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Physical Diagnosis and Rehabilitation Technologies

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 26482

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Special Issue Editors

Prof. Dr. João Ferreira

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Guest Editor

Electrical Engineering Department, Superior Institute of Engineering of Coimbra, 3030-199 Coimbra, Portugal
Interests: humanoid robots; human gait; rehabilitation robotics and artificial intelligence and its application
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tao Liu

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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 Issues, Collections and Topics in MDPI journals

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 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. 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 2400 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 (11 papers)

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Editorial

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2 pages, 171 KiB

Open AccessEditorial

Editorial for the Special Issue on Physical Diagnosis and Rehabilitation Technologies

by Tao Liu and João Paulo Morais Ferreira

Electronics 2022, 11(14), 2247; https://doi.org/10.3390/electronics11142247 - 18 Jul 2022

Viewed by 868

Abstract

Recently, physical diagnosis and human motion analysis have become active research topics in bioelectronics, and they have a broad range of applications, such as pathology detection, rehabilitation, prosthesis design, biometric identification, and humanoid robotic locomotion [...] Full article

(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)

Research

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9 pages, 848 KiB

Open AccessArticle

A Wearable Prefrontal Cortex Oxygen Saturation Measurement System Based on Near Infrared Spectroscopy

by Yi Han, Qian Zhai, Yinkai Yu, Shuoyu Wang and Tao Liu

Electronics 2022, 11(13), 1971; https://doi.org/10.3390/electronics11131971 - 24 Jun 2022

Cited by 1 | Viewed by 1378

Abstract

The measurement of blood oxygen saturation in the prefrontal cortex (PFC), especially during sleep, is of great significance for clinical research. Herein, we present a wearable PFC oxygen saturation measurement system using dual-wavelength functional near-infrared spectroscopy. The system is well designed for user-friendly donning and has the advantages of comfort, convenience, portability, and affordability. The performance of the proposed system is investigated by the calibration and experimental results. The wearable system has demonstrated great potential to conduct the physiological monitoring of PFC, and it can be widely deployed in daily life. Full article

(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)

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15 pages, 4631 KiB

Open AccessArticle

Human Joint Torque Estimation Based on Mechanomyography for Upper Extremity Exosuit

by Yongjun Shi, Wei Dong, Weiqi Lin, Long He, Xinrui Wang, Pengjie Li and Yongzhuo Gao

Electronics 2022, 11(9), 1335; https://doi.org/10.3390/electronics11091335 - 22 Apr 2022

Cited by 9 | Viewed by 1783

Abstract

Human intention recognition belongs to the algorithm basis for exoskeleton robots to generate synergic movements and provide corresponding assistance. In this article, we acquire and analyze the mechanomyography (MMG) to estimate the current joint torque and apply this method to the rehabilitation training research of the upper extremity exosuit. In order to obtain relatively pure biological signals, a MMG processing method based on the Hilbert-Huang Transform (HHT) is proposed to eliminate the mixed noise and motion artifacts. After extracting features and forming the dataset, a random forest regression (RFR) model is designed to build the mapping relationship between MMG and human joint output through offline learning. In addition, an upper extremity exosuit is constructed for multi-joint assistance. Based on the above research, we develop a torque estimation-based control strategy and make it responsible for the intention understanding and motion servo of this customized system. Finally, an actual test verifies the accuracy and reliability of this recognition algorithm, and an efficiency evaluation experiment also proves the feasibility for power assistance. Full article

(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)

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15 pages, 8783 KiB

Open AccessArticle

A Dual-Armed Robotic Puncture System: Design, Implementation and Preliminary Tests

by Yongzhuo Gao, Xiaomin Liu, Xu Zhang, Zhanfeng Zhou, Wenhe Jiang, Lei Chen, Zheng Liu, Dongmei Wu and Wei Dong

Electronics 2022, 11(5), 740; https://doi.org/10.3390/electronics11050740 - 28 Feb 2022

Cited by 4 | Viewed by 2555

Abstract

Traditional renal puncture surgery requires manual operation, which has a poor puncture effect, low surgical success rate, and high incidence of postoperative complications. Robot-assisted puncture surgery can effectively improve the accuracy of punctures, improve the success rate of surgery, and reduce the occurrence of postoperative complications. This paper provides a dual-armed robotic puncture scheme to assist surgeons. The system is divided into an ultrasound scanning arm and a puncture arm. Both robotic arms with a compliant positioning function and master–slave control function are designed, respectively, and the control system is achieved. The puncture arm’s position and posture are decoupled by the wrist RCM mechanism and the arm decoupling mechanism. According to the independent joint control principle, the compliant positioning function is realized based on the single-joint human–computer interactive admittance control. The simulation and tests verify its functions and performance. The differential motion incremental master–slave mapping strategy is used to realize the master–slave control function. The error feedback link is introduced to solve the cumulative error problem in the master–slave control. The dual-armed robotic puncture system prototype is established and animal tests verify the effectiveness. Full article

