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Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 21415

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Special Issue Editors

Dr. Andoni Carrasco-Uribarren

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Guest Editor

Department of Physiotherapy, Universitat Internacional de Catalunya, Barcelona, Spain
Interests: physiotherapy; musculoskeletal manipulations; exercise therapy; clinical reasoning; evidence-based practice; patients-reported outcomes; biomedical instrumentation; electronics; 3D printing
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Dr. Lucia Billeci

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Guest Editor

Clinical Physiology Institute, National Research Council of Italy (IFC-CNR), Via Moruzzi, 1, 56124 Pisa, Italy
Interests: sensors; wearable systems; signal processing; artificial intelligence; health; neuroscience
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Dr. Xavier Marimon

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Guest Editor Assistant

1. Bioengineering Institute of Technology, Universitat Internacional de Catalunya (UIC), 08190 Barcelona, Spain
2. Automatic Control Department, Universitat Politècnica de Catalunya (UPC-BarcelonaTECH), 08034 Barcelona, Spain
Interests: computational modelling; multiphysics simulation; finite element method (FEM); biomedical signal processing; biomedical imaging; dental implants
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Dr. Portela Alejandro

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Guest Editor Assistant

Bioengineering Institute of Technology, Universitat Internacional de Catalunya (UIC), 08195 Barcelona, Spain
Interests: biomedical instrumentation; biosensors; electronics; optical design; photospectrometry; nano/microfabrication; microfluidics
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Dr. Oliver Millan

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Guest Editor Assistant

Department of Electronic Engineering, Eastern Barcelona School of Engineering (EEBE), Technical University of Catalonia–BarcelonaTech (UPC), E-08019 Barcelona, Spain
Interests: electric power conversion; energy processing and management of renewable energy systems; bioengineering; sensor design; ultrasonic systems; transducers modeling; machine/deep learning
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Special Issue Information

Dear Colleagues,

This Special Issue delves into the intricate intersection of sensor technology and clinical rehabilitation, covering a range of topics, including the development of advanced sensor-based devices and wearables tailored to aid in clinical rehabilitation processes.

Articles within this Special Issue may explore the integration of sensors with rehabilitation equipment, monitoring patient progress through wearable devices, and leveraging analytical data for personalized rehabilitation programs.

The content is designed to appeal to researchers seeking a deeper understanding of the technological advancements in this field, and healthcare professionals engaged in rehabilitation will find valuable insights into how these innovations can improve patient outcomes and contribute to more effective and personalized rehabilitation strategies. Overall, the Special Issue aims to facilitate collaboration and the exchange of knowledge among experts working in the fields of sensor technology and clinical rehabilitation for the benefit of both research and practical applications.

This Special Issue welcomes a broad spectrum of contributions, ranging from original research articles to systematic literature reviews. Researchers are encouraged to submit their original findings in the form of research articles, showcasing novel insights and advancements in sensor-based devices and wearables for clinical rehabilitation. Additionally, systematic literature reviews providing a comprehensive analysis and synthesis of existing research in this domain are also welcome.

Dr. Andoni Carrasco-Uribarren
Dr. Lucia Billeci
Guest Editors

Dr. Xavier Marimon
Dr. Portela Alejandro
Dr. Oliver Millan
Guest Editor Assistants

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. Applied Sciences 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 2400 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

wearables
clinical rehabilitation
biomedical instrumentation

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Published Papers (9 papers)

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Research

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15 pages, 4433 KiB

Open AccessArticle

Wearable 256-Element MUX-Based Linear Array Transducer for Monitoring of Deep Abdominal Muscles

by Daniel Speicher, Tobias Grün, Steffen Weber, Holger Hewener, Stephan Klesy, Schabo Rumanus, Hannah Strohm, Oskar Stamm, Luis Perotti, Steffen H. Tretbar and Marc Fournelle

