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Special Issue "Sensor Applications in Medical Monitoring and Assistive Devices"

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

Deadline for manuscript submissions: closed (15 July 2018).

Special Issue Editors

Dr. Pubudu N. Pathirana
E-Mail Website
Guest Editor
School of Engineering and Technology, Deakin University, Locked Bag 20000, Geelong VIC 3220, Australia
Interests: Human motion capture; wearable sensors; Bio-medical signal processing; Robust and real time estimation
Dr. David J Szmulewicz
E-Mail
Guest Editor
Balance Disorders & Ataxia Service, Royal Victorian Eye and Ear Hospital, University of Melbourne, Australia
Interests: cerebellar and vestibular balance disorers; ataxia metrics
Prof. Malcolm Horne
E-Mail
Guest Editor
Florey Institute of Neurosciences and Mental Health, St Vincent's Hospital Melbourne, University of Melbourne, Australia
Interests: Measurement of Movement; Parkinsons Disease

Special Issue Information

Dear Colleagues,

The combination of rapidly-progressing sensor technology and the wide array of medical specialty areas, provide a myriad of opportunities for biomedical applications. An example of such opportunities is found in the ability to accurately and non-invasively capture and analyze human movement. The kinematics of human motion has direct application in the diagnosis of neurodegenerative disorders. Additionally, there are a dearth of robust biomarkers in the broader area of balance disorders, which can be implemented in the measurement of disease progression or in the quantification of clinical improvement. This latter point comprises a raft of potential applications, which include the ability to gauge the success of rehabilitation programs and in the growing number of clinical treatment trials. Many neurological diseases severely compromise a patient’s ability to independently carry out their activities of daily living. Traditional subjective assessments have been found wanting, in part, because of their lack of fidelity in representing an individual’s true functional limitations.

Ambulatory devices can be used to measure vital human physiological parameters. Traditional examples of such technology include the electrocardiogram, thermometer, and electroencephalogram. These devices are commercially available in ambulatory forms, providing a means of home-based clinical monitoring. The crucial features of these devices include correct positioning on the human body, sensor quality, a relatively low power demand and wireless communication functionality (thus ensuring patient comfort). Physiological monitoring can be utilized in the diagnosis and treatment of a large number of individuals with neurological, cardiac and pulmonary diseases, such as seizures, ataxia, cardiac arrhythmias, hypertension and asthma.

Micro Electronic Mechanical Systems (MEMS) have been utilized for the development of miniaturized sensors for health monitoring systems and can be manufactured at a relatively low cost. Audio capture technology (for speech disorders,) inertial measurement units (IMU) and optico-kinetic sensors (as a means of capturing human movement parameters) are generally considered to be robust and affordable means by which human sensor systems can be developed. Indeed, the wearability and long-term use of sensors systems are vital to their successful uptake in the medical device arena.

Internet of Things (IoT) enabled devices provide tele-rehabilitation and remote therapy opportunities which minimizes patient travel (particularly from regional to urban areas) and the ability to significantly reduce the ever increasing cost of healthcare provision. The security of bio-medical data must of course be maintained throughout any process of health data acquisition, and in processes, which include data mining and machine learning, this must be robustly integrated into the development of any health monitoring or assistive devices. This is vital in providing a durable and seamless end-to-end service. The overarching objective is to enhance the uptake and promote the use of affordable, integrated functional measurement systems in a range of medical applications, including long-term physiological monitoring and the enhancement of patient’s abilities to engage in vital activities of daily living.

The development of sensor systems for monitoring and assistive devices will encompass the following areas:

  • Sensor platforms and biomedical signal and data processing
  • Power and wearability aspects in device design
  • Pattern recognition, data mining and machine learning applied to biomedical system design
  • Feature extraction and quantification of biomedical movements of speech
  • Rehabilitation based devices and innovative technology
  • Computational Modeling and Data Integration
  • Biomedical Text Mining
  • Medical Robotics
  • Health informatics and Clinical Data analytics
  • Biomedical Computing
  • Clinical Data Analysis
  • Wireless physiological monitoring devices and software application development
  • Autonomic context sensing and multi-sensor fusion technology
  • Remote sensing systems for patient assessment and treatment in medical and allied health clinics
  • Real-time monitoring of physical daily life activities, caloric intake, sleep quality, gait, posture and other factors related to personal well-being.
  • Body-sensor networks in biomedical application
  • Healthcare applications based on IoT technologies.
  • Cloud computing
  • Clinical assisted living applications
  • Human gait dynamics and balance metrics

Prof. Pubudu N. Pathirana
Dr. David J Szmulewicz
Prof. Malcolm Horne
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 papers will be 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. Sensors 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 2000 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.

