This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including: Affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control and advances in brain–machine interfacing

Puente Méndez, Santiago T.

Torres, Fernando

Úbeda, Andrés

Sensors

Assistance Robotics and Biosensors

Repositorio Institucional de la Universidad de Alicante

sensorsEditorialAssistance Robotics and BiosensorsFernando Torres 1,2,* , Santiago T. Puente 1,2 and Andrés Úbeda 1,21 Department of Physics, System Engineering and Signal Theory, University of Alicante, 03690 Alicante, Spain;santiago.puente@ua.es (S.T.P.); andres.ubeda@ua.es (A.Ú.)2 Computer Science Research Institute, University of Alicante, 03690 Alicante, Spain* Correspondence: fernando.torres@ua.es; Tel.: +34-965-90-9491Received: 10 October 2018; Accepted: 15 October 2018; Published: 17 October 2018Abstract: This Special Issue is focused on breakthrough developments in the field of biosensors andcurrent scientific progress in biomedical signal processing. The papers address innovative solutionsin assistance robotics based on bioelectrical signals, including: Affordable biosensor technology,affordable assistive-robotics devices, new techniques in myoelectric control and advances inbrain–machine interfacing.Keywords: electromyographic (EMG) sensors; electroencephalographic (EEG) sensors; assistancerobotics applications; robotic exoskeletons; robotic prostheses; advanced biomedical signal processing1. IntroductionIn recent years, the use of bioelectrical information to enhance traditional motor-disabilityassistance has experienced significant growth, mostly based on the development and improvementof biosensor technology and the increasing interest in solving accessibility limitations in a morenatural and effective way. For that purpose, control outputs are directly decoded from the user’sbiological information. Biomedical signals, recorded from cortical or muscular activity, are usedto interact with external devices, such as robotics exoskeletons or assistive robotic arms or hands.However, efforts are still needed to make these technologies affordable for end users, as currentbiomedical devices are still mostly present in rehabilitation centers, hospitals and research facilities.2. ContributionsThis Special Issue collected ten outstanding papers covering different aspects of assistance roboticsand biosensors. In the following, a brief summary of the scope and main contributions of each of thesepapers is provided as a teaser for the interested reader.One of the most important issues in assistive robotics is helping people with special needs ordisabilities to adequately perform rehabilitation exercises in the friendliest way. In “A High-LevelControl Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton” [1] the authorsdesigned a high-level control algorithm capable of generating position and torque references fromsurface electromyography signals (sEMG). They applied this algorithm to a shape memory alloy(SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints.In the same field of assistance, the paper “Intelligent Multimodal Framework for HumanAssistive Robotics Based on Computer Vision Algorithms” [2] shows a multimodal interface basedon computer vision, which has been integrated into a robotic system together with other sensorysystems (electrooculography (EOG) and electroencephalography (EEG)). The results were partof an European project, AIDE, whose purpose is to contribute to the improvement of currentassistance technologies.Undoubtedly, rehabilitation tasks require friendly systems and exoskeletons that are at the sametime more precise. In this sense, the improvement of hardware systems is crucial. In the paperSensors 2018, 18, 3502; doi:10.3390/s18103502 www.mdpi.com/journal/sensorsSensors 2018, 18, 3502 2 of 3“ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for PoweredProstheses” [3] the authors propose a novel gait detection algorithm that can predict a full gait cyclediscretized within a 1% interval. In addition, the system provides an opportunity to eliminate detectiondelays for real-time applications.In the case of assistive robotics, another field of great interest is creating equipment andfriendly environments for people with physical movement disabilities. In this context, the authorsof the paper “A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by SurfaceElectromyography” [4] propose an interface that combines computer vision with electromyography,aiming to allow a person with impeded movement to teleoperate a robotic hand. Experiments werecarried out on basic operations of the grasping and shifting of objects.The problem of more reliable myoelectric systems is addressed in the paper “Virtual Sensor ofSurface Electromyography in a New Extensive Fault-Tolerant Classification System” [5]. The authorspropose extending the use of virtual sensors used in other research fields to the myoelectric field.With this, they provide a new, extensive, fault-tolerant classification system to maintain theclassification accuracy after the occurrence of the following contaminants: ECG interference,electrode displacement, movement artifacts, power line interference, and saturation. The time-varyingautoregressive moving average (TVARMA) and time-varying Kalman filter (TVK) models werecompared to define the most robust model for the virtual sensor.In “Effects of tDCS on Real-Time BCI Detection of Pedaling Motor Imagery” [6] the authors soughtto strengthen the cortical excitability over the primary motor cortex (M1) and the cerebro-cerebellarpathway by means of a new transcranial direct current stimulation (tDCS) configuration to detectlower limb motor imagery (MI) in real time using two