Submit to Biosensors Review for Biosensors Propose a Special Issue

Journal Menu

Journal Browser

Biosensors in Rehabilitation and Assistance Robotics

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Intelligent Biosensors and Bio-Signal Processing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 14251

Share This Special Issue

Special Issue Editors

Dr. Andres Ubeda

E-Mail Website
Guest Editor

HUman Robotics Group, Universitat d'Alacant, Alicante, Spain
Interests: neurorehabilitation; myoelectric control or brain control; human–robot interaction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gabriel J. Garcia

E-Mail Website
Guest Editor

Human Robotics Group, University of Alicante, 03690 Alicante, Spain
Interests: visual servoing; robotics; FPGA; humanoid robots

Prof. Dr. Carlos A. Jara

E-Mail Website
Guest Editor

HUman Robotics Group, Universitat d'Alacant, Alicante, Spain
Interests: design and robot simulation; robotic manipulation; human–robot interaction; assistive and rehabilitation robotics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Vicente Morell

E-Mail Website
Guest Editor

Human Robotics Group, University of Alicante, 03690 Alicante, Spain
Interests: robotics; computer vision; artificial vision; machine learning

Special Issue Information

Dear Colleagues,

Robotic developments in the field of rehabilitation and assistance have seen a significant increase in the last few years. The improvement of biosensing technologies provides robust ways of assessing the user’s motor or cognitive intentions, which, combined with robotic therapies, leads to critical improvements in motor or cognitive function recovery. Recent advances in bioelectrical signal processing and acquisition devices, in computer-vision techniques and machine-learning, or in the kinetic and dynamic analysis of movement have a huge impact on the efficient development of the aforementioned robotic approaches.

This Special Issue is focused on breakthrough developments in the field of biosensors applied to rehabilitation and assistive robotics. Papers should address innovative robotic solutions combined with the acquisition of biomechanical or cognitive information using a variety of techniques including electrophysiology, computer vision or motion tracking. Both review articles and original research papers are encouraged.

Prof. Dr. Andres Ubeda
Prof. Dr. Gabriel J. Garcia
Prof. Dr. Carlos A. Jara
Prof. Dr. Vicente Morell
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. Biosensors is an international peer-reviewed open access monthly 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 2700 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

assistive robotics
rehabilitation robotics
electromyography (EMG)
electroencephalography (EEG)
computer vision
motion tracking
cognitive robotics
prosthetics
exoskeletons

Published Papers (6 papers)

Download All Papers

Editorial

Jump to: Research

2 pages, 173 KiB

Open AccessEditorial

Biosensors in Rehabilitation and Assistance Robotics

by Andres Ubeda, Gabriel J. Garcia, Carlos A. Jara and Vicente Morell

Biosensors 2022, 12(11), 997; https://0-doi-org.brum.beds.ac.uk/10.3390/bios12110997 - 10 Nov 2022

Viewed by 999

Abstract

Robotic developments in the field of rehabilitation and assistance have seen a significant increase in the last few years [...] Full article

(This article belongs to the Special Issue Biosensors in Rehabilitation and Assistance Robotics)

Research

Jump to: Editorial

18 pages, 19534 KiB

Open AccessArticle

Biomechanical Effects of Adding an Ankle Soft Actuation in a Unilateral Exoskeleton

by Sophia Otálora, Felipe Ballen-Moreno, Luis Arciniegas-Mayag, Carlos A. Cifuentes and Marcela Múnera

Biosensors 2022, 12(10), 873; https://0-doi-org.brum.beds.ac.uk/10.3390/bios12100873 - 14 Oct 2022

Cited by 3 | Viewed by 2146

Abstract

Stroke disease leads to a partial or complete disability affecting muscle strength and functional mobility. Early rehabilitation sessions might induce neuroplasticity and restore the affected function or structure of the patients. Robotic rehabilitation minimizes the burden on therapists by providing repetitive and regularly monitored therapies. Commercial exoskeletons have been found to assist hip and knee motion. For instance, unilateral exoskeletons have the potential to become an effective training system for patients with hemiparesis. However, these robotic devices leave the ankle joint unassisted, essential in gait for body propulsion and weight-bearing. This article evaluates the effects of the robotic ankle orthosis T-FLEX during cooperative assistance with the AGoRA unilateral lower-limb exoskeleton (hip and knee actuation). This study involves nine subjects, measuring muscle activity and gait parameters such as stance and swing times. The results showed a reduction in muscle activity in the Biceps Femoris of 50%, Lateral Gastrocnemius of 59% and Tibialis Anterior of 35% when adding T-FLEX to the AGoRA unilateral lower-limb exoskeleton. No differences were found in gait parameters. Nevertheless, stability is preserved when comparing the two legs. Future works should focus on evaluating the devices in ground tests in healthy subjects and pathological patients. Full article

(This article belongs to the Special Issue Biosensors in Rehabilitation and Assistance Robotics)

► Show Figures

Figure 1

16 pages, 780 KiB

Open AccessArticle

EEG Evaluation in a Neuropsychological Intervention Program Based on Virtual Reality in Adults with Parkinson’s Disease

by Daniela Muñoz, Patricio Barria, Carlos A. Cifuentes, Rolando Aguilar, Karim Baleta, José M. Azorín and Marcela Múnera

Biosensors 2022, 12(9), 751; https://0-doi-org.brum.beds.ac.uk/10.3390/bios12090751 - 12 Sep 2022