(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)

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Graphical abstract

16 pages, 26453 KiB

Open AccessArticle

Investigation of Input Modalities Based on a Spatial Region Array for Hand-Gesture Interfaces

by Huanwei Wu, Yi Han, Yanyin Zhou, Xiangliang Zhang, Jibin Yin and Shuoyu Wang

Electronics 2021, 10(24), 3078; https://doi.org/10.3390/electronics10243078 - 10 Dec 2021

Cited by 1 | Viewed by 1785

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 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)

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13 pages, 4353 KiB

Open AccessArticle

The Study of Bending and Twisting Input Modalities in Deformable Interfaces

by Jibin Yin, Shujie Bai, Yi Han, Xiangliang Zhang, Siyang Deng and Shuoyu Wang

Electronics 2021, 10(23), 2991; https://doi.org/10.3390/electronics10232991 - 01 Dec 2021

Cited by 1 | Viewed by 1323

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 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)

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14 pages, 5572 KiB

Open AccessArticle

An Alignment-Free Sensing Module for Noninvasive Radial Artery Blood Pressure Measurement

by Binpeng Zhan, Chao Yang, Fuyuan Xie, Liang Hu, Weiting Liu and Xin Fu

Electronics 2021, 10(23), 2896; https://doi.org/10.3390/electronics10232896 - 23 Nov 2021

Cited by 2 | Viewed by 1985

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 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)

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15 pages, 10761 KiB

Open AccessArticle

A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks

by Jibin Yin, Pengfei Zhao, Yi Zhang, Yi Han and Shuoyu Wang

Electronics 2021, 10(21), 2657; https://doi.org/10.3390/electronics10212657 - 29 Oct 2021

Cited by 3 | Viewed by 1604

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 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)

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10 pages, 1659 KiB

Open AccessArticle

Research on fNIRS Recognition Method of Upper Limb Movement Intention

by Chunguang Li, Yongliang Xu, Liujin He, Yue Zhu, Shaolong Kuang and Lining Sun

Electronics 2021, 10(11), 1239; https://doi.org/10.3390/electronics10111239 - 24 May 2021

Cited by 14 | Viewed by 1713

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 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)

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Review

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14 pages, 834 KiB

Open AccessReview

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

by Yi Han, Chenhao Liu, Bin Zhang, Ning Zhang, Shuoyu Wang, Meimei Han, João P. Ferreira, Tao Liu and Xiufeng Zhang

Electronics 2022, 11(10), 1633; https://doi.org/10.3390/electronics11101633 - 20 May 2022

Cited by 4 | Viewed by 2145

Abstract

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications. Full article

(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)

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16 pages, 1038 KiB

Open AccessReview

A Review on the Rehabilitation Exoskeletons for the Lower Limbs of the Elderly and the Disabled

by Tao Wang, Bin Zhang, Chenhao Liu, Tao Liu, Yi Han, Shuoyu Wang, João P. Ferreira, Wei Dong and Xiufeng Zhang

Electronics 2022, 11(3), 388; https://doi.org/10.3390/electronics11030388 - 27 Jan 2022

Cited by 30 | Viewed by 7631

Abstract

Research on the lower limb exoskeleton for rehabilitation have developed rapidly to meet the need of the aging population. The rehabilitation exoskeleton system is a wearable man–machine integrated mechanical device. In recent years, the vigorous development of exoskeletal technology has brought new ideas to the rehabilitation and medical treatment of patients with motion dysfunction, which is expected to help such people complete their daily physiological activities or even reshape their motion function. The rehabilitation exoskeletons conduct assistance based on detecting intention, control algorithm, and high-performance actuators. In this paper, we review rehabilitation exoskeletons from the aspects of the overall design, driving unit, intention perception, compliant control, and efficiency validation. We discussed the complexity and coupling of the man–machine integration system, and we hope to provide a guideline when designing a rehabilitation exoskeleton system for the lower limbs of elderly and disabled patients. Full article

(This article belongs to the Special Issue Physical Diagnosis and Rehabilitation Technologies)

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