Appl. Sci. 2025, 15(7), 3600; https://doi.org/10.3390/app15073600 - 25 Mar 2025

Viewed by 286

Abstract

Reliable acoustic coupling in a non-handheld mode and reducing the form factor of electronics are specific challenges in making ultrasound wearable. Applications relying on a large field of view (such as tracking of large muscles) induce a need for a large element count to achieve high image quality. In our work, we developed a 256-element linear array for imaging of abdominal muscles with four integrated custom-developed 8:32 multiplexer Integrated Circuits (ICs), allowing the array to be driven by our compact 32 ch electronics. The system is optimized for flexible use in R&D applications and allows adjustable transmit voltages (up to +/−100 V), arbitrary delay patterns, and 12-bit analog-to-digital conversion (ADC) with up to 50 MSPS and wireless (21.6 MBit/s) or USB link. Image metrics (SLL, FWHM) were very similar to a fully populated array driven with a 256 ch system. The contrast allowed imaging of lesions down to 7 cm in the phantom. In a first in-vivo study, we demonstrated reliable acoustic contact even during exercise and were able to visualize deep abdominal muscles such as the TrA. In combination with a muscle tracking algorithm, the change of thickness of the TrA during SSE could be monitored, demonstrating the potential of the approach as biofeedback for physiotherapy training. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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13 pages, 1408 KiB

Open AccessArticle

Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults

by Ji-Eun Baek, Jin-Hwa Jung, Hang-Keun Kim and Hwi-Young Cho

Appl. Sci. 2024, 14(23), 11321; https://doi.org/10.3390/app142311321 - 4 Dec 2024

Viewed by 1296

Abstract

Smartphone-based accelerometers offer a cost-effective and portable alternative to traditional gait analysis systems, with high reliability in measuring key parameters such as walking speed, cadence, and distance. This study assessed their validity compared to the GAITRite system, a widely recognized gold-standard tool, using data from 30 healthy adults walking at 3 self-selected speeds: preferred, slow, and fast. The results demonstrated a high degree of agreement between the two systems, with intraclass correlation coefficients (ICCs) ranging from 0.778 to 0.999. Although the findings emphasize the potential of smartphone accelerometers for clinical and real-world applications, certain limitations were noted, including participant homogeneity and minor discrepancies at extreme walking speeds. To address these limitations, incorporating data from additional sensors, such as gyroscopes and magnetometers, may enhance the accuracy and reliability of spatial parameter estimation. Overall, the findings support the use of smartphone accelerometers as a promising tool for advancing gait monitoring technologies, particularly in the contexts of telerehabilitation and mobility assessments. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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16 pages, 584 KiB

Open AccessArticle

Enhancing Motor Imagery Classification in Brain–Computer Interfaces Using Deep Learning and Continuous Wavelet Transform

by Yu Xie and Stefan Oniga

Appl. Sci. 2024, 14(19), 8828; https://doi.org/10.3390/app14198828 - 1 Oct 2024

Viewed by 1319

Abstract

In brain–computer interface (BCI) systems, motor imagery (MI) electroencephalogram (EEG) is widely used to interpret the human brain. However, MI classification is challenging due to weak signals and a lack of high-quality data. While deep learning (DL) methods have shown significant success in pattern recognition, their application to MI-based BCI systems remains limited. To address these challenges, we propose a novel deep learning algorithm that leverages EEG signal features through a two-branch parallel convolutional neural network (CNN). Our approach incorporates different input signals, such as continuous wavelet transform, short-time Fourier transform, and common spatial patterns, and employs various classifiers, including support vector machines and decision trees, to enhance system performance. We evaluate our algorithm using the BCI Competition IV dataset 2B, comparing it with other state-of-the-art methods. Our results demonstrate that the proposed method excels in classification accuracy, offering improvements for MI-based BCI systems. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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11 pages, 7149 KiB

Open AccessArticle

Measuring Craniovertebral Angle Reference Values in Adults Using Kinovea Software

by Carlos Antonio Zárate-Tejero, Pere Ramón Rodríguez-Rubio, Lindsay Brandt, John Krauss, Mar Hernández-Secorún, César Hidalgo-García and Orosia Lucha-López

