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Assistive and Rehabilitation Technologies Based on Intelligent Sensors
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Assistive and Rehabilitation Technologies Based on Intelligent Sensors

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

Deadline for manuscript submissions: 1 December 2024 | Viewed by 4389

Special Issue Editors

Dr. Andrea Tigrini

E-Mail Website
Guest Editor
Deparment of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: human biomechanics; neuromuscular control and rehabilitation engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Mengarelli

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy
Interests: biomechanics; movement analysis; advanced signal processing; biomechanical modeling and control
Special Issues, Collections and Topics in MDPI journals
Dr. Federica Verdini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: biomechanics; movement analysis; advanced signal processing; biomechanical modeling and control
Special Issues, Collections and Topics in MDPI journals
Dr. Ali Al-Timemy

E-Mail Website
Guest Editor
Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 10011, Iraq
Interests: feature extraction; machine learning; biomedical signal processing; EEG
Dr. Simone Ranaldi

E-Mail Website
Guest Editor
Department of Industrial, Electronics and Mechanical Engineering, Roma Tre University, 00154 Roma, Italy
Interests: cooperative human-robot interaction; human movement analysis; prosthetic control

Special Issue Information

Dear Colleagues,

The increase in the elderly population and rising prevalence of chronic conditions require advancements in assistive and rehabilitation technologies. Intelligent sensors play a pivotal role in the development of such technologies as they allow the patient to be monitored while offering the possibility of implementing smart control strategies for more fluent human–device interaction. This Special Issue aims to explore how intelligent sensors are being leveraged to create smarter assistive and rehabilitation devices.

The Special Issue welcomes contributions from the academic community in the following areas:

  • Sensor-based technologies for balance maintenance, gait and physical activity assessment.
  • Intelligent prosthetics and active orthotics.
  • Embedded systems for rehabilitation monitoring.
  • Human–computer interfaces and interactions for assistive technologies.
  • Physical-based and data-driven solutions for assistive device prototyping

Dr. Andrea Tigrini
Dr. Alessandro Mengarelli
Dr. Federica Verdini
Dr. Ali Hussein Al-Timemy
Dr. Simone Ranaldi
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–computer interfaces
  • rehabilitation and assistive technologies
  • prosthesis control, EMG sensors, IMU systems

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Published Papers (3 papers)

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Research

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15 pages, 999 KiB  
Article
Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition
by Andrea Tigrini, Rami Mobarak, Alessandro Mengarelli, Rami N. Khushaba, Ali H. Al-Timemy, Federica Verdini, Ennio Gambi, Sandro Fioretti and Laura Burattini
Sensors 2024, 24(17), 5828; https://doi.org/10.3390/s24175828 - 8 Sep 2024
Viewed by 907
Abstract
Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) [...] Read more.
Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach’s ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification. Full article
(This article belongs to the Special Issue Assistive and Rehabilitation Technologies Based on Intelligent Sensors)
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14 pages, 3600 KiB  
Article
Inertial Measurement Unit and Heart Rate Monitoring to Assess Cardiovascular Fitness of Manual Wheelchair Users during the Six-Minute Push Test
by Grace Fasipe, Maja Goršič, Erika V. Zabre and Jacob R. Rammer
Sensors 2024, 24(13), 4172; https://doi.org/10.3390/s24134172 - 27 Jun 2024
Cited by 1 | Viewed by 1117
Abstract
Manual wheelchair users (MWUs) are prone to a sedentary life that can negatively affect their physical and cardiovascular health, making regular assessment important to identify appropriate interventions and lifestyle modifications. One mean of assessing MWUs’ physical health is the 6 min push test [...] Read more.
Manual wheelchair users (MWUs) are prone to a sedentary life that can negatively affect their physical and cardiovascular health, making regular assessment important to identify appropriate interventions and lifestyle modifications. One mean of assessing MWUs’ physical health is the 6 min push test (6MPT), where the user propels themselves as far as they can in six minutes. However, reliance on observer input introduces subjectivity, while limited quantitative data inhibit comprehensive assessment. Incorporating sensors into the 6MPT can address these limitations. Here, ten MWUs performed the 6MPT with additional sensors: two inertial measurement units (IMUs)—one on the wheelchair and one on the wrist together with a heart rate wristwatch. The conventional measurements of distance and laps were recorded by the observer, and the IMU data were used to calculate laps, distance, speed, and cadence. The results demonstrated that the IMU can provide the metrics of the traditional 6MPT with strong significant correlations between calculated laps and observer lap counts (r = 0.947, p < 0.001) and distances (r = 0.970, p < 0.001). Moreover, heart rate during the final minute was significantly correlated with calculated distance (r = 0.762, p = 0.017). Enhanced 6MPT assessment can provide objective, quantitative, and comprehensive data for clinicians to effectively inform interventions in rehabilitation. Full article
(This article belongs to the Special Issue Assistive and Rehabilitation Technologies Based on Intelligent Sensors)
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Review

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19 pages, 937 KiB  
Review
Handgrip Strength in Health Applications: A Review of the Measurement Methodologies and Influencing Factors
by Antonino Quattrocchi, Giada Garufi, Giovanni Gugliandolo, Cristiano De Marchis, Domenicantonio Collufio, Salvatore Massimiliano Cardali and Nicola Donato
Sensors 2024, 24(16), 5100; https://doi.org/10.3390/s24165100 - 6 Aug 2024
Viewed by 1962
Abstract
This narrative review provides a comprehensive analysis of the several methods and technologies employed to measure handgrip strength (HGS), a significant indicator of neuromuscular strength and overall health. The document evaluates a range of devices, from traditional dynamometers to innovative sensor-based systems, and [...] Read more.
This narrative review provides a comprehensive analysis of the several methods and technologies employed to measure handgrip strength (HGS), a significant indicator of neuromuscular strength and overall health. The document evaluates a range of devices, from traditional dynamometers to innovative sensor-based systems, and assesses their effectiveness and application in different demographic groups. Special attention is given to the methodological aspects of HGS estimation, including the influence of device design and measurement protocols. Endogenous factors such as hand dominance and size, body mass, age and gender, as well as exogenous factors including circadian influences and psychological factors, are examined. The review identifies significant variations in the implementation of HGS measurements and interpretation of the resultant data, emphasizing the need for careful consideration of these factors when using HGS as a diagnostic or research tool. It highlights the necessity of standardizing measurement protocols to establish universal guidelines that enhance the comparability and consistency of HGS assessments across various settings and populations. Full article
(This article belongs to the Special Issue Assistive and Rehabilitation Technologies Based on Intelligent Sensors)
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