the implementation of artificial-intelligence-based techniques. Therefore, this Special Issue welcomes all the papers and reviews of literature dealing with motion analysis and diagnosis/prognosis (with and without artificial intelligence solutions).

Dr. Carlo Ricciardi
Prof. Dr. Francesco Amato
Prof. Dr. Mario Cesarelli
Guest Editors

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Keywords

Motion analysis
Gait analysis
Wearable inertial systems
IMUs
Biomechanics
Machine learning
Deep learning
Modeling
Diagnosis
Prognosis

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Published Papers (12 papers)

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23 pages, 5630 KiB

Open AccessArticle

Mathematical Analysis and Motion Capture System Utilization Method for Standardization Evaluation of Tracking Objectivity of 6-DOF Arm Structure for Rehabilitation Training Exercise Therapy Robot

by Jaehwang Seol, Kicheol Yoon and Kwang Gi Kim

Diagnostics 2022, 12(12), 3179; https://doi.org/10.3390/diagnostics12123179 - 15 Dec 2022

Cited by 1 | Viewed by 2194

Abstract

A treatment method for suppressing shoulder pain by reducing the secretion of neurotransmitters in the brain is being studied in compliance with domestic and international standards. A robot is being developed to assist physical therapists in shoulder rehabilitation exercise treatment. The robot used for rehabilitation therapy enables the training of patients to perform rehabilitation exercises repeatedly. However, the biomechanical movement (or motion) of the shoulder joint should be accurately designed to enhance efficiency using a shoulder rehabilitation robot. Furthermore, safely treating patients by accurately evaluating biomechanical movements in compliance with domestic and international standards is a major task. Therefore, an in-depth analysis of shoulder movement is essential for understanding the mechanism of shoulder rehabilitation using robots. This paper proposes a method for analyzing shoulder movements. The rotation angle and range of motion (ROM) of the shoulder joint are measured by attaching a marker to the body and analyzing the inverse kinematics. The first motion is abduction and adduction, and the second is external and internal rotation. The location information of the marker is transmitted to an application software through an infrared camera. For the analysis using an inverse kinematics solution, five males and five females participated in the motion capture experiment. The subjects did not have any disability, and abduction and adduction were repeated 10 times. As a result, ROM of the abduction and adduction were 148° with males and 138.7° in females. Moreover, ROM of the external and internal rotation were 111.2° with males and 106° in females. Because this study enables tracking of the center coordinates of the joint suitably through a motion capture system, inverse kinematics can be accurately calculated. Additionally, a mathematical inverse kinematics equation will utilize follow-up study for designing an upper rehabilitations robot. The proposed method is assessed to be able to contribute to the definition of domestic and international standardization of rehabilitation robots and motion capture for objective evaluation. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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11 pages, 552 KiB

Open AccessArticle

Nonlinear and Linear Measures in the Differentiation of Postural Control in Patients after Total Hip or Knee Replacement and Healthy Controls

by Anna Hadamus, Michalina Błażkiewicz, Aleksandra J. Kowalska, Kamil T. Wydra, Marta Grabowicz, Małgorzata Łukowicz, Dariusz Białoszewski and Wojciech Marczyński

Diagnostics 2022, 12(7), 1595; https://doi.org/10.3390/diagnostics12071595 - 30 Jun 2022

Cited by 11 | Viewed by 1743

Abstract

Primary osteoarthritis treatments such as a total hip (THR) or knee (TKR) replacement lead to postural control changes reinforced by age. Balance tests such as standing with eyes open (EO) or closed (EC) give a possibility to calculate both linear and nonlinear indicators. This study aimed to find the group of linear and/or nonlinear measures that can differentiate healthy people and patients with TKR or THR from each other. This study enrolled 49 THR patients, 53 TKR patients, and 16 healthy controls. The center of pressure (CoP) path length, sample entropy (SampEn), fractal dimension (FD), and the largest Lyapunov exponent (LyE) were calculated separately for AP and ML directions from standing with EO/EC. Cluster analysis did not result in correct allocation to the groups according to all variables. The discriminant model included LyE (ML-EO, ML-EC, AP-EC), FD (AP-EO, ML-EC, AP-EC), CoP-path AP-EC, and SampEn AP-EC. Regression analysis showed that all nonlinear variables depend on the group. The CoP path length is different only in THR patients. It was concluded that standing with EC is a better way to assess the amount of regularity of CoP movement and attention paid to maintain balance. Nonlinear measures better differentiate TKR and THR patients from healthy controls. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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12 pages, 1455 KiB

