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Applied Sciences
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Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios
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Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 28654

Special Issue Editors

Dr. Matteo Laffranchi

E-Mail Website
Guest Editor
Rehab Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
Interests: wearable robotics; healthcare robotics; mechatronics; design; user-centered design
Special Issues, Collections and Topics in MDPI journals
Dr. Michela Chiappalone

E-Mail Website
Guest Editor
Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
Interests: neuroengineering; neuroprosthetics; neurorehabilitation; pre-clinical studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are witnessing a rapid growth in the demand of wearable robotics. Indeed, recent market studies forecast a huge shift in the market size, which is expected to grow from today’s $200 M to $3.5 B in 2026. Within this domain, healthcare exoskeletons will retain a dominant share because of their rapidly increasing need in the treatment of motor and neuromotor diseases such as spinal cord injury, stroke, brain damage, and neurodegenerative diseases. Consequently, researchers are developing novel devices and technologies to fulfill this need and to facilitate their adoption by increasing user acceptability through co-development approaches which directly involve end-users in the process.

This Research Topic aims at gathering cutting-edge research on novel developments in the field of wearable robotics for both rehabilitation and personal use. The emphasis is on mechatronic hardware developments and control, as well as on design of novel (neuro)rehabilitation training protocols and quantitative methods for the assessment of recovery, including connectomics.

Among other works, we welcome interdisciplinary applied research conducted in co-development with clinicians and/or with other stakeholders with specific focus on the end user, i.e., user-centered design, applied to real-world use case scenarios. In addition, we seek for robotic-based clinical research for (neuro)rehabilitation, Brain/Body-Computer Interfaces involving wearables, neuroimaging-based methods to assess the progress of recovery, methods to evaluate neural and muscular correlates of behavior, including structural/functional/effective connectivity.

Dr. Matteo Laffranchi
Dr. Michela Chiappalone
Guest Editors

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Keywords

  • applied research
  • clinical research
  • connectomics
  • control design
  • electroencephalography
  • electromyography
  • mechatronics
  • neurorehabilitation
  • rehabilitation robotics
  • user-centred design
  • wearable robotics

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Published Papers (7 papers)

