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Assistance Robotics and Biosensors

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 67890

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Special Issue Editors

Prof. Dr. Fernando Torres

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Guest Editor

Automatics, Robotics and Computer Vision Group, Computer Science Research Institute, University of Alicante, 03690 Alicante, Spain
Interests: intelligent robotic manipulation; visual control of robots; robot perception systems; field robots and advanced automation for industry 4.0; artificial vision engineering and e-learning
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Prof. Dr. Santiago T. Puente

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Guest Editor

Automatics, Robotics and Computer Vision Group, Computer Science Research Institute, University of Alicante, 03690 Alicante, Spain
Interests: dexterous grasping; outdoor manipulation; neuro-robotics; myoelectric control; marine robotics; deep learning; production automation and automatic disassembly
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Dr. Andres Ubeda

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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

Special Issue Information

Dear Colleagues,

In recent years, the use of bioelectrical information to enhance traditional motor-disability assistance has experienced a significant growth, mostly based on the development and improvement of biosensor technology and the increasing interest in solving accessibility limitations in a more natural and effective way. For that purpose, control outputs are directly decoded from the user’s biological information. Biomedical signals, recorded from cortical or muscular activity, are used to 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 current biomedical devices are still mostly present in rehabilitation centers, hospitals and research facilities.

This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. Papers should 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. Both review articles and original research papers are solicited.

Prof. Dr. Fernando Torres
Prof. Dr. Santiago Puente
Prof. Dr. Andrés Ubeda
Guest Editors

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Keywords

electromyographic (EMG) sensors
electroencephalographic (EEG) sensors
assistance robotics applications
advanced biomedical signal processing
affordable biomedical devices
robotic exoskeletons
robotic hands
robotic prostheses

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Published Papers (11 papers)

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Editorial

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3 pages, 179 KiB

Open AccessEditorial

Assistance Robotics and Biosensors

by Fernando Torres, Santiago T. Puente and Andrés Úbeda

Sensors 2018, 18(10), 3502; https://doi.org/10.3390/s18103502 - 17 Oct 2018

Cited by 4 | Viewed by 2975

Abstract

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 [...] Read more.

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

Research

Jump to: Editorial

18 pages, 21420 KiB

Open AccessArticle

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

by Dorin Copaci, David Serrano, Luis Moreno and Dolores Blanco

Sensors 2018, 18(8), 2522; https://doi.org/10.3390/s18082522 - 2 Aug 2018

Cited by 36 | Viewed by 5336

Abstract

A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion–extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm–exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient’s intention. Full article

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

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24 pages, 12123 KiB

Open AccessArticle

Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms

by Eugenio Ivorra, Mario Ortega, José M. Catalán, Santiago Ezquerro, Luis Daniel Lledó, Nicolás Garcia-Aracil and Mariano Alcañiz

Sensors 2018, 18(8), 2408; https://doi.org/10.3390/s18082408 - 24 Jul 2018

Cited by 10 | Viewed by 4602

Abstract

Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project. The main contribution of this computer vision part is the integration with the robotic system and with the other sensory systems (electrooculography (EOG) and electroencephalography (EEG)). The technical achievements solved herein are the algorithm for the selection of objects using the gaze, and especially the state-of-the-art algorithm for the efficient detection and pose estimation of textureless objects. These algorithms were tested in real conditions, and were thoroughly evaluated both qualitatively and quantitatively. The experimental results of the object selection algorithm were excellent (object selection over 90%) in less than 12 s. The detection and pose estimation algorithms evaluated using the LINEMOD database were similar to the state-of-the-art method, and were the most computationally efficient. Full article

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

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19 pages, 2239 KiB

Open AccessArticle

ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses

by Huong Thi Thu Vu, Felipe Gomez, Pierre Cherelle, Dirk Lefeber, Ann Nowé and Bram Vanderborght

Sensors 2018, 18(7), 2389; https://doi.org/10.3390/s18072389 - 23 Jul 2018

Cited by 66 | Viewed by 15728

Abstract

Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection is considered to be of great importance, as achieving high detection accuracy will produce a more precise, stable, and safe rehabilitation device. In this paper, we propose a novel gait percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects’ signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications. Full article

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

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11 pages, 1354 KiB

Open AccessArticle

A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface Electromyography

by Andrés Úbeda, Brayan S. Zapata-Impata, Santiago T. Puente, Pablo Gil, Francisco Candelas and Fernando Torres

Sensors 2018, 18(7), 2366; https://doi.org/10.3390/s18072366 - 20 Jul 2018

Cited by 7 | Viewed by 4025

Abstract

This paper presents a system that combines computer vision and surface electromyography techniques to perform grasping tasks with a robotic hand. In order to achieve a reliable grasping action, the vision-driven system is used to compute pre-grasping poses of the robotic system based on the analysis of tridimensional object features. Then, the human operator can correct the pre-grasping pose of the robot using surface electromyographic signals from the forearm during wrist flexion and extension. Weak wrist flexions and extensions allow a fine adjustment of the robotic system to grasp the object and finally, when the operator considers that the grasping position is optimal, a strong flexion is performed to initiate the grasping of the object. The system has been tested with several subjects to check its performance showing a grasping accuracy of around 95% of the attempted grasps which increases in more than a 13% the grasping accuracy of previous experiments in which electromyographic control was not implemented. Full article

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

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20 pages, 3856 KiB

Open AccessArticle

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

by Karina De O. A. De Moura and Alexandre Balbinot

Sensors 2018, 18(5), 1388; https://doi.org/10.3390/s18051388 - 1 May 2018

Cited by 17 | Viewed by 4184

Abstract

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior. Full article

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

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14 pages, 18098 KiB

Open AccessArticle

Effects of tDCS on Real-Time BCI Detection of Pedaling Motor Imagery

by Maria De la Soledad Rodriguez-Ugarte, Eduardo Iáñez, Mario Ortiz-Garcia and José M. Azorín

