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Wearable Sensors, Robotic Systems and Assistive Devices
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Wearable Sensors, Robotic Systems and Assistive Devices

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

Deadline for manuscript submissions: 1 May 2025 | Viewed by 8704

Special Issue Editors

Prof. Dr. Gurvinder Singh Virk

E-Mail Website
Guest Editor
1. School of Engineering, UPES University, Dehradun, India
2. Endoenergy Systems, Sahibzada Ajit Singh Nagar, Punjab, India
Interests: service robots; climbing and walking robots; wearable exoskeletons (medical and non-medical applications); ISO/IEC standardisation, human-robot interaction; robot safety; robot security; robot modularity; human motion intention detection; autonomous robotics; intelligent systems; real-time embedded systems; software engineering; entrepreneurship
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Occhipinti

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Cambridge, Cambridge CB3 0FA, UK
Interests: sensors and integrated bio-systems for applications in health care; industrial sustainability; smart wearables; environmental monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Zlatka Stoeva

E-Mail Website
Guest Editor
DZP Technologies Ltd., Future Business Centre, Kings Hedges Rd., Cambridge CB4 2HY, UK
Interests: flexible and stretchable electronics; wearable technology; graphene sensors; novel sensor materials
Prof. Dr. Shaoping Bai

E-Mail Website
Guest Editor
Department of Materials and Production, Aalborg University, Fibigerstræde 16, 9220 Aalborg, Denmark
Interests: robotics; parallel robots; exoskeletons; linkages
Special Issues, Collections and Topics in MDPI journals
Dr. Jan Veneman

E-Mail Website
Guest Editor
Hocoma AG, Volketswil, Switzerland
Interests: exoskeletons; rehabilitation robotics; mechatronics; gait and posture; human-robot Interaction

Special Issue Information

Dear Colleagues,

The field of wearables is a rapidly expanding and wide-ranging area of research and entrepreneurship, with many of its activities driven by concerns of an ageing society concerns and to foster a culture of healthy ageing. Examples include smart sensors to monitor the health and wellbeing of ageing relatives, or servant robots to help users perform domestic tasks and even help individuals carry out daily living activities themselves so that elderly persons can stay living in their own homes for as long as possible with independence, dignity and good quality of life. Such wearable and assistive innovations are urgently needed. The topic is highly interdisciplinary and requires input from engineers, software developers, physicists and materials scientists, healthcare professionals, etc. This broad range of knowledge and expertise across different disciplines is fruitful ground for exciting new developments and breakthrough innovations.

Wearable technologies also have applications in other sectors; these include general wellbeing, use in sports training and monitoring performance, industrial and agritech applications, enhancement of humans for military use, etc. In fact, multiple-use case scenarios where the physical and cognitive capabilities of humans need to be supported or enhanced are widely being investigated by researchers and new products are becoming available to citizens in emerging markets. New international regulations are also being developed to ensure the safety and security of citizens as they start to use new wearable innovations.

This Special Issue invites contributions from all areas where wearable technologies are being researched and further developed for commercialisation.

Prof. Dr. Gurvinder Singh Virk
Dr. Luigi Occhipinti
Dr. Zlatka Stoeva
Prof. Dr. Shaoping Bai
Dr. Jan Veneman
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

  • human bio-sensors
  • human ambulation sensors
  • human motion intention detection systems
  • mobile servant robots
  • physical assistance
  • cognitive assistance
  • humanoid robots
  • wearable assistive exoskeletons
  • supplementation assistive systems
  • augmentation assistive systems
  • medical assistive systems
  • rehabilitation exoskeletons
  • physical assessment capability systems
  • wearable technology regulations
  • human ethics

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Published Papers (4 papers)

