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Wearable & Soft Robotics Technologies and Beyond

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 16166

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Special Issue Editors

Dr. Emanuele Lindo Secco

E-Mail Website
Guest Editor

Robotics Lab, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Hope Park, Liverpool L16 9JD, UK
Interests: robotics; life-like systems; wearable sensors

Dr. Stefano Dalla Gasperina

E-Mail Website
Guest Editor

NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Interests: robotics; mechatronics; exoskeletons; physical human-robot interaction; rehabilitation

Special Issue Information

Dear Colleagues,

In recent years, the field of wearable robotics has undergone a dramatic change, from rigid systems to soft exoskeletons and suits, emerging as a research topic in robotics and progressively bringing wearable technologies a step closer towards use in daily life.

In this context, due to their ergonomics and portability, soft wearable robots—also referred to as soft robotic suits or exo-suits—have shown the potential to improve people's lives in countless ways. Indeed, soft robots can promote human empowerment and augmentation, as well as injury prevention, and support motor disabilities in impaired, elderly and disabled people. Thus, the market is envisioning a rapid growth in the demand for soft wearable robotics, with an expected market size of approximately USD 5.2 billion by 2025.

Despite such growing interest in soft wearable robotics, several issues are currently limiting their applicability in daily life, such as non-robust design and control, bulkiness, improper force human–robot interaction, and high power consumption, to mention a few. In this context, we highlight the need for novel cutting-edge research to improve the user acceptability of wearable technologies, potentially obtainable through ergonomic, comfortable, and user-friendly systems to adapt the physical human–robot interaction according to the users' needs and the environmental conditions.

This Special Issue aims to bridge the gap between available technologies and application needs. The focus includes novel actuator mechanisms, biologically inspired and biomimetic designs, FES-based hybrid systems, intelligent controls, and user-based evaluations in real-world scenarios. Additionally, we seek research to assess the practical potential and impact of soft wearable robots on people with disabilities, athletes, workers, and others.

Topics include, but are not limited to:

Advancements in actuators, mechanical design, and transmissions for wearable robotics;
Intelligent controls for adaptable human–robot interaction;
Functional electrical stimulation (FES) in wearable technologies;
Human-cooperative and collaborative control strategies;
Power consumption issues and solutions;
Human subject evaluation of wearable robots in healthcare and industrial scenarios.

Dr. Emanuele Lindo Secco
Dr. Stefano Dalla Gasperina
Guest Editors

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Keywords

wearable robotics
exoskeletons
soft robots and exo-suits
human–robot interaction (pHRI)
user-centered design
bio-inspired design
mechatronics
intelligent control
human empowerment
human augmentation
rehabilitation robotics
assistive devices

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Published Papers (5 papers)

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Research

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12 pages, 3280 KiB

Open AccessArticle

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance

by Letizia Gionfrida, Richard W. Nuckols, Conor J. Walsh and Robert D. Howe

Sensors 2023, 23(3), 1670; https://doi.org/10.3390/s23031670 - 3 Feb 2023

Cited by 4 | Viewed by 2443

Abstract

In the field of wearable robotics, assistance needs to be individualized for the user to maximize benefit. Information from muscle fascicles automatically recorded from brightness mode (B-mode) ultrasound has been used to design assistance profiles that are proportional to the estimated muscle force of young individuals. There is also a desire to develop similar strategies for older adults who may have age-altered physiology. This study introduces and validates a ResNet + 2x-LSTM model for extracting fascicle lengths in young and older adults. The labeling was generated in a semimanual manner for young (40,696 frames) and older adults (34,262 frames) depicting B-mode imaging of the medial gastrocnemius. First, the model was trained on young and tested on both young (R² = 0.85, RMSE = 2.36 ± 1.51 mm, MAPE = 3.6%, aaDF = 0.48 ± 1.1 mm) and older adults (R² = 0.53, RMSE = 4.7 ± 2.51 mm, MAPE = 5.19%, aaDF = 1.9 ± 1.39 mm). Then, the performances were trained across all ages (R² = 0.79, RMSE = 3.95 ± 2.51 mm, MAPE = 4.5%, aaDF = 0.67 ± 1.8 mm). Although age-related muscle loss affects the error of the tracking methodology compared to the young population, the absolute percentage error for individual fascicles leads to a small variation of 3–5%, suggesting that the error may be acceptable in the generation of assistive force profiles. Full article

