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Advances in Technological Rehabilitation
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Advances in Technological Rehabilitation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 26789

Special Issue Editors

Prof. Dr. Rodolfo Faglia

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, Università degli Studi di Brescia, Brescia, Italy
Interests: applied mechanics; robotics; design engineering; physical rehabilitation; engineering; mechatronics
Dr. Alberto Borboni

E-Mail Website
Co-Guest Editor
Department of Mechanical and Industrial Engineering, Università degli Studi di Brescia, 25121 Brescia, Italy
Interests: applied mechanics; robotics; design engineering; physical rehabilitation; engineering; mechatronics
Special Issues, Collections and Topics in MDPI journals
Dott. Cinzia Amici

E-Mail Website
Co-Guest Editor
Department of Mechanical and Industrial Engineering, Università degli Studi di Brescia, Brescia, Italy
Interests: mechatronics; applied mechanics; robotics; biomechanics; industrial automation; automation & robotics; rehabilitation robotics; open innovation; technology transfer

Special Issue Information

Advances in medical and chirurgical sciences in last decades allowed an increase of life expectance in the presence of different acute and chronical pathologies. Different studies showed that rehabilitation techniques can increase abilities and life expectance more than natural evolution of these pathologies. Furthermore, in different conditions, technological rehabilitation protocols produced better results than non-technological ones, due to the possibility to increase efficiency and efficacy. Technological instruments were adopted to measure the quality of the patient’s outcomes and to drive the therapist in the rehabilitation process to improve the patient’s Activities of Daily Living (ADL) with a standardized and personalized therapeutic protocol. Thus, this Special Issue is devoted to collecting recent advances in technological rehabilitation, with a particular focus on the following disciplines: robotics, exoskeletons, assistive devices, prostheses, virtual reality, measurements, bioengineering, biomechanics, rehabilitation sciences, and healthcare management.

Prof. Rodolfo Faglia
Prof. Alberto Borboni
Dott. Cinzia Amici
Guest Editors

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Keywords

  • Rehabilitation
  • robotics
  • measurement
  • prostheses
  • virtual reality
  • biomechanics
  • bioengineering
  • healthcare management

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Published Papers (6 papers)