Sensors 2018, 18(4), 1136; https://doi.org/10.3390/s18041136 - 8 Apr 2018

Cited by 20 | Viewed by 5672

Abstract

The purpose of this work is to strengthen the cortical excitability over the primary motor cortex (M1) and the cerebro-cerebellar pathway by means of a new transcranial direct current stimulation (tDCS) configuration to detect lower limb motor imagery (MI) in real time using two different cognitive neural states: relax and pedaling MI. The anode is located over the primary motor cortex in Cz, and the cathode over the right cerebro-cerebellum. The real-time brain–computer interface (BCI) designed is based on finding, for each electrode selected, the power at the particular frequency where the most difference between the two mental tasks is observed. Electroencephalographic (EEG) electrodes are placed over the brain’s premotor area (PM), M1, supplementary motor area (SMA) and primary somatosensory cortex (S1). A single-blind study is carried out, where fourteen healthy subjects are separated into two groups: sham and active tDCS. Each subject is experimented on for five consecutive days. On all days, the results achieved by the active tDCS group were over 60% in real-time detection accuracy, with a five-day average of 62.6%. The sham group eventually reached those levels of accuracy, but it needed three days of training to do so. Full article

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

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31 pages, 7561 KiB

Open AccessArticle

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

by Han Sun, Xiong Zhang, Yacong Zhao, Yu Zhang, Xuefei Zhong and Zhaowen Fan

Sensors 2018, 18(3), 869; https://doi.org/10.3390/s18030869 - 15 Mar 2018

Cited by 22 | Viewed by 5841

Abstract

The novel human-computer interface (HCI) using bioelectrical signals as input is a valuable tool to improve the lives of people with disabilities. In this paper, surface electromyography (sEMG) signals induced by four classes of wrist movements were acquired from four sites on the lower arm with our designed system. Forty-two features were extracted from the time, frequency and time-frequency domains. Optimal channels were determined from single-channel classification performance rank. The optimal-feature selection was according to a modified entropy criteria (EC) and Fisher discrimination (FD) criteria. The feature selection results were evaluated by four different classifiers, and compared with other conventional feature subsets. In online tests, the wearable system acquired real-time sEMG signals. The selected features and trained classifier model were used to control a telecar through four different paradigms in a designed environment with simple obstacles. Performance was evaluated based on travel time (TT) and recognition rate (RR). The results of hardware evaluation verified the feasibility of our acquisition systems, and ensured signal quality. Single-channel analysis results indicated that the channel located on the extensor carpi ulnaris (ECU) performed best with mean classification accuracy of 97.45% for all movement’s pairs. Channels placed on ECU and the extensor carpi radialis (ECR) were selected according to the accuracy rank. Experimental results showed that the proposed FD method was better than other feature selection methods and single-type features. The combination of FD and random forest (RF) performed best in offline analysis, with 96.77% multi-class RR. Online results illustrated that the state-machine paradigm with a 125 ms window had the highest maneuverability and was closest to real-life control. Subjects could accomplish online sessions by three sEMG-based paradigms, with average times of 46.02, 49.06 and 48.08 s, respectively. These experiments validate the feasibility of proposed real-time wearable HCI system and algorithms, providing a potential assistive device interface for persons with disabilities. Full article

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

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12 pages, 730 KiB

Open AccessArticle

Evaluating the Influence of Chromatic and Luminance Stimuli on SSVEPs from Behind-the-Ears and Occipital Areas

by Alan Floriano, Pablo F. Diez and Teodiano Freire Bastos-Filho

Sensors 2018, 18(2), 615; https://doi.org/10.3390/s18020615 - 17 Feb 2018

Cited by 44 | Viewed by 5707

Abstract

This work presents a study of chromatic and luminance stimuli in low-, medium-, and high-frequency stimulation to evoke steady-state visual evoked potential (SSVEP) in the behind-the-ears area. Twelve healthy subjects participated in this study. The electroencephalogram (EEG) was measured on occipital (Oz) and left and right temporal (TP9 and TP10) areas. The SSVEP was evaluated in terms of amplitude, signal-to-noise ratio (SNR), and detection accuracy using power spectral density analysis (PSDA), canonical correlation analysis (CCA), and temporally local multivariate synchronization index (TMSI) methods. It was found that stimuli based on suitable color and luminance elicited stronger SSVEP in the behind-the-ears area, and that the response of the SSVEP was related to the flickering frequency and the color of the stimuli. Thus, green-red stimulus elicited the highest SSVEP in medium-frequency range, and green-blue stimulus elicited the highest SSVEP in high-frequency range, reaching detection accuracy rates higher than 80%. These findings will aid in the development of more comfortable, accurate and stable BCIs with electrodes positioned on the behind-the-ears (hairless) areas. Full article

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

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3263 KiB

Open AccessArticle

Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

by Qingsong Ai, Chengxiang Zhu, Jie Zuo, Wei Meng, Quan Liu, Sheng Q. Xie and Ming Yang

Sensors 2018, 18(1), 66; https://doi.org/10.3390/s18010066 - 28 Dec 2017

Cited by 45 | Viewed by 6182

Abstract

A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions. Full article

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

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2198 KiB

Open AccessArticle

Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors

by Ana Cecilia Villa-Parra, Denis Delisle-Rodriguez, Jessica Souza Lima, Anselmo Frizera-Neto and Teodiano Bastos

Sensors 2017, 17(12), 2751; https://doi.org/10.3390/s17122751 - 28 Nov 2017

Cited by 37 | Viewed by 6513

Abstract

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users’ anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase. Full article

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

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