Open AccessArticle

Can We Use the Oculus Quest VR Headset and Controllers to Reliably Assess Balance Stability?

by Cathy M. Craig, James Stafford, Anastasiia Egorova, Carla McCabe and Mark Matthews

Diagnostics 2022, 12(6), 1409; https://doi.org/10.3390/diagnostics12061409 - 7 Jun 2022

Cited by 8 | Viewed by 4485

Abstract

Balance is the foundation upon which all other motor skills are built. Indeed, many neurological diseases and injuries often present clinically with deficits in balance control. With recent advances in virtual reality (VR) hardware bringing low-cost headsets into the mainstream market, the question remains as to whether this technology could be used in a clinical context to assess balance. We compared the head tracking performance of a low-cost VR headset (Oculus Quest) with a gold standard motion tracking system (Qualisys). We then compared the recorded head sway with the center of pressure (COP) measures collected from a force platform in different stances and different visual field manipulations. Firstly, our analysis showed that there was an excellent correspondence between the two different head movement signals (ICCs > 0.99) with minimal differences in terms of accuracy (<5 mm error). Secondly, we found that head sway mapped onto COP measures more strongly when the participant adopted a Tandem stance during balance assessment. Finally, using the power of virtual reality to manipulate the visual input to the brain, we showed how the Oculus Quest can reliably detect changes in postural control as a result of different types of visual field manipulations. Given the high levels of accuracy of the motion tracking of the Oculus Quest headset, along with the strong relationship with the COP and ability to manipulate the visual field, the Oculus Quest makes an exciting alternative to traditional lab-based balance assessments. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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9 pages, 2503 KiB

Open AccessArticle

Sleep Position Detection with a Wireless Audio-Motion Sensor—A Validation Study

by Wojciech Kukwa, Tomasz Lis, Jonasz Łaba, Ron B. Mitchell and Marcel Młyńczak

Diagnostics 2022, 12(5), 1195; https://doi.org/10.3390/diagnostics12051195 - 11 May 2022

Cited by 5 | Viewed by 2849

Abstract

It is well documented that body position significantly affects breathing indices during sleep in patients with obstructive sleep apnea. They usually worsen while changing from a non-supine to a supine position. Therefore, body position should be an accurately measured and credible parameter in all types of sleep studies. The aim of this study was to specify the accuracy of a neck-based monitoring device (Clebre, Olsztyn, Poland) mounted at the suprasternal notch, in determining a supine and non-supine sleeping position, as well as specific body positions during sleep, in comparison to polysomnography (PSG). A sleep study (PSG along with a neck-based audio-motion sensor) was performed on 89 consecutive patients. The accuracy in determining supine and non-supine positions was

96.9 % \pm 3.9 %

and

97.0 % \pm 3.6 %

, respectively. For lateral positions, the accuracy was

98.6 % \pm 2 %

and

97.4 % \pm 4.5 %

for the right and left side, respectively. The prone position was detected with an accuracy of

97.3 % \pm 5.6 %

. The study showed a high accuracy in detecting supine, as well as other gross positions, during sleep based on a sensor attached to the suprasternal notch, compared to the PSG study. We feel that the suprasternal notch is a promising area for placing wireless sleep study devices. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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11 pages, 1644 KiB

Open AccessArticle

Effect of Plano-Valgus Foot on Lower-Extremity Kinematics and Spatiotemporal Gait Parameters in Children of Age 5–9

by Anna Boryczka-Trefler, Małgorzata Kalinowska, Ewa Szczerbik, Jolanta Stępowska, Anna Łukaszewska and Małgorzata Syczewska

Diagnostics 2022, 12(1), 2; https://doi.org/10.3390/diagnostics12010002 - 21 Dec 2021

Cited by 4 | Viewed by 3021

Abstract

Aim of the study was to see how a definition of the flexible flat foot (FFF) influences the results of gait evaluation in a group of 49 children with clinically established FFF. Objective gait analysis was performed using VICON system with Kistler force platforms. The gait parameters were compared between healthy feet and FFF using two classifications: in static and dynamic conditions. In static condition, the ink footprints with Clarke’s graphics were used for classification, and in dynamic condition, the Arch Index from Emed pedobarograph while walking was used for classification. When the type of the foot was based on Clarke’s graphics, no statistically significant differences were found. When the division was done according to the Arch Index, statistically significant differences between flat feet and normal feet groups were found for normalized gait speed, normalized cadence, pelvic rotation, ankle range of motion in sagittal plane, range of motion of foot progression, and two parameters of a vertical component of the ground reaction force: FZ2 (middle of stance phase) and FZ3 (push-off). Some statically flat feet function well during walking due to dynamic correction mechanisms. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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13 pages, 7420 KiB

