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Sensors and Wearable Assistive Devices
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Sensors and Wearable Assistive Devices

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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 23529

Special Issue Editors

Prof. Dr. Rezaul Begg

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Guest Editor
Leader: Gait and Intelligent Technologies Research Group, Chair: Program in Assistive Technologies Innovation (PATI), Institute for Health and Sport (IHeS), Victoria University, Melbourne, Australia
Interests: assistive devices; exoskeletons; gait biomechanics; machine learning; sensor technology
Special Issues, Collections and Topics in MDPI journals
Dr. Kurt Mudie

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Guest Editor
Defence Science and Technology Group (DST Group), Australia
Interests: Assistive Devices; Exoskeletons; Gait Biomechanics; Load Carriage; Movement Variability
Dr. Dan Billing

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Guest Editor
Defence Science and Technology Group (DST Group), Australia
Interests: Assistive Devices; Exoskeletons; Biomechanics; Load Carriage
Dr. Daniel Lai

E-Mail Website
Guest Editor
Smart Electronics Systems Research Group, College of Engineering and Science, Victoria University, Melbourne, Australia
Interests: new sensing, communication technologies and computational intelligence for applications in health and sports
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Assistive devices are designed to facilitate fundamental human actions, such as walking, grasping, lifting and carrying to improve productivity and reduce fatigue and musculoskeletal injuries. Developments in sensors, actuators, machine learning and related technology provide increasing potential for integrating humans with machines; reflected in a worldwide research effort to develop lightweight, low-cost, powered and unpowered assistive devices, such as exoskeletons. As innovations in artificial intelligence and sensor technology converge, there is also the capacity for designing increasingly intelligent or autonomous assistive technologies across medical, industrial and military applications. We invite submissions to this Special Issue with a focus on technology applications to wearable assistive devices. Topics of interest to our readers may include; sensors, actuators, exoskeletons, human-machine interaction, biomechanical and physiological data modelling and advanced computional methods, such as machine learning.

Prof. Rezaul Begg
Dr. Kurt Mudie
Dr. Dan Billing
Dr. Daniel Lai
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. 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 2600 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 devices 
  • exoskeletons 
  • sensors 
  • machine learning 
  • biomechanical modelling 
  • computational methods 
  • human–machine interaction

Published Papers (5 papers)