(This article belongs to the Special Issue Wearable & Soft Robotics Technologies and Beyond)

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17 pages, 6078 KiB

Open AccessArticle

Passive Exoskeleton with Gait-Based Knee Joint Support for Individuals with Cerebral Palsy

by Maxwell Kennard, Hideki Kadone, Yukiyo Shimizu and Kenji Suzuki

Sensors 2022, 22(22), 8935; https://doi.org/10.3390/s22228935 - 18 Nov 2022

Cited by 2 | Viewed by 2562

Abstract

Cerebral palsy is a neurological disorder with a variety of symptoms that can affect muscle coordination and movement. Crouch gait is one such symptom that is defined as excessive knee flexion accompanied by a crouched posture. This paper introduces a passive exoskeleton to support the knee joint during stance of individuals with cerebral palsy that are affected by crouch gait. The exoskeleton utilizes a hydraulic disc brake mechanism that is actuated only by the body weight and gait of the wearer to provide a braking torque at the knee joint. This passive, gait-based control method aims to offer a compact, lightweight, and simple alternative to existing exoskeletons. Preliminary experiments were conducted to verify the mechanics, safety, and braking capabilities of the device with healthy participants. A pilot study with an individual with cerebral palsy was then conducted. The individual with cerebral palsy showed a reduction in hip joint angle when using the device (18.8

^{\circ}

and 21.7

^{\circ}

for left and right sides, respectively). The muscle co-activation index was also reduced from 0.48 to 0.24 on the right side and from 0.17 to 0.017 on the left side. However, changes such as activation timing and device training need to be improved to better support the user. Full article

(This article belongs to the Special Issue Wearable & Soft Robotics Technologies and Beyond)

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14 pages, 1223 KiB

Open AccessArticle

Investigating the Overall Experience of Wearable Robots during Prototype-Stage Testing

by Jinlei Wang, Suihuai Yu, Xiaoqing Yuan, Yahui Wang, Dengkai Chen and Wendong Wang

Sensors 2022, 22(21), 8367; https://doi.org/10.3390/s22218367 - 1 Nov 2022

Cited by 1 | Viewed by 1749

Abstract

Wearable robots (WRs) might interact with humans in a similar manner to teammates to accomplish specific tasks together. However, the available data on WR user experience (UX) studies are limited, especially during the prototyping phase. Therefore, this study aims to examine the overall experience of WRs during the prototyping phase based on an exploratory research model. This theoretical model considered usability, hedonic quality, and attitude toward using WRs as key factors in explaining and predicting overall experience. To test the hypotheses inherent in the research model, quantitative empirical research was conducted and the data were analyzed by partial least squares structural equation modeling (PLS-SEM). The results from the PLS-SEM analysis revealed the significance level of correlations between the latent variables in the research model. The exploratory research model was able to explain up to 53.2% of the variance in the overall experience of using WRs, indicating medium predictive power. This research develops a new quantitative empirical research model that can be used to explain and predict the overall experience of interactive products such as WRs. Meanwhile, the model is needed during WR testing in the prototype phase. Full article

(This article belongs to the Special Issue Wearable & Soft Robotics Technologies and Beyond)

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Review

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22 pages, 2618 KiB

Open AccessReview

A Critical Review on Factors Affecting the User Adoption of Wearable and Soft Robotics

by Benjamin Wee Keong Ang, Chen-Hua Yeow and Jeong Hoon Lim

Sensors 2023, 23(6), 3263; https://doi.org/10.3390/s23063263 - 20 Mar 2023

Cited by 6 | Viewed by 3844

Abstract

In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user’s perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as “soft”, “robot”, “wearable”, and “exoskeleton” was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted. Full article

(This article belongs to the Special Issue Wearable & Soft Robotics Technologies and Beyond)

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19 pages, 1842 KiB

Open AccessReview

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

by Vaheh Nazari and Yong-Ping Zheng

Sensors 2023, 23(4), 1885; https://doi.org/10.3390/s23041885 - 8 Feb 2023

Cited by 14 | Viewed by 4463

Abstract

This paper presents a critical review and comparison of the results of recently published studies in the fields of human–machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords “Human Machine Interface”, “Sonomyography”, “Ultrasound”, “Upper Limb Prosthesis”, “Artificial Intelligence”, and “Non-Invasive Sensors” was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human–machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%. Full article

(This article belongs to the Special Issue Wearable & Soft Robotics Technologies and Beyond)

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