Open AccessArticle

Low-Dose PET Imaging of Tumors in Lung and Liver Regions Using Internal Motion Estimation

by Sang-Keun Woo, Byung-Chul Kim, Eun Kyoung Ryu, In Ok Ko and Yong Jin Lee

Diagnostics 2021, 11(11), 2138; https://doi.org/10.3390/diagnostics11112138 - 18 Nov 2021

Viewed by 1684

Abstract

Motion estimation and compensation are necessary for improvement of tumor quantification analysis in positron emission tomography (PET) images. The aim of this study was to propose adaptive PET imaging with internal motion estimation and correction using regional artificial evaluation of tumors injected with low-dose and high-dose radiopharmaceuticals. In order to assess internal motion, molecular sieves imitating tumors were loaded with ¹⁸F and inserted into the lung and liver regions in rats. All models were classified into two groups, based on the injected radiopharmaceutical activity, to compare the effect of tumor intensity. The PET study was performed with injection of F-18 fluorodeoxyglucose (¹⁸F-FDG). Respiratory gating was carried out by external trigger device. Count, signal to noise ratio (SNR), contrast and full width at half maximum (FWHM) were measured in artificial tumors in gated images. Motion correction was executed by affine transformation with estimated internal motion data. Monitoring data were different from estimated motion. Contrast in the low-activity group was 3.57, 4.08 and 6.19, while in the high-activity group it was 10.01, 8.36 and 6.97 for static, 4 bin and 8 bin images, respectively. The results of the lung target in 4 bin and the liver target in 8 bin showed improvement in FWHM and contrast with sufficient SNR. After motion correction, FWHM was improved in both regions (lung: 24.56%, liver: 10.77%). Moreover, with the low dose of radiopharmaceuticals the PET image visualized specific accumulated radiopharmaceutical areas in the liver. Therefore, low activity in PET images should undergo motion correction before quantification analysis using PET data. We could improve quantitative tumor evaluation by considering organ region and tumor intensity. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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17 pages, 5377 KiB

Open AccessArticle

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

by Benyameen Keelson, Luca Buzzatti, Jakub Ceranka, Adrián Gutiérrez, Simone Battista, Thierry Scheerlinck, Gert Van Gompel, Johan De Mey, Erik Cattrysse, Nico Buls and Jef Vandemeulebroucke

Diagnostics 2021, 11(11), 2062; https://doi.org/10.3390/diagnostics11112062 - 7 Nov 2021

Cited by 6 | Viewed by 2689

Abstract

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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13 pages, 1500 KiB

Open AccessArticle

Remote Gait Type Classification System Using Markerless 2D Video

by Pedro Albuquerque, João Pedro Machado, Tanmay Tulsidas Verlekar, Paulo Lobato Correia and Luís Ducla Soares

Diagnostics 2021, 11(10), 1824; https://doi.org/10.3390/diagnostics11101824 - 2 Oct 2021

Cited by 11 | Viewed by 3116

Abstract

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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Review

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14 pages, 1141 KiB

Open AccessReview

Robot-Aided Motion Analysis in Neurorehabilitation: Benefits and Challenges

by Mirjam Bonanno and Rocco Salvatore Calabrò

Diagnostics 2023, 13(23), 3561; https://doi.org/10.3390/diagnostics13233561 - 29 Nov 2023