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Research

13 pages, 6293 KiB  
Article
Mechanical Design and Kinematic Modeling of a Cable-Driven Arm Exoskeleton Incorporating Inaccurate Human Limb Anthropomorphic Parameters
by Weihai Chen, Zhongyi Li, Xiang Cui, Jianbin Zhang and Shaoping Bai
Sensors 2019, 19(20), 4461; https://doi.org/10.3390/s19204461 - 15 Oct 2019
Cited by 16 | Viewed by 3807
Abstract
Compared with conventional exoskeletons with rigid links, cable-driven upper-limb exoskeletons are light weight and have simple structures. However, cable-driven exoskeletons rely heavily on the human skeletal system for support. Kinematic modeling and control thus becomes very challenging due to inaccurate anthropomorphic parameters and [...] Read more.
Compared with conventional exoskeletons with rigid links, cable-driven upper-limb exoskeletons are light weight and have simple structures. However, cable-driven exoskeletons rely heavily on the human skeletal system for support. Kinematic modeling and control thus becomes very challenging due to inaccurate anthropomorphic parameters and flexible attachments. In this paper, the mechanical design of a cable-driven arm rehabilitation exoskeleton is proposed to accommodate human limbs of different sizes and shapes. A novel arm cuff able to adapt to the contours of human upper limbs is designed. This has given rise to an exoskeleton which reduces the uncertainties caused by instabilities between the exoskeleton and the human arm. A kinematic model of the exoskeleton is further developed by considering the inaccuracies of human-arm skeleton kinematics and attachment errors of the exoskeleton. A parameter identification method is used to improve the accuracy of the kinematic model. The developed kinematic model is finally tested with a primary experiment with an exoskeleton prototype. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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28 pages, 10567 KiB  
Article
Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure
by Myounghoon Shim, Jong In Han, Ho Seon Choi, Seong Min Ha, Jung-Hoon Kim and Yoon Su Baek
Sensors 2019, 19(20), 4418; https://doi.org/10.3390/s19204418 - 12 Oct 2019
Cited by 6 | Viewed by 3951
Abstract
While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points [...] Read more.
While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points between the limb exoskeleton and ground. We used the center of pressure as a contact point on the ground and calculated the location of the contact points on the walking terrain based on kinematic analysis of the exoskeleton. Then, a set of contact points collected from each step during walking was modeled as the plane that represents the surface of the walking terrain through the least-square method. Finally, by comparing the normal vectors of the modeled planes for each step, features of the walking terrain were estimated. We analyzed the estimation accuracy of the proposed method through experiments on level ground, stairs, and a ramp. Classification using the estimated features showed recognition accuracy higher than 95% for all experimental motions. The proposed method approximately analyzed the movement of the exoskeleton on various terrains even though no prior information on the walking terrain was provided. The method can enable exoskeleton systems to actively assist walking in various environments. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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11 pages, 2895 KiB  
Article
Thin Magnetically Permeable Targets for Inductive Sensing: Application to Limb Prosthetics
by Ethan J. Weathersby, Clement J. Gurrey, Jake B. McLean, Benjamin N. Sanders, Brian G. Larsen, Ryan Carter, Joseph L. Garbini and Joan E. Sanders
Sensors 2019, 19(18), 4041; https://doi.org/10.3390/s19184041 - 19 Sep 2019
Cited by 15 | Viewed by 3428
Abstract
The purpose of this research was to create a thin ferrous polymer composite to be used as a target for inductive sensing in limb prosthetics. Inductive sensors are used to monitor limb-to-socket distance in prosthetic sockets, which reflects socket fit. A styrene–ethylene–ethylene/propylene–styrene (SEEPS) [...] Read more.
The purpose of this research was to create a thin ferrous polymer composite to be used as a target for inductive sensing in limb prosthetics. Inductive sensors are used to monitor limb-to-socket distance in prosthetic sockets, which reflects socket fit. A styrene–ethylene–ethylene/propylene–styrene (SEEPS) polymer was mixed with iron powder at three concentrations (75, 77, 85 wt%), and thin disk-shaped samples were fabricated (0.50, 0,75, 1.00 mm thickness). For 85 wt% samples of 0.50 mm thickness, which proved the best combination of high signal strength and low target volume, inductive sensor sensitivity ranged from 3.2E5 counts/mm at 0.00–1.00 mm distances to 7.2E4 counts/mm at 4.00–5.00 mm distances. The application of compressive stress (up to 425 kPa) introduced an absolute measurement error of less than 3.3 μm. Tensile elasticity was 282 kPa, which is comparable to that of commercial elastomeric liners. Durability testing in the shoe of an able-bodied participant demonstrated a change in calibration coefficient of less than 3.8% over two weeks of wear. The ferrous polymer composite may facilitate the development of automatically adjusting sockets that use limb-to-socket distance measurement for feedback control. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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31 pages, 10178 KiB  
Article
A New Integrated System for Assistance in Communicating with and Telemonitoring Severely Disabled Patients
by Radu Gabriel Bozomitu, Lucian Niţă, Vlad Cehan, Ioana Dana Alexa, Adina Carmen Ilie, Alexandru Păsărică and Cristian Rotariu
Sensors 2019, 19(9), 2026; https://doi.org/10.3390/s19092026 - 30 Apr 2019
Cited by 12 | Viewed by 4398
Abstract
In this paper, we present a new complex electronic system for facilitating communication with severely disabled patients and telemonitoring their physiological parameters. The proposed assistive system includes three subsystems (Patient, Server, and Caretaker) connected to each other via the Internet. The two-way communication [...] Read more.
In this paper, we present a new complex electronic system for facilitating communication with severely disabled patients and telemonitoring their physiological parameters. The proposed assistive system includes three subsystems (Patient, Server, and Caretaker) connected to each other via the Internet. The two-way communication function is based on keywords technology using a WEB application implemented at the server level, and the application is accessed remotely from the patient’s laptop/tablet PC. The patient’s needs can be detected by using different switch-type sensors that are adapted to the patient’s physical condition or by using eye-tracking interfaces. The telemonitoring function is based on a wearable wireless sensor network, organized around the Internet of Things concept, and the sensors acquire different physiological parameters of the patients according to their needs. The mobile Caretaker device is represented by a Smartphone, which uses an Android application for communicating with patients and performing real-time monitoring of their physiological parameters. The prototype of the proposed assistive system was tested in “Dr. C.I. Parhon” Clinical Hospital of Iaşi, Romania, on hospitalized patients from the Clinic of Geriatrics and Gerontology. The system contributes to an increase in the level of care and treatment for disabled patients, and this ultimately lowers costs in the healthcare system. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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13 pages, 2548 KiB  
Article
Classification of Lifting Techniques for Application of A Robotic Hip Exoskeleton
by Baojun Chen, Francesco Lanotte, Lorenzo Grazi, Nicola Vitiello and Simona Crea
Sensors 2019, 19(4), 963; https://doi.org/10.3390/s19040963 - 25 Feb 2019
Cited by 27 | Viewed by 6068
Abstract
The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize [...] Read more.
The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users’ movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class “no lift”) were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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