Appl. Sci. 2024, 14(19), 8639; https://doi.org/10.3390/app14198639 - 25 Sep 2024

Cited by 2 | Viewed by 3806

Abstract

The clinical examination of patients with cervical spine pathology includes measures of posture, mobility, strength, and stability. The forward head position as measured by craniovertebral angle (CVA) has been suggested as a risk factor for cervical spine pathology. The purpose of this study was to describe CVA reference values in healthy adults and assess the relationship with age and sex. This prospective cross-sectional observational study measured the CVA in 122 healthy adults using digital analysis of a lateral view photograph. To facilitate this analysis, a marker was placed at the level of the C7 vertebra, with the tragus of the ear visible in the photograph. The CVA was measured using Kinovea 0.8.15 software, where the CVA was calculated using the intersection of a horizontal line with a line joining the spinous process of C7 and the tragus of the ear. Mean CVA values were 48.76° (6.77) across all participants, 50.07° (6.63) for females, and 47.46° (6.71) for males. Linear regression analysis indicated significant relationships with age and with sex. The study established CVA reference values in healthy adults and identified a significant difference in head position between males and females and a 1.6° decrease in CVA per decade of increased age. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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19 pages, 1310 KiB

Open AccessArticle

Comparative Analysis of Neuromuscular Activation Patterns Associated with Force between Semi-Professional Female Soccer Players with Previous Anterior Cruciate Ligament Surgery and Healthy Players in Thigh Musculature Related to Valgus Collapse

by Loreto Ferrández-Laliena, Rocío Sánchez-Rodríguez, Lucía Vicente-Pina, María Orosia Lucha-López, Mira Ambrus, César Hidalgo-García, Sofía Monti-Ballano and José Miguel Tricás-Moreno

Appl. Sci. 2024, 14(16), 6869; https://doi.org/10.3390/app14166869 - 6 Aug 2024

Viewed by 1149

Abstract

This study investigates electromyography activation and force development differences in key lower limb muscles between female football players with previous anterior cruciate ligament injuries compared with healthy players. Twenty-two semi-professional players were divided into ACL-injured (n = 11) and non-injured groups (n = 11). Participants underwent maximal voluntary isometric contractions while electromyography activation, peak and average, and peak torque of force were measured. Results indicated significant differences in electromyography activation patterns between anterior cruciate ligament players and non-injured players, particularly in biceps femoris and gluteus maximus muscles. These differences were also evident when comparing between limbs within anterior cruciate ligament players. Interestingly, both groups exhibited similar peak torque of force during maximal contractions, suggesting a compensatory neuromuscular strategy that supports a return to sport based on kinetic and kinematic factors. However, these findings underscore persistent muscle integration imbalances potentially contributing to the high rate of anterior cruciate ligament reinjury. In conclusion, this study highlights the importance of evaluating electromyography activation alongside force development in understanding neuromuscular adaptations post anterior cruciate ligament injury. These insights emphasize the need for comprehensive rehabilitation strategies that address muscle imbalance to mitigate the risk of recurrent anterior cruciate ligament injuries in female football players. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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16 pages, 1867 KiB

Open AccessArticle

Immersive Virtual Reality High-Intensity Aerobic Training to Slow Parkinson’s Disease: The ReViPark Program

by Gustavo Rodríguez-Fuentes, Pablo Campo-Prieto and José Ma Cancela-Carral

Appl. Sci. 2024, 14(11), 4708; https://doi.org/10.3390/app14114708 - 30 May 2024

Cited by 5 | Viewed by 2230

Abstract

Intense exercise can be neuromodulatory and neuroprotective against Parkinson’s disease (PD). Indoor cycling is a good and safe option for this population, but some barriers (monotonous and repetitive environment, lack of monitoring, and an inability to compete with others) could limit its use. Immersive virtual reality (IVR) could be a possible solution to some of these drawbacks, increasing patient motivation and thus adherence to forced exercise programs using cycloergometers. A double-blind, randomized, controlled clinical trial was conducted to explore the feasibility and effects of the ReViPark program (high-intensity pedaling + IVR for 3 months with two sessions per week) on PD progression. In all, 52 people with PD were allocated to the ReViPark group (n = 30; 70.87 ± 6.67 years) and smart cycloergometer group (n = 22; 70.59 ± 6.67 years). This program was feasible, with no adverse effects (no significant Simulator Sickness Questionnaire symptoms and a low negative experience rating for the Game Experience Questionnaire (0.04/4)), no dropouts, high adherence, and good usability (System Usability Scale score of 82.90%). The ReViPark group showed significant improvements in functionality, quality of life, and disease progression as measured by their balance, gait and risk of falling (Timed Up and Go p = 0.028; Tinetti test p = 0.046), PDQ-39 scores (p = 0.035), and MDS-UPDRS scores (p = 0.001). This program is feasible and could lead to improvements in the functional parameters, quality of life, and symptomatology of the participants. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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Review