Cited by 2 | Viewed by 2010

Abstract

In the neurorehabilitation field, robot-aided motion analysis (R-AMA) could be helpful for two main reasons: (1) it allows the registration and monitoring of patients’ motion parameters in a more accurate way than clinical scales (clinical purpose), and (2) the multitude of data produced using R-AMA can be used to build machine learning algorithms, detecting prognostic and predictive factors for better motor outcomes (research purpose). Despite their potential in clinical settings, robotic assessment tools have not gained widespread clinical acceptance. Some barriers remain to their clinical adoption, such as their reliability and validity compared to the existing standardized scales. In this narrative review, we sought to investigate the usefulness of R-AMA systems in patients affected by neurological disorders. We found that the most used R-AMA tools are the Lokomat (an exoskeleton device used for gait and balance rehabilitation) and the Armeo (both Power and Spring, used for the rehabilitation of upper limb impairment). The motion analysis provided by these robotic devices was used to tailor rehabilitation sessions based on the objective quantification of patients’ functional abilities. Spinal cord injury and stroke patients were the most investigated individuals with these common exoskeletons. Research on the use of robotics as an assessment tool should be fostered, taking into account the biomechanical parameters able to predict the accuracy of movements. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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16 pages, 1167 KiB

Open AccessReview

Motion Capture Technologies for Ergonomics: A Systematic Literature Review

by Sani Salisu, Nur Intan Raihana Ruhaiyem, Taiseer Abdalla Elfadil Eisa, Maged Nasser, Faisal Saeed and Hussain A. Younis

Diagnostics 2023, 13(15), 2593; https://doi.org/10.3390/diagnostics13152593 - 4 Aug 2023

Cited by 12 | Viewed by 4595

Abstract

Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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Other

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12 pages, 546 KiB

Open AccessSystematic Review

Postural Control Measurements to Predict Future Motor Impairment in Preterm Infants: A Systematic Review

by Jennifer Bosserman, Sonia Kelkar, Kristen D. LeBlond, Jessica Cassidy and Dana B. McCarty

Diagnostics 2023, 13(22), 3473; https://doi.org/10.3390/diagnostics13223473 - 18 Nov 2023

Cited by 1 | Viewed by 2129

Abstract

Preterm infants are more likely to demonstrate developmental delays than fullterm infants. Postural measurement tools may be effective in measuring the center of pressure (COP) and asymmetry, as well as predicting future motor impairment. The objective of this systematic review was to evaluate existing evidence regarding use of pressure mats or force plates for measuring COP and asymmetry in preterm infants, to determine how measures differ between preterm and fullterm infants and if these tools appropriately predict future motor impairment. The consulted databases included PubMed, Embase, Scopus, and CINAHL. The quality of the literature and the risk of bias were assessed utilizing the ROB2: revised Cochrane risk-of bias tool. Nine manuscripts met the criteria for review. The postural control tools included were FSA UltraThin seat mat, Conformat Pressure-Sensitive mat, Play and Neuro-Developmental Assessment, and standard force plates. Studies demonstrated that all tools were capable of COP assessment in preterm infants and support the association between the observation of reduced postural complexity prior to the observation of midline head control as an indicator of future motor delay. Postural measurement tools provide quick and objective measures of postural control and asymmetry. Based on the degree of impairment, these tools may provide an alternative to standardized assessments that may be taxing to the preterm infant, inaccessible to therapists, or not sensitive enough to capture motor delays. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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28 pages, 654 KiB

Open AccessSystematic Review

The E-Textile for Biomedical Applications: A Systematic Review of Literature

by Giuseppe Cesarelli, Leandro Donisi, Armando Coccia, Federica Amitrano, Giovanni D’Addio and Carlo Ricciardi

Diagnostics 2021, 11(12), 2263; https://doi.org/10.3390/diagnostics11122263 - 3 Dec 2021

Cited by 11 | Viewed by 3371

Abstract

The use of e-textile technologies spread out in the scientific research with several applications in both medical and nonmedical world. In particular, wearable technologies and miniature electronics devices were implemented and tested for medical research purposes. In this paper, a systematic review regarding the use of e-textile for clinical applications was conducted: the Scopus and Pubmed databases were investigate by considering research studies from 2010 to 2020. Overall, 262 papers were found, and 71 of them were included in the systematic review. Of the included studies, 63.4% focused on information and communication technology studies, while the other 36.6% focused on industrial bioengineering applications. Overall, 56.3% of the research was published as an article, while the remainder were conference papers. Papers included in the review were grouped by main aim into cardiological, muscular, physical medicine and orthopaedic, respiratory, and miscellaneous applications. The systematic review showed that there are several types of applications regarding e-textile in medicine and several devices were implemented as well; nevertheless, there is still a lack of validation studies on larger cohorts of subjects since the majority of the research only focuses on developing and testing the new device without considering a further extended validation. Full article

(This article belongs to the Special Issue The Use of Motion Analysis for Diagnostics)

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