different cognitive neural states: relaxed andpedaling MI. In this case, the use of software or hardware techniques with the purpose of improvingthe reception of signals was again treated.The use of assistive robotics is justified when it improves the life of people with certain disabilities.In this sense, the use of appropriate signals for each case is of great importance. In “A NovelFeature Optimization for Wearable Human–Computer Interfaces Using Surface ElectromyographySensors” [7], the authors carried out a study of the signals and selection of optimal-feature selectionmade according to a modified entropy criteria (EC) and Fisher discrimination (FD) criteria. The featureselection results were evaluated using four different classifiers, and compared with other conventionalfeature subsets. These experiments validated the feasibility of the proposed real-time wearable HCIsystem and algorithms, providing a potential assistive device interface for persons with disabilities.In the same field of achieving improvements for people with certain disabilities, the paper titled“Evaluating the Influence of Chromatic and Luminance Stimuli on SSVEPs from Behind-the-Earsand Occipital Areas” [8] presents a study of chromatic and luminance stimuli in low-, medium-,and high-frequency stimulation to evoke steady-state visual evoked potential (SSVEP) in thebehind-the-ears area. These findings will aid in the development of more comfortable, accurate andstable BCI with electrodes positioned in the behind-the-ears (hairless) areas.The use of exoskeletons in rehabilitation therapies is increasingly widespread and it is oneof the most promising and expected future lines in the field of assistive robotics. The authors of“Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscle-DrivenAnkle Rehabilitation Robot” [9] propose the improvement of a therapeutic robot for the rehabilitationof ankle injuries. To do this, they proposed a new method of adaptive backstepping sliding modecontrol (ABS-SMC) in order to solve the PM’s nonlinear characteristics during operation and to tacklethe human–robot uncertainties in rehabilitation.The correct adaptation of an exoskeleton to patients with knee problems is dealt with in “KneeImpedance Modulation to Control an Active Orthosis Using Insole Sensors” [10]. In this case,the authors propose a method for online knee impedance modulation that generates variable gainsthrough the gait cycle according to the users’ anthropometric data and gait sub-phases recognizedthrough footswitch signals.Sensors 2018, 18, 3502 3 of 3Acknowledgments: The authors of the submissions have expressed their appreciation to the work ofthe anonymous reviewers and the Sensors editorial team for their cooperation, suggestions and advice.Likewise, the special editors of this Special Issue thank the staff of Sensors for the trust shown and the goodwork done.Conflicts of Interest: The authors declare no conflicts of interest.References1. Copaci, D.; Serrano, D.; Moreno, L.; Blanco, D. A High-Level Control Algorithm Based on sEMG Signallingfor an Elbow Joint SMA Exoskeleton. Sensors 2018, 18, 2522. [CrossRef] [PubMed]2. Ivorra, E.; Ortega, M.; Catalán, J.M.; Ezquerro, S.; Lledó, L.D.; Garcia-Aracil, N.; Alcañiz, M. IntelligentMultimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms. Sensors 2018,18, 2408. [CrossRef] [PubMed]3. Vu, H.T.T.; Gomez, F.; Cherelle, P.; Lefeber, D.; Nowé, A.; Vanderborght, B. ED-FNN: A New Deep LearningAlgorithm to Detect Percentage of the Gait Cycle for Powered Prostheses. Sensors 2018, 18, 2389. [CrossRef][PubMed]4. Úbeda, A.; Zapata-Impata, B.S.; Puente, S.T.; Gil, P.; Candelas, F.; Torres, F. A Vision-Driven CollaborativeRobotic Grasping System Tele-Operated by Surface Electromyography. Sensors 2018, 18, 2366. [CrossRef][PubMed]5. De Moura, K.D.O.; Balbinot, A. Virtual Sensor of Surface Electromyography in a New ExtensiveFault-Tolerant Classification System. Sensors 2018, 18, 1388. [CrossRef] [PubMed]6. Rodriguez-Ugarte, M.D.L.S.; Iáñez, E.; Ortiz-Garcia, M.; Azorín, J.M. Effects of tDCS on Real-Time BCIDetection of Pedaling Motor Imagery. Sensors 2018, 18, 1136. [CrossRef] [PubMed]7. Sun, H.; Zhang, X.; Zhao, Y.; Zhang, Y.; Zhong, X.; Fan, Z. A Novel Feature Optimization for WearableHuman-Computer Interfaces Using Surface Electromyography Sensors. Sensors 2018, 18, 869. [CrossRef][PubMed]8. Floriano, A.; Diez, P.F.; Freire Bastos-Filho, T. Evaluating the Influence of Chromatic and Luminance Stimulion SSVEPs from Behind-the-Ears and Occipital Areas. Sensors 2018, 18, 615. [CrossRef] [PubMed]9. Ai, Q.; Zhu, C.; Zuo, J.; Meng, W.; Liu, Q.; Xie, S.Q.; Yang, M. Disturbance-Estimated Adaptive BacksteppingSliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot. Sensors 2017, 18, 66.[CrossRef] [PubMed]10. Villa-Parra, A.C.; Delisle-Rodriguez, D.; Souza Lima, J.; Frizera-Neto, A.; Bastos, T. Knee ImpedanceModulation to Control an Active Orthosis Using Insole Sensors. Sensors 2017, 17, 2751. [CrossRef] [PubMed]© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

This Special Issue is focused on breakthrough developments in the field of biosensors, as well as on the current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including the following: affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control, and advances in brain–machine interfacing

Fernando Torres (Ed.)

Santiago Puente (Ed.)

Andrés Ubeda (Ed.)

Directory of Open Access Books (DOAB)

This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including: Affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control and advances in brain&ndash;machine interfacing

Assistance Robotics and Biosensors

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