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15 pages, 1266 KiB

Open AccessReview

Digital-Focused Approaches in Cancer Patients’ Management in the Post-COVID Era: Challenges and Solutions

by Ilona Georgescu, Anica Dricu, Stefan-Alexandru Artene, Nicolae-Răzvan Vrăjitoru, Edmond Barcan, Daniela Elise Tache, Lucian-Ion Giubelan, Georgiana-Adeline Staicu, Elena-Victoria Manea (Carneluti), Cristina Pană and Stefana Oana Popescu (Purcaru)

Appl. Sci. 2024, 14(18), 8097; https://doi.org/10.3390/app14188097 - 10 Sep 2024

Cited by 1 | Viewed by 2250

Abstract

The COVID-19 pandemic has significantly accelerated the adoption of telemedicine and digital health technologies, revealing their immense potential in managing cancer patients effectively. This article explores the impact of recent technological developments and widened consumer perspectives on personalised healthcare and patient awareness, particularly in oncology. Smartphones and wearable devices have become integral to daily life, promoting healthy lifestyles and supporting cancer patients through remote monitoring and health management. The widespread use of these devices presents an unprecedented opportunity to transform clinical trials and patient care by offering convenient and accessible means of collecting health data continuously and non-invasively. However, to fully harness their potential, it is crucial to establish standardised methods for measuring patient metrics to ensure data reliability and validity. This article also addresses the challenges of integrating these technologies into clinical practice, such as cost, patient and professional reluctance, and technological oversaturation. It emphasises the need for continuous innovation, the development of robust digital infrastructures, and the importance of fostering a supportive environment to integrate these advancements permanently. Ultimately, the convergence of technological innovation and personalised healthcare promises to enhance patient outcomes, improve quality of life, and revolutionise cancer management in the post-COVID era. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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35 pages, 12412 KiB

Open AccessReview

PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review

by Didzis Lapsa, Rims Janeliukstis, Margus Metshein and Leo Selavo

Appl. Sci. 2024, 14(17), 7451; https://doi.org/10.3390/app14177451 - 23 Aug 2024

Cited by 5 | Viewed by 6449

Abstract

The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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Other

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9 pages, 879 KiB

Open AccessCase Report

Multisensory Stimulation in Amyotrophic Lateral Sclerosis Disease: Case Report of an Innovative Proposal through Immersive Virtual Reality

by Ángel Casal-Moldes, Pablo Campo-Prieto, Gustavo Rodríguez-Fuentes and José Mª Cancela-Carral

Appl. Sci. 2024, 14(20), 9238; https://doi.org/10.3390/app14209238 - 11 Oct 2024

Viewed by 1324

Abstract

Physical–cognitive stimulation has emerged as a promising strategy for improving the quality of life of patients with amyotrophic lateral sclerosis (ALS). This case study reports on the use of immersive virtual reality (IVR) as a tool for multisensory stimulation in a woman with ALS (76 years old; 11 years since diagnosis; stage 2). The program consisted of IVR stimulation sessions (three sessions per week for 12 weeks). The results showed that the implementation of the program was feasible and safe (no adverse symptoms on the Simulator Sickness Questionnaire), as well as easy to execute (>80% on the System Usability Scale). Additionally, the participant reported improvements in aspects related to her mental health (44% depression and 20% anxiety) and improvements in her quality of life, and she also maintained her values in her functional capacity. This study presents novel and important findings by demonstrating the feasibility of implementing physical–cognitive stimulation programs with IVR in a person with ALS, allowing for multisensory stimulation with commercially available hardware and software and the generation of benefits in their health-related quality of life and mental health. Full article

(This article belongs to the Special Issue Advances in Sensor-Based Devices and Wearables for Clinical Rehabilitation)

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