Published Papers (14 papers)

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Research

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Open AccessArticle
Using Inertial Sensors to Quantify Postural Sway and Gait Performance during the Tandem Walking Test
Sensors 2019, 19(4), 751; https://doi.org/10.3390/s19040751 - 13 Feb 2019
Cited by 2
Abstract
Vestibular dysfunction typically manifests as postural instability and gait irregularities, in part due to inaccuracies in processing spatial afference. In this study, we have instrumented the tandem walking test with multiple inertial sensors to easily and precisely investigate novel variables that can distinguish [...] Read more.
Vestibular dysfunction typically manifests as postural instability and gait irregularities, in part due to inaccuracies in processing spatial afference. In this study, we have instrumented the tandem walking test with multiple inertial sensors to easily and precisely investigate novel variables that can distinguish abnormal postural and gait control in patients with unilateral vestibular hypofunction. Ten healthy adults and five patients with unilateral vestibular hypofunction were assessed with the tandem walking test during eyes open and eyes closed conditions. Each subject donned five inertial sensors on the upper body (head, trunk, and pelvis) and lower body (each lateral malleolus). Our results indicate that measuring the degree of balance and gait regularity using five body-worn inertial sensors during the tandem walking test provides a novel quantification of movement that identifies abnormalities in patients with vestibular impairment. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk
Sensors 2018, 18(10), 3219; https://doi.org/10.3390/s18103219 - 24 Sep 2018
Cited by 6
Abstract
With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related [...] Read more.
With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Quantification of Axial Abnormality Due to Cerebellar Ataxia with Inertial Measurements
Sensors 2018, 18(9), 2791; https://doi.org/10.3390/s18092791 - 24 Aug 2018
Cited by 3
Abstract
Cerebellar Ataxia (CA) leads to deficiencies in muscle movement and lack of coordination that is often manifested as gait and balance disabilities. Conventional CA clinical assessments are subjective, cumbersome and provide less insight into the functional capabilities of patients. This cross-sectional study investigates [...] Read more.
Cerebellar Ataxia (CA) leads to deficiencies in muscle movement and lack of coordination that is often manifested as gait and balance disabilities. Conventional CA clinical assessments are subjective, cumbersome and provide less insight into the functional capabilities of patients. This cross-sectional study investigates the use of wearable inertial sensors strategically positioned on the front-chest and upper-back locations during the Romberg and Trunk tests for objective assessment of human postural balance due to CA. The primary aim of this paper is to quantify the performance of postural stability of 34 patients diagnosed with CA and 22 healthy subjects as controls. Several forms of entropy descriptions were considered to uncover characteristics of movements intrinsic to CA. Indeed, correlation with clinical observation is vital in ascertaining the validity of the inertial measurements in addition to capturing unique features of movements not typically observed by the practicing clinician. Both of these aspects form an integral part of the underlying objective assessment scheme. Uncertainty in the velocity contained a significant level of information with respect to truncal instability and, based on an extensive clustering and discrimination analysis, fuzzy entropy was identified as an effective measure in characterising the underlying disability. Front-chest measurements demonstrated a strong correlation with clinical assessments while the upper-back measurements performed better in classifying the two cohorts, inferring that the standard clinical assessments are relatively influenced by the frontal observations. The Romberg test was confirmed to be an effective test of neurological diagnosis as well as a potential candidate for objective assessment resulting in a significant correlation with the clinical assessments. In contrast, the Trunk test is observed to be relatively less informative. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
ICT to Promote Well-Being within Families
Sensors 2018, 18(9), 2760; https://doi.org/10.3390/s18092760 - 22 Aug 2018
Cited by 2
Abstract
Within the Active Living and Well-Being Project (RRP3), funded by the Republic of Slovenia and the European Regional Development Fund Investing in Your Future program, we aim to develop different approaches and prototype solutions to provide ICT solutions for the family in order [...] Read more.
Within the Active Living and Well-Being Project (RRP3), funded by the Republic of Slovenia and the European Regional Development Fund Investing in Your Future program, we aim to develop different approaches and prototype solutions to provide ICT solutions for the family in order to connect its members; communicate; promote quality family time, active life, a health-friendly lifestyle and well-being; and integrate various sensor and user-based data sources into a smart city ecosystem platform. A mixed methodology, combined qualitative and quantitative approaches, was selected to conduct the study. An online survey with a structured questionnaire as well as semi-structured interviews were performed. Through the analysis of the results, we tried to establish a family-centered design approach that would be inclusive as much as possible, creating benefits for all generations in order to develop an interactive prototype solution that would allow us to further test and verify different use-case scenarios. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices