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Research

21 pages, 20739 KiB  
Article
Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language
by Anna Filipowska, Wojciech Filipowski, Julia Mieszczanin, Katarzyna Bryzik, Maciej Henkel, Emilia Skwarek, Paweł Raif, Szymon Sieciński, Rafał Doniec, Barbara Mika, Julia Bodak, Piotr Ferst, Marcin Pieniążek, Kamil Pilarski and Marcin Grzegorzek
Sensors 2024, 24(20), 6710; https://doi.org/10.3390/s24206710 - 18 Oct 2024
Viewed by 1634
Abstract
Gesture recognition has become a significant part of human–machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including [...] Read more.
Gesture recognition has become a significant part of human–machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language. Full article
(This article belongs to the Special Issue Wearable Sensors, Robotic Systems and Assistive Devices)
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21 pages, 4604 KiB  
Article
The Aerial Guide Dog: A Low-Cognitive-Load Indoor Electronic Travel Aid for Visually Impaired Individuals
by Xiaochen Zhang, Ziyi Pan, Ziyang Song, Yang Zhang, Wujing Li and Shiyao Ding
Sensors 2024, 24(1), 297; https://doi.org/10.3390/s24010297 - 4 Jan 2024
Cited by 1 | Viewed by 2389
Abstract
Most navigation aids for visually impaired individuals require users to pay close attention and actively understand the instructions or feedback of guidance, which impose considerable cognitive loads in long-term usage. To tackle the issue, this study proposes a cognitive burden-free electronic travel aid [...] Read more.
Most navigation aids for visually impaired individuals require users to pay close attention and actively understand the instructions or feedback of guidance, which impose considerable cognitive loads in long-term usage. To tackle the issue, this study proposes a cognitive burden-free electronic travel aid for individuals with visual impairments. Utilizing human instinctive compliance in response to external force, we introduce the “Aerial Guide Dog”, a helium balloon aerostat drone designed for indoor guidance, which leverages gentle tugs in real time for directional guidance, ensuring a seamless and intuitive guiding experience. The introduced Aerial Guide Dog has been evaluated in terms of directional guidance and path following in the pilot study, focusing on assessing its accuracy in orientation and the overall performance in navigation. Preliminary results show that the Aerial Guide Dog, utilizing Ultra-Wideband (UWB) spatial positioning and Measurement Unit (IMU) angle sensors, consistently maintained minimal deviation from the targeting direction and designated path, while imposing negligible cognitive burdens on users while completing the guidance tasks. Full article
(This article belongs to the Special Issue Wearable Sensors, Robotic Systems and Assistive Devices)
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21 pages, 8758 KiB  
Article
Outdoor Navigation Assistive System Based on Robust and Real-Time Visual–Auditory Substitution Approach
by Florian Scalvini, Camille Bordeau, Maxime Ambard, Cyrille Migniot and Julien Dubois
Sensors 2024, 24(1), 166; https://doi.org/10.3390/s24010166 - 27 Dec 2023
Cited by 5 | Viewed by 2218
Abstract
Blindness affects millions of people worldwide, leading to difficulties in daily travel and a loss of independence due to a lack of spatial information. This article proposes a new navigation aid to help people with severe blindness reach their destination. Blind people are [...] Read more.
Blindness affects millions of people worldwide, leading to difficulties in daily travel and a loss of independence due to a lack of spatial information. This article proposes a new navigation aid to help people with severe blindness reach their destination. Blind people are guided by a short 3D spatialised sound that indicates the target point to follow. This sound is combined with other sonified information on potential obstacles in the vicinity. The proposed system is based on inertial sensors, GPS data, and the cartographic knowledge of pedestrian paths to define the trajectory. In addition, visual clues are used to refine the trajectory with ground floor information and obstacle information using a camera to provide 3D spatial information. The proposed method is based on a deep learning approach. The different neural networks used in this approach are evaluated on datasets that regroup navigations from pedestrians’ point-of-view. This method achieves low latency and real-time processing without relying on remote connections, instead using a low-power embedded GPU target and a multithreaded approach for video processing, sound generation, and acquisition. This system could significantly improve the quality of life and autonomy of blind people, allowing them to reliably and efficiently navigate in their environment. Full article
(This article belongs to the Special Issue Wearable Sensors, Robotic Systems and Assistive Devices)
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23 pages, 11983 KiB  
Article
A Self-Coordinating Controller with Balance-Guiding Ability for Lower-Limb Rehabilitation Exoskeleton Robot
by Li Qin, Houzhao Ji, Minghao Chen and Ke Wang
Sensors 2023, 23(11), 5311; https://doi.org/10.3390/s23115311 - 3 Jun 2023
Cited by 4 | Viewed by 1679
Abstract
The restricted posture and unrestricted compliance brought by the controller during human–exoskeleton interaction (HEI) can cause patients to lose balance or even fall. In this article, a self-coordinated velocity vector (SCVV) double-layer controller with balance-guiding ability was developed for a lower-limb rehabilitation exoskeleton [...] Read more.
The restricted posture and unrestricted compliance brought by the controller during human–exoskeleton interaction (HEI) can cause patients to lose balance or even fall. In this article, a self-coordinated velocity vector (SCVV) double-layer controller with balance-guiding ability was developed for a lower-limb rehabilitation exoskeleton robot (LLRER). In the outer loop, an adaptive trajectory generator that follows the gait cycle was devised to generate a harmonious hip–knee reference trajectory on the non-time-varying (NTV) phase space. In the inner loop, velocity control was adopted. By searching the minimum L2 norm between the reference phase trajectory and the current configuration, the desired velocity vectors in which encouraged and corrected effects can be self-coordinated according to the L2 norm were obtained. In addition, the controller was simulated using an electromechanical coupling model, and relevant experiments were carried out with a self-developed exoskeleton device. Both simulations and experiments validated the effectiveness of the controller. Full article
(This article belongs to the Special Issue Wearable Sensors, Robotic Systems and Assistive Devices)
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