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Research

21 pages, 26877 KiB  
Article
A Gravity-Compensated Upper-Limb Exoskeleton for Functional Rehabilitation of the Shoulder Complex
by Stefano Buccelli, Federico Tessari, Fausto Fanin, Luca De Guglielmo, Gianluca Capitta, Chiara Piezzo, Agnese Bruschi, Frank Van Son, Silvia Scarpetta, Antonio Succi, Paolo Rossi, Stefano Maludrottu, Giacinto Barresi, Ilaria Creatini, Elisa Taglione, Matteo Laffranchi and Lorenzo De Michieli
Appl. Sci. 2022, 12(7), 3364; https://doi.org/10.3390/app12073364 - 25 Mar 2022
Cited by 32 | Viewed by 6501
Abstract
In the last decade, several exoskeletons for shoulder rehabilitation have been presented in the literature. Most of these devices focus on the shoulder complex and limit the normal mobility of the rest of the body, forcing the patient into a fixed standing or [...] Read more.
In the last decade, several exoskeletons for shoulder rehabilitation have been presented in the literature. Most of these devices focus on the shoulder complex and limit the normal mobility of the rest of the body, forcing the patient into a fixed standing or sitting position. Nevertheless, this severely limits the range of activities that can potentially be simulated during the rehabilitation, preventing the execution of occupational therapy which involves the execution of tasks based on activities of daily living (ADLs). These tasks involve different muscular groups and whole-body movements, such as, e.g., picking up objects from the ground. To enable whole-body functional rehabilitation, the challenge is to shift the paradigm of robotic rehabilitation towards machines that can enable wide workspaces and high mobility. In this perspective, here we present Float: an upper-limb exoskeleton designed to promote and accelerate the motor and functional recovery of the shoulder joint complex following post-traumatic or post-surgical injuries. Indeed, Float allows the patient to move freely in a very large workspace. The key component that enables this is a passive polyarticulated arm which supports the total exoskeleton weight and allows the patient to move freely in space, empowering rehabilitation through a deeper interaction with the surrounding environment. A characterization of the reachable workspace of both the exoskeleton and the polyarticulated passive arm is presented. These results support the conclusion that a patient wearing Float can perform a wide variety of ADLs without bearing its weight. Full article
(This article belongs to the Special Issue Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios)
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20 pages, 1522 KiB  
Article
Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation
by Vicente Quiles, Laura Ferrero, Eduardo Iáñez, Mario Ortiz, José M. Cano and José M. Azorín
Appl. Sci. 2022, 12(1), 415; https://doi.org/10.3390/app12010415 - 1 Jan 2022
Cited by 7 | Viewed by 2606
Abstract
Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the [...] Read more.
Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented. Full article
(This article belongs to the Special Issue Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios)
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16 pages, 1178 KiB  
Article
Implementing Ethical, Legal, and Societal Considerations in Wearable Robot Design
by Alexandra Kapeller, Heike Felzmann, Eduard Fosch-Villaronga, Kostas Nizamis and Ann-Marie Hughes
Appl. Sci. 2021, 11(15), 6705; https://doi.org/10.3390/app11156705 - 21 Jul 2021
Cited by 16 | Viewed by 5305
Abstract
Ethical, legal and societal implications (ELSI) in the development of wearable robots (WRs) are currently not explicitly addressed in most guidelines for WR developers. Previous work has identified ELSI related to WRs, e.g., impacts on body and identity, ableism, data protection, control and [...] Read more.
Ethical, legal and societal implications (ELSI) in the development of wearable robots (WRs) are currently not explicitly addressed in most guidelines for WR developers. Previous work has identified ELSI related to WRs, e.g., impacts on body and identity, ableism, data protection, control and responsibilities, but translation of these concerns into actionable recommendations remains outstanding. This paper provides practical guidance for the implementation of ELSI in WR design, development and use. First, we identify the need for domain-specific recommendations against the context of current ELSI guidance. We then demonstrate the feasibility and usefulness of taking a domain-specific approach by successively transforming currently identified ELSI into an action-guiding flowchart for integration of ELSI specific to the different stages of WR development. This flowchart identifies specific questions to be considered by WR development teams and suggests actions to be taken in response. By tailoring ELSI guidance to WR developers, centring it on user needs, their relation to others and wider society, and being cognizant of existing legislation and values, we hope to help the community develop better WRs that are safer, have greater usability, and which impact positively on society. Full article
(This article belongs to the Special Issue Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios)
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17 pages, 9627 KiB  
Article
SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
by Zeyu Yin, Jianbin Zheng, Liping Huang, Yifan Gao, Huihui Peng and Linghan Yin
Appl. Sci. 2021, 11(12), 5573; https://doi.org/10.3390/app11125573 - 16 Jun 2021
Cited by 23 | Viewed by 2478
Abstract
An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The [...] Read more.
An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% ± 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots. Full article
(This article belongs to the Special Issue Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios)