Sensors 2018, 18(9), 2759; https://doi.org/10.3390/s18092759 - 22 Aug 2018
Cited by 6
Abstract
Tele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms. Therefore, [...] Read more.
Tele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms. Therefore, this paper proposes a novel algorithm for estimating knee joint angle using lower limb angular velocity, obtained with only two leg-mounted gyroscopes. This gyroscope only (GO) algorithm calculates knee angle by integrating gyroscope-derived knee angular velocity signal, and thus avoids reliance on noisy accelerometer data. To eliminate drift in gyroscope data, a zero-angle update derived from a characteristic point in the knee angular velocity is applied to every stride. The concurrent validity and construct convergent validity of the GO algorithm was determined with two existing IMU-based algorithms, complementary and Kalman filters, and an optical motion capture system, respectively. Bland–Altman analysis indicated a high-level of agreement between the GO algorithm and other measures of knee angle. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Smart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography
Sensors 2018, 18(8), 2705; https://doi.org/10.3390/s18082705 - 17 Aug 2018
Cited by 2
Abstract
The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of [...] Read more.
The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of the ankle, need to be setup. Currently, calibrations are performed by experts, who base the inputs on subjective observations and experience. In this study, a novel evidence-based tuning method was presented using multi-channel electromyogram data from the residual limb, and a model for muscle activity was built. Tuning using this model requires an exhaustive search over all the possible combinations of parameters, leading to computationally inefficient system. Various data-driven optimization methods were investigated and a modified Nelder–Mead algorithm using a Latin Hypercube Sampling method was introduced to tune the powered prosthetic. The results of the modified Nelder–Mead optimization were compared to the Exhaustive search, Genetic Algorithm, and conventional Nelder–Mead method, and the results showed the feasibility of using the presented method, to objectively calibrate the parameters in a time-efficient way using biological evidence. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
An Associative Memory Approach to Healthcare Monitoring and Decision Making
Sensors 2018, 18(8), 2690; https://doi.org/10.3390/s18082690 - 16 Aug 2018
Cited by 2
Abstract
The rapid proliferation of connectivity, availability of ubiquitous computing, miniaturization of sensors and communication technology, have changed healthcare in all its areas, creating the well-known healthcare paradigm of e-Health. In this paper, an embedded system capable of monitoring, learning and classifying biometric signals [...] Read more.
The rapid proliferation of connectivity, availability of ubiquitous computing, miniaturization of sensors and communication technology, have changed healthcare in all its areas, creating the well-known healthcare paradigm of e-Health. In this paper, an embedded system capable of monitoring, learning and classifying biometric signals is presented. The machine learning model is based on associative memories to predict the presence or absence of coronary artery disease in patients. Classification accuracy, sensitivity and specificity results show that the performance of our proposal exceeds the performance achieved by each of the fifty widely known algorithms against which it was compared. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Assistive Handlebar Based on Tactile Sensors: Control Inputs and Human Factors
Sensors 2018, 18(8), 2471; https://doi.org/10.3390/s18082471 - 30 Jul 2018
Abstract
Tactile sensors can be used to build human-machine interfaces, for instance in isometric joysticks or handlebars. When used as input sensor device for control, questions arise related to the contact with the human, which involve ergonomic aspects. This paper focuses on the example [...] Read more.
Tactile sensors can be used to build human-machine interfaces, for instance in isometric joysticks or handlebars. When used as input sensor device for control, questions arise related to the contact with the human, which involve ergonomic aspects. This paper focuses on the example application of driving a powered wheelchair as attendant. Since other proposals use force and torque sensors as control input variables, this paper explores the relationship between these variables and others obtained from the tactile sensor. For this purpose, a handlebar is instrumented with tactile sensors and a 6-axis force torque sensor. Several experiments are carried out with this handlebar mounted on a wheelchair and also fixed to a table. It is seen that it is possible to obtain variables well correlated with those provided by force and torque sensors. However, it is necessary to contemplate the influence of issues such as the gripping force of the human hand on the sensor or the different kinds of grasps due to different physical constitutions of humans and to the inherent random nature of the grasp. Moreover, it is seen that a first step is necessary where the contact with the hands has to stabilize, and its characteristics and settle time are obtained. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