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Research

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17 pages, 6003 KiB  
Article
On a Vector towards a Novel Hearing Aid Feature: What Can We Learn from Modern Family, Voice Classification and Deep Learning Algorithms
by William Hodgetts, Qi Song, Xinyue Xiang and Jacqueline Cummine
Appl. Sci. 2021, 11(12), 5659; https://doi.org/10.3390/app11125659 - 18 Jun 2021
Viewed by 1990
Abstract
(1) Background: The application of machine learning techniques in the speech recognition literature has become a large field of study. Here, we aim to (1) expand the available evidence for the use of machine learning techniques for voice classification and (2) discuss the [...] Read more.
(1) Background: The application of machine learning techniques in the speech recognition literature has become a large field of study. Here, we aim to (1) expand the available evidence for the use of machine learning techniques for voice classification and (2) discuss the implications of such approaches towards the development of novel hearing aid features (i.e., voice familiarity detection). To do this, we built and tested a Convolutional Neural Network (CNN) Model for the identification and classification of a series of voices, namely the 10 cast members of the popular television show “Modern Family”. (2) Methods: Representative voice samples were selected from Season 1 of Modern Family (N = 300; 30 samples for each of the classes of the classification in this model, namely Phil, Claire, Hailey, Alex, Luke, Gloria, Jay, Manny, Mitch, Cameron). The audio samples were then cleaned and normalized. Feature extraction was then implemented and used as the input to train a basic CNN model and an advanced CNN model. (3) Results: Accuracy of voice classification for the basic model was 89%. Accuracy of the voice classification for the advanced model was 99%. (4) Conclusions: Greater familiarity with a voice is known to be beneficial for speech recognition. If a hearing aid can eventually be programmed to recognize voices that are familiar or not, perhaps it can also apply familiar voice features to improve hearing performance. Here we discuss how such machine learning, when applied to voice recognition, is a potential technological solution in the coming years. Full article
(This article belongs to the Special Issue Advances in Technological Rehabilitation)
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17 pages, 19815 KiB  
Article
Development of a Wearable Finger Exoskeleton for Rehabilitation
by Carlos Hernández-Santos, Yasser A. Davizón, Alejandro R. Said, Rogelio Soto, L.C. Félix-Herrán and Adriana Vargas-Martínez
Appl. Sci. 2021, 11(9), 4145; https://doi.org/10.3390/app11094145 - 1 May 2021
Cited by 21 | Viewed by 6355
Abstract
This research work shows a new architecture of a novel wearable finger exoskeleton for rehabilitation; the proposed design consists of a one degree of freedom mechanism that generates the flexion and extension movement for the proximal, medial and distal phalange of the fingers [...] Read more.
This research work shows a new architecture of a novel wearable finger exoskeleton for rehabilitation; the proposed design consists of a one degree of freedom mechanism that generates the flexion and extension movement for the proximal, medial and distal phalange of the fingers to assist patients during the rehabilitation process, after neurological trauma, such as a stroke. The anatomy and anthropometric measures for the hand were used to define the design of the mechanism. In the analytic part, the representative equations for the forward and inverse kinematic analysis of the fingers are obtained, also a dynamic analysis is presented. The position and displacement continued for the structural analysis, were developed by following a static analysis, to know the deformation that the structure links show when an external load is applied in the mechanism. As result, a prototype was manufactured with acrylonitrile butadiene styrene (ABS) using an additive manufacturing machine. Full article
(This article belongs to the Special Issue Advances in Technological Rehabilitation)
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20 pages, 48215 KiB  
Article
Configuration Design of an Upper Limb Rehabilitation Robot with a Generalized Shoulder Joint
by Hao Yan, Hongbo Wang, Peng Chen, Jianye Niu, Yuansheng Ning, Shuangshuang Li and Xusheng Wang
Appl. Sci. 2021, 11(5), 2080; https://doi.org/10.3390/app11052080 - 26 Feb 2021
Cited by 9 | Viewed by 4340
Abstract
For stroke patients with upper limb motor dysfunction, rehabilitation training with the help of rehabilitation robots is a social development trend. Existing upper limb rehabilitation robots have difficulty fully fitting the complex motion of the human shoulder joint and have poor human–robot compatibility. [...] Read more.
For stroke patients with upper limb motor dysfunction, rehabilitation training with the help of rehabilitation robots is a social development trend. Existing upper limb rehabilitation robots have difficulty fully fitting the complex motion of the human shoulder joint and have poor human–robot compatibility. In this paper, based on the anatomical structure of the human upper limb, an equivalent mechanism model of the human upper limb is established. The configuration synthesis of the upper limb rehabilitation mechanism was carried out, a variety of shoulder joint man–machine closed-chain Θs and shoulder elbow human–machine closed-chain Θse configuration combinations were synthesized, and the configuration model with compatibility and reduced moment conduction attenuation was selected from them. Two configurations, 2Pa1P3Ra and 5Ra1P, are proposed for the generalized shoulder joint mechanism of the robot. The closed-chain kinematic models of the two configurations are established, and the velocity Jacobian matrix is obtained. Motion performance analysis, condition reciprocal analysis and operability ellipsoid analysis of different configuration design schemes were carried out in different operation planes. The results show that in the normal upper limb posture of the human body, the 5Ra1P configuration of the shoulder joint has better kinematic performance. Finally, on this basis, an upper limb rehabilitation robot prototype with good human–computer compatibility is developed, and its moving space was verified. Full article
(This article belongs to the Special Issue Advances in Technological Rehabilitation)
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18 pages, 6547 KiB  
Article
A 4-DOF Workspace Lower Limb Rehabilitation Robot: Mechanism Design, Human Joint Analysis and Trajectory Planning
by Hongbo Wang, Musong Lin, Zhennan Jin, Hao Yan, Guowei Liu, Shihe Liu and Xinyu Hu
Appl. Sci. 2020, 10(13), 4542; https://doi.org/10.3390/app10134542 - 30 Jun 2020
Cited by 12 | Viewed by 3409
Abstract
Most of currently rehabilitation robots cannot achieve the adduction/abduction (A/A) training of the hip joint and lack the consideration of the patient handling. This paper presents a four degrees of freedom (DOF) spatial workspace lower limb rehabilitation robot, and it could provide flexion/extension [...] Read more.
Most of currently rehabilitation robots cannot achieve the adduction/abduction (A/A) training of the hip joint and lack the consideration of the patient handling. This paper presents a four degrees of freedom (DOF) spatial workspace lower limb rehabilitation robot, and it could provide flexion/extension (F/E) training to three lower limb joints and A/A training to the hip joint. The training method is conducting the patient’s foot to complete the rehabilitation movement, and the patient could directly take training on the wheelchair and avoid frequent patient handling between the wheelchair and the rehabilitation device. Because patients own different joint range of motions (ROM), an analysis method for obtaining human joint motions is proposed to guarantee the patient’s joint safety in this training method. The analysis method is based on a five-bar linkage kinematic model, which includes the human lower limb. The human-robot hybrid kinematic model is analyzed according to the Denavit-Hartenberg (D-H) method, and a variable human-robot workspace based on the user is proposed. Two kinds of trajectory planning methods are introduced. The trajectory planning method and the human joint analysis method are validated through the trajectory tracking experiment of the prototype. Full article
(This article belongs to the Special Issue Advances in Technological Rehabilitation)
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Review