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14 pages, 16847 KiB  
Article
The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy
by Jianfei Zhu, Chunzhi Yi, Baichun Wei, Chifu Yang, Zhen Ding and Feng Jiang
Appl. Sci. 2021, 11(9), 3821; https://doi.org/10.3390/app11093821 - 23 Apr 2021
Cited by 6 | Viewed by 2936
Abstract
Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing [...] Read more.
Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing the time-varying changes of the extracted features, and proposes a training strategy that can improve the classifiers’ robustness against muscle fatigue. In particular, we first select some features that are commonly used in fatigue-related studies and use them to classify gait phases under muscle fatigue. Then, we analyze the time-varying characteristics of extracted features, with the aim of explaining the performance of the classifiers. Finally, we propose a training strategy that effectively improves the robustness against muscle fatigue, which contributes to an easy-to-use method. Ten subjects performing prolonged walking are recruited. Our study contributes to a novel perspective of designing gait phase classifiers under muscle fatigue. Full article
(This article belongs to the Special Issue Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios)
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12 pages, 1578 KiB  
Article
Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
by Elisa Galofaro, Erika D’Antonio, Fabrizio Patané, Maura Casadio and Lorenzo Masia
Appl. Sci. 2021, 11(6), 2615; https://doi.org/10.3390/app11062615 - 15 Mar 2021
Cited by 9 | Viewed by 3936
Abstract
Proprioception—the sense of body segment’s position and movement—plays a crucial role in human motor control, integrating the sensory information necessary for the correct execution of daily life activities. Despite scientific evidence recognizes that several neurological diseases hamper proprioceptive encoding with consequent inability to [...] Read more.
Proprioception—the sense of body segment’s position and movement—plays a crucial role in human motor control, integrating the sensory information necessary for the correct execution of daily life activities. Despite scientific evidence recognizes that several neurological diseases hamper proprioceptive encoding with consequent inability to correctly perform movements, proprioceptive assessment in clinical settings is still limited to standard scales. Literature on physiology of upper limb’s proprioception is mainly focused on experimental approaches involving planar setups, while the present work provides a novel paradigm for assessing proprioception during single—and multi-joint matching tasks in a three-dimensional workspace. To such extent, a six-degrees of freedom exoskeleton, ALEx-RS (Arm Light Exoskeleton Rehab Station), was used to evaluate 18 healthy subjects’ abilities in matching proprioceptive targets during combined single and multi-joint arm’s movements: shoulder abduction/adduction, shoulder flexion/extension, and elbow flexion/extension. Results provided evidence that proprioceptive abilities depend on the number of joints simultaneously involved in the task and on their anatomical location, since muscle spindles work along their preferred direction, modulating the streaming of sensory information accordingly. These findings suggest solutions for clinical sensorimotor evaluation after neurological disease, where assessing proprioceptive deficits can improve the recovery path and complement the rehabilitation outcomes. Full article
(This article belongs to the Special Issue Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios)
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14 pages, 3434 KiB  
Article
A Novel Method for Designing Motion Profiles Based on a Fuzzy Logic Algorithm Using the Hip Joint Angles of a Lower-Limb Exoskeleton Robot
by Buchun Song, Dongyoung Lee, Sang Yong Park and Yoon Su Baek
Appl. Sci. 2020, 10(19), 6852; https://doi.org/10.3390/app10196852 - 29 Sep 2020
Cited by 11 | Viewed by 3433
Abstract
In this study, a novel method for designing real-time motion profiles based on a weighted fuzzy logic algorithm for an exoskeleton robot was proposed. When developing exoskeleton robots, it is important that they can identify a wearer’s motion intent in real time; therefore, [...] Read more.
In this study, a novel method for designing real-time motion profiles based on a weighted fuzzy logic algorithm for an exoskeleton robot was proposed. When developing exoskeleton robots, it is important that they can identify a wearer’s motion intent in real time; therefore, we produced the motion profiles of an exoskeleton robot knee joint angles using hip joint angles and plantar pressure sensors. Two types of sensors were used to design the robot’s knee estimation angle profiles in real time—namely, hip joint angles to design the fuzzy logic algorithm and plantar pressure sensors to classify the robot’s gait phase. In the fuzzy set, four fuzzy inputs were produced through the hip joint angles; then, four fuzzy outputs were implemented based on the fuzzy inputs using 68 predefined rule bases in the fuzzy inference. The fuzzy outputs were used as the basis for calculating the motion profiles during the defuzzification. To adjust the knee angle of the robot, the weighted values were assigned to each hip joint angle section. To validate the proposed algorithm, we conducted two experiments—namely, the exoskeleton robot with and without an actuator. The method was verified through experiments showing that the motion profiles estimated the robot’s knee angles close to the desired angles. Full article
(This article belongs to the Special Issue Wearable Robotics for Healthcare: User-Centered Development, Control and Application Scenarios)
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