Special Issue "Physical Diagnosis and Rehabilitation Technologies"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 31 May 2022.

Special Issue Editors

Prof. Dr. João Paulo Morais Ferreira
E-Mail Website1 Website2
Guest Editor
Electrical Engineering Department, Superior Institute of Engineering of Coimbra, Portugal
Interests: humanoid robots; human gait; rehabilitation robotics; artificial intelligence and its application
Prof. Dr. Tao Liu
E-Mail Website
Guest Editor
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: rehabilitation robotics; human dynamics and biomedical information; wearable sensors

Special Issue Information

Dear Colleagues,

Human gait analysis is one of the most active research fields in bioelectronics and has a broad range of applications, such as pathology detection, rehabilitation, prosthesis design, biometric identification and humanoid robotic locomotion. Clinical gait analysis methods aim to provide an objective means of quantifying the severity of pathology. A set of pathology-related gait disorders have been identified and can be used to support diagnosis and the development of new assistive technologies.

The aim of this Special Issue is to focus on the intersection of neuroscience and Physical Diagnosis and Rehabilitation Technologies, providing new approaches and technologies for better understanding neural systems’ applications, human movement, and the relationships between them, with a focus on assistive devices that improve life for patients, practicing clinicians, and everyday use.

Submissions to this Special Issue on “Physical Diagnosis and Rehabilitation Technologies” are solicited to represent a snapshot of the field’s development by covering a range of topics that include, but are not limited to, new devices, algorithms, solutions and applications in the following areas:

  • Human gait and balance analysis;
  • The diagnosis of gait disorders;
  • Neuroscience-based physical diagnosis;
  • Assistive instrumentation;
  • Augmentative devices;
  • Assistive robotics devices;
  • Wearable sensors.

Prof. Dr. João Ferreira
Prof. Dr. Tao Liu
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 papers will be 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. Electronics 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 2000 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

  • Human gait and balance analysis
  • Diagnosis of gait disorders
  • Neuroscience-based physical diagnosis
  • Assistive instrumentation
  • Augmentative devices
  • Assistive robotics devices
  • Wearable sensors

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Investigation of Input Modalities Based on a Spatial Region Array for Hand-Gesture Interfaces
Electronics 2021, 10(24), 3078; https://doi.org/10.3390/electronics10243078 - 10 Dec 2021
Viewed by 394
Abstract
To improve the efficiency of computer input, extensive research has been conducted on hand movement in a spatial region. Most of it has focused on the technologies but not the users’ spatial controllability. To assess this, we analyze a users’ common operational area [...] Read more.
To improve the efficiency of computer input, extensive research has been conducted on hand movement in a spatial region. Most of it has focused on the technologies but not the users’ spatial controllability. To assess this, we analyze a users’ common operational area through partitioning, including a layered array of one dimension and a spatial region array of two dimensions. In addition, to determine the difference in spatial controllability between a sighted person and a visually impaired person, we designed two experiments: target selection under a visual and under a non-visual scenario. Furthermore, we explored two factors: the size and the position of the target. Results showed the following: the 5 × 5 target blocks, which were 60.8 mm × 48 mm, could be easily controlled by both the sighted and the visually impaired person; the sighted person could easily select the bottom-right area; however, for the visually impaired person, the easiest selected area was the upper right. Based on the results of the users’ spatial controllability, we propose two interaction techniques (non-visual selection and a spatial gesture recognition technique for surgery) and four spatial partitioning strategies for human-computer interaction designers, which can improve the users spatial controllability. Full article
(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)
Show Figures

Figure 1

Article
The Study of Bending and Twisting Input Modalities in Deformable Interfaces
Electronics 2021, 10(23), 2991; https://doi.org/10.3390/electronics10232991 - 01 Dec 2021
Viewed by 261
Abstract
The deformable input provides users with the ability of physical operation equipment to interact with the system. In order to facilitate further development in flexible display interactive technology, we devised FlexSheet, an input device that can simulate the deformation environment. This paper presents [...] Read more.
The deformable input provides users with the ability of physical operation equipment to interact with the system. In order to facilitate further development in flexible display interactive technology, we devised FlexSheet, an input device that can simulate the deformation environment. This paper presents two forms of deformation input, bending and twisting, with regard to three selection techniques. We conduct a controlled experiment to select discrete targets by combining two input forms and three selection strategies, taking into account the influence of visual feedback. Further, we use the deformation angle to reflect the degree of deformation and map it to the experimental variables. In accordance with the experimental results, we analyze the experimental performance under three evaluation indexes and prove the viability of our selection technology in bending and twisting input modes. Finally, we provide suggestions on the control level in bending and twisting input modes, respectively. Full article
(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)
Show Figures

Figure 1

Article
An Alignment-Free Sensing Module for Noninvasive Radial Artery Blood Pressure Measurement
Electronics 2021, 10(23), 2896; https://doi.org/10.3390/electronics10232896 - 23 Nov 2021
Viewed by 296
Abstract
Sensor–artery alignment has always been a significant problem in arterial tonometry devices and prevents their application to wearable continuous blood pressure (BP) monitoring. Traditional solutions are to use a complex servo system to search for the best measurement position or to use an [...] Read more.
Sensor–artery alignment has always been a significant problem in arterial tonometry devices and prevents their application to wearable continuous blood pressure (BP) monitoring. Traditional solutions are to use a complex servo system to search for the best measurement position or to use an inefficient pressure sensor array. In this study, a novel solid–liquid mixture pressure sensing module is proposed. A flexible film with unique liquid-filled structures greatly reduces the pulse measurement error caused by sensor misplacement. The ideal measuring location was defined as −2.5 to 2.5 mm from the center of the module and the pressure variation was within 5.4%, which is available in the real application. Even at a distance of ±4 mm from the module center, the pressure decays by 23.7%, and its dynamic waveform is maintained. In addition, the sensing module is also endowed with the capability of measuring the pulse wave transmit time as a complementary method for BP measuring. The capability of the developed alignment-free sensing module in BP measurement was been validated. Twenty subjects were selected for the BP measurement experiment, which followed IEEE standards. The experimental results showed that the mean error of SBP is −4.26 mmHg with a standard deviation of 7.0 mmHg, and the mean error of DBP is 2.98 mmHg with a standard deviation of 5.07 mmHg. The device is expected to provide a new solution for wearable continuous BP monitoring. Full article
(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)
Show Figures

Figure 1

Article
A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
Electronics 2021, 10(21), 2657; https://doi.org/10.3390/electronics10212657 - 29 Oct 2021
Viewed by 350
Abstract
The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma [...] Read more.
The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed. First, the proposed WTSS, which uses multiple non-linear regression based on the characteristics of war trauma data and the medical evaluation by an expert panel, performed a standardized assessment of an injury and predicts its trauma consequences. Second, to generate virtual injury, based on the probability of occurrence, the injured parts, injury types, and complications were randomly sampled and combined, and then WTSS was used to assess the consequences of the virtual injury. Third, to evaluate the accuracy of the predicted injury consequences, we built a DNN classifier and then trained it with the generated data and tested it with real data. Finally, we used the Delphi method to filter out unreasonable injuries and improve data rationality. The experimental results verified that the proposed approach surpassed the traditional artificial generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war trauma data augmentation. Full article
(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)
Show Figures

Figure 1

Article
Research on fNIRS Recognition Method of Upper Limb Movement Intention
Electronics 2021, 10(11), 1239; https://doi.org/10.3390/electronics10111239 - 24 May 2021
Viewed by 560
Abstract
This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement [...] Read more.
This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance. Full article
(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)
Show Figures

Figure 1

Back to TopTop