Cited by 2 | Viewed by 2647

Abstract

Nowadays, several strategies for treating neuropsychologic function loss in Parkinson’s disease (PD) have been proposed, such as physical activity performance and developing games to exercise the mind. However, few studies illustrate the incidence of these therapies in neuronal activity. This work aims to study the feasibility of a virtual reality-based program oriented to the cognitive functions’ rehabilitation of PD patients. For this, the study was divided into intervention with the program, acquisition of signals, data processing, and results analysis. The alpha and beta bands’ power behavior was determined by evaluating the electroencephalography (EEG) signals obtained during the execution of control tests and games of the “Hand Physics Lab” Software, from which five games related to attention, planning, and sequencing, concentration, and coordination were taken. Results showed the characteristic performance of the cerebral bands during resting states and activity states. In addition, it was determined that the beta band increased its activity in all the cerebral lobes in all the tested games (p-value < 0.05). On the contrary, just one game exhibited an adequate performance of the alpha band activity of the temporal and frontal lobes (p-value < 0.02). Furthermore, the visual attention and the capacity to process and interpret the information given by the surroundings was favored during the execution of trials (p-value < 0.05); thus, the efficacy of the virtual reality program to recover cognitive functions was verified. The study highlights implementing new technologies to rehabilitate people with neurodegenerative diseases. Full article

(This article belongs to the Special Issue Biosensors in Rehabilitation and Assistance Robotics)

► Show Figures

Figure 1

24 pages, 1735 KiB

Open AccessArticle

Decoding of Turning Intention during Walking Based on EEG Biomarkers

by Vicente Quiles, Laura Ferrero, Eduardo Iáñez, Mario Ortiz and José M. Azorín

Biosensors 2022, 12(8), 555; https://0-doi-org.brum.beds.ac.uk/10.3390/bios12080555 - 22 Jul 2022

Cited by 1 | Viewed by 1683

Abstract

In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used:

H^{\infty}

filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter

H^{\infty}

. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average. Full article

(This article belongs to the Special Issue Biosensors in Rehabilitation and Assistance Robotics)

► Show Figures

Figure 1

11 pages, 2339 KiB

Open AccessArticle

ARMIA: A Sensorized Arm Wearable for Motor Rehabilitation

by Gabriel J. Garcia, Angel Alepuz, Guillermo Balastegui, Lluis Bernat, Jonathan Mortes, Sheila Sanchez, Esther Vera, Carlos A. Jara, Vicente Morell, Jorge Pomares, Jose L. Ramon and Andres Ubeda

Biosensors 2022, 12(7), 469; https://0-doi-org.brum.beds.ac.uk/10.3390/bios12070469 - 29 Jun 2022

Cited by 7 | Viewed by 2512

Abstract

In this paper, we present ARMIA: a sensorized arm wearable that includes a combination of inertial and sEMG sensors to interact with serious games in telerehabilitation setups. This device reduces the cost of robotic assistance technologies to be affordable for end-users at home and at rehabilitation centers. Hardware and acquisition software specifications are described together with potential applications of ARMIA in real-life rehabilitation scenarios. A detailed comparison with similar medical technologies is provided, with a specific focus on wearable devices and virtual and augmented reality approaches. The potential advantages of the proposed device are also described showing that ARMIA could provide similar, if not better, the effectivity of physical therapy as well as giving the possibility of home-based rehabilitation. Full article

(This article belongs to the Special Issue Biosensors in Rehabilitation and Assistance Robotics)

► Show Figures

Figure 1

15 pages, 5401 KiB

Open AccessArticle

Biomechanical and Physiological Evaluation of a Multi-Joint Exoskeleton with Active-Passive Assistance for Walking

by Wujing Cao, Zhewen Zhang, Chunjie Chen, Yong He, Dashuai Wang and Xinyu Wu

Biosensors 2021, 11(10), 393; https://0-doi-org.brum.beds.ac.uk/10.3390/bios11100393 - 15 Oct 2021

Cited by 3 | Viewed by 2803

Abstract

How to improve the walking efficiency while ensuring the wearability is an important issue of lower limb exoskeletons. Active devices can provide greater forces, while the passive devices have advantage in weight. We presented a multi-joint exoskeleton with active hip extension assistance and passive ankle plantarflexion assistance in this work. An admittance controller based on a feedforward model was proposed to track the desired active force of the hip extension. An underfoot clutch mechanism was adapted to realize the passive ankle plantarflexion assistance. To assess the efficacy of the multi-joint exoskeleton in assisting walking, we conducted comprehensive experiments to evaluate the force tracking performance, lower limb muscle activities and metabolic cost. The results demonstrated that: (i) The average tracking error of the peak hip extension assistance force from three subjects was less than 3%. (ii) The reductions of normalized root-mean-square EMG in the lateral soleus, medial soleus and gluteus maximus of eight subjects achieved 15.33%, 11.11%, and 3.74%, respectively. (iii) The average metabolic cost of six subjects was reduced by 10.41% under exoskeleton on (EO) condition comparing to the condition of walking with no exoskeleton (NE). This work proved that the concept of the multi-joint exoskeleton with active-passive assistance can improve the walking efficiency. Full article

(This article belongs to the Special Issue Biosensors in Rehabilitation and Assistance Robotics)

► Show Figures