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18 pages, 1610 KiB  
Review
Haptic-Enabled Hand Rehabilitation in Stroke Patients: A Scoping Review
by Mohamed-Amine Choukou, Sophia Mbabaali, Jasem Bani Hani and Carol Cooke
Appl. Sci. 2021, 11(8), 3712; https://doi.org/10.3390/app11083712 - 20 Apr 2021
Cited by 13 | Viewed by 6646
Abstract
There is a plethora of technology-assisted interventions for hand therapy, however, less is known about the effectiveness of these interventions. This scoping review aims to explore studies about technology-assisted interventions targeting hand rehabilitation to identify the most effective interventions. It is expected that [...] Read more.
There is a plethora of technology-assisted interventions for hand therapy, however, less is known about the effectiveness of these interventions. This scoping review aims to explore studies about technology-assisted interventions targeting hand rehabilitation to identify the most effective interventions. It is expected that multifaceted interventions targeting hand rehabilitation are more efficient therapeutic approaches than mono-interventions. The scoping review will aim to map the existing haptic-enabled interventions for upper limb rehabilitation and investigates their effects on motor and functional recovery in patients with stroke. The methodology used in this review is based on the Arksey and O’Malley framework, which includes the following stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating, summarizing, and reporting the results. Results show that using three or four different technologies was more positive than using two technologies (one technology + haptics). In particular, when standardized as a percentage of outcomes, the combination of three technologies showed better results than the combination of haptics with one technology or with three other technologies. To conclude, this study portrayed haptic-enabled rehabilitation approaches that could help therapists decide which technology-enabled hand therapy approach is best suited to their needs. Those seeking to undertake research and development anticipate further opportunities to develop haptic-enabled hand telerehabilitation platforms. Full article
(This article belongs to the Special Issue Advances in Technological Rehabilitation)
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21 pages, 1627 KiB  
Review
Current Status and Consideration of Support/Care Robots for Stand-Up Motion
by Kensuke Nakamura and Norihiko Saga
Appl. Sci. 2021, 11(4), 1711; https://doi.org/10.3390/app11041711 - 14 Feb 2021
Cited by 5 | Viewed by 2749
Abstract
In order to make robots, which are expected to play an active role in the medical and nursing care fields in the future, more practical for use in rehabilitation, it is necessary to evaluate the current status of the design of these robots. [...] Read more.
In order to make robots, which are expected to play an active role in the medical and nursing care fields in the future, more practical for use in rehabilitation, it is necessary to evaluate the current status of the design of these robots. Therefore, this paper aims to investigate the existing literature on standing motion assistance robots developed and reported to date and investigate each existing design technique from the perspectives of “Functions and Effects” and “Assist form and control.” Then, we search and investigate papers written in English on standing motion assistance robots reported from 2008 to 2019 and organize the contents of the relevant papers into their different assistance modes and four categories related to design. As a result, the standing motion assistance robots are classified into three assist modes: partial assistance, total assistance, and both. The assistance forms are roughly divided into two types: a wearable type and a non-wearable type. It is also demonstrated that both the assistance forms adopt the same trends in terms of the control strategy design and system I/O relationships. On the other hand, power equipment tends to be different between the two forms. Full article
(This article belongs to the Special Issue Advances in Technological Rehabilitation)
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