LifeChair: A Conductive Fabric Sensor-Based Smart Cushion for Actively Shaping Sitting Posture
Sensors 2018, 18(7), 2261; https://doi.org/10.3390/s18072261 - 13 Jul 2018
Cited by 5
Abstract
The LifeChair is a smart cushion that provides vibrotactile feedback by actively sensing and classifying sitting postures to encourage upright posture and reduce slouching. The key component of the LifeChair is our novel conductive fabric pressure sensing array. Fabric sensors have been explored [...] Read more.
The LifeChair is a smart cushion that provides vibrotactile feedback by actively sensing and classifying sitting postures to encourage upright posture and reduce slouching. The key component of the LifeChair is our novel conductive fabric pressure sensing array. Fabric sensors have been explored in the past, but a full sensing solution for embedded real world use has not been proposed. We have designed our system with commercial use in mind, and as a result, it has a high focus on manufacturability, cost-effectiveness and adaptiveness. We demonstrate the performance of our fabric sensing system by installing it into the LifeChair and comparing its posture detection accuracy with our previous study that implemented a conventional flexible printed PCB-sensing system. In this study, it is shown that the LifeChair can detect all 11 postures across 20 participants with an improved average accuracy of 98.1%, and it demonstrates significantly lower variance when interfacing with different users. We also conduct a performance study with 10 participants to evaluate the effectiveness of the LifeChair device in improving upright posture and reducing slouching. Our performance study demonstrates that the LifeChair is effective in encouraging users to sit upright with an increase of 68.1% in time spent seated upright when vibrotactile feedback is activated. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor
Sensors 2018, 18(7), 2260; https://doi.org/10.3390/s18072260 - 13 Jul 2018
Cited by 4
Abstract
Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such [...] Read more.
Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Inverse Piezoresistive Nanocomposite Sensors for Identifying Human Sitting Posture
Sensors 2018, 18(6), 1745; https://doi.org/10.3390/s18061745 - 29 May 2018
Cited by 1
Abstract
Sitting posture is the position in which one holds his/her body upright against gravity while sitting. Poor sitting posture is regarded as an aggravating factor for various diseases. In this paper, we present an inverse piezoresistive nanocomposite sensor, and related deciphering neural network, [...] Read more.
Sitting posture is the position in which one holds his/her body upright against gravity while sitting. Poor sitting posture is regarded as an aggravating factor for various diseases. In this paper, we present an inverse piezoresistive nanocomposite sensor, and related deciphering neural network, as a new tool to identify human sitting postures accurately. As a low power consumption device, the proposed tool has simple structure, and is easy to use. The strain gauge is attached to the back of the user to acquire sitting data. A three-layer BP neural network is employed to distinguish normal sitting posture, slight hunchback and severe hunchback according to the acquired data. Experimental results show that our method is both realizable and effective, achieving 98.75% posture identification accuracy. This successful application of inverse piezoresistive nanocomposite sensors reveals that the method could potentially be used for monitoring of diverse physiological parameters in the future. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor
Sensors 2018, 18(3), 920; https://doi.org/10.3390/s18030920 - 20 Mar 2018
Cited by 4
Abstract
Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI). This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation [...] Read more.
Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI). This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation of cardiopulmonary-signal-and-detection-related abnormal cardiopulmonary events (e.g., tachycardia, bradycardia, tachypnea, bradypnea, and central apnoea) in many possible sleeping postures within varying environmental settings including in total darkness and whether the subject is covered by a blanket or not. The proposed system extracts the signal from the abdominal-thoracic region where cardiopulmonary activity is most pronounced, using a real-time image sequence captured by Kinect v2 sensor. The proposed system shows promising results in any sleep posture, regardless of illumination conditions and unclear ROI even in the presence of a blanket, whilst being reliable, safe, and cost-effective. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Open AccessArticle
Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration
Sensors 2018, 18(2), 467; https://doi.org/10.3390/s18020467 - 05 Feb 2018
Cited by 5
Abstract
Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able [...] Read more.
Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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Review

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Open AccessReview
EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review
Sensors 2018, 18(10), 3342; https://doi.org/10.3390/s18103342 - 07 Oct 2018
Cited by 4
Abstract
Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices [...] Read more.
Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area. Full article
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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