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Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy
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Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5710

Special Issue Editor

Dr. Vance Bergeron

E-Mail Website
Guest Editor
Centre National de la Recherche Scientifique (CNRS), Ecole Normale Supérieure de Lyon (ENSL), Lyon, France
Interests: neurorehabilitation; human–machine interfaces; functional electrical stimulation

Special Issue Information

Dear Colleagues,

Neurorehabilitation aims to restore and enhance motor and cognitive functions in individuals with neurological disorders. Here, we wish to delve into the advances, challenges, and future prospects of advanced sensor technologies in neurorehabilitation. Recent achievements in sensor technology have revolutionized the field, offering objective assessments and personalized interventions. This Special Issue provides an overview of advanced sensors for neurorehabilitation and their potential to transform traditional approaches.

Advanced sensors, such as wearables, robots, and neuroimaging technologies, enable real-time monitoring of physiological and biomechanical parameters. They quantify motor performance, assess neuromuscular activation, and evaluate brain activity, providing insights into intervention effectiveness. High-resolution data collected by these sensors can be analyzed using artificial intelligence and machine learning algorithms to enhance our understanding of neuroplastic processes.

By enabling personalized treatment plans, advanced sensors optimize neurorehabilitation and improve functional outcomes. Clinicians benefit from objective measures, allowing for precise progress monitoring and data-driven adjustments to therapy. Patients receive immediate feedback, enhancing engagement and motivation during rehabilitation.

In conclusion, advanced sensors in neurorehabilitation hold promise for transforming the field. Objective assessments, personalized interventions, and real-time feedback have the potential to revolutionize traditional rehabilitation, leading to improved patient outcomes and enhanced quality of life.

Dr. Vance Bergeron
Guest Editor

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

  • neurorehabilitation
  • advanced sensors
  • neural interfaces
  • rehabilitation technology
  • wearable sensors
  • human–machine interface

Published Papers (5 papers)

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Research

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21 pages, 2350 KiB  
Article
Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography
by Eira Lotta Spieker, Ardit Dvorani, Christina Salchow-Hömmen, Carolin Otto, Klemens Ruprecht, Nikolaus Wenger and Thomas Schauer
Sensors 2024, 24(2), 634; https://doi.org/10.3390/s24020634 - 19 Jan 2024
Viewed by 946
Abstract
Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For [...] Read more.
Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use. Full article
(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)
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15 pages, 7098 KiB  
Article
Application of Dynamic Mode Decomposition to Characterize Temporal Evolution of Plantar Pressures from Walkway Sensor Data in Women with Cancer
by Kangjun Seo, Hazem H. Refai and Elizabeth S. Hile
Sensors 2024, 24(2), 486; https://doi.org/10.3390/s24020486 - 12 Jan 2024
Viewed by 887
Abstract
Pressure sensor-impregnated walkways transform a person’s footfalls into spatiotemporal signals that may be sufficiently complex to inform emerging artificial intelligence (AI) applications in healthcare. Key consistencies within these plantar signals show potential to uniquely identify a person, and to distinguish groups with and [...] Read more.
Pressure sensor-impregnated walkways transform a person’s footfalls into spatiotemporal signals that may be sufficiently complex to inform emerging artificial intelligence (AI) applications in healthcare. Key consistencies within these plantar signals show potential to uniquely identify a person, and to distinguish groups with and without neuromotor pathology. Evidence shows that plantar pressure distributions are altered in aging and diabetic peripheral neuropathy, but less is known about pressure dynamics in chemotherapy-induced peripheral neuropathy (CIPN), a condition leading to falls in cancer survivors. Studying pressure dynamics longitudinally as people develop CIPN will require a composite model that can accurately characterize a survivor’s gait consistencies before chemotherapy, even in the presence of normal step-to-step variation. In this paper, we present a state-of-the-art data-driven learning technique to identify consistencies in an individual’s plantar pressure dynamics. We apply this technique to a database of steps taken by each of 16 women before they begin a new course of neurotoxic chemotherapy for breast or gynecologic cancer. After extracting gait features by decomposing spatiotemporal plantar pressure data into low-rank dynamic modes characterized by three features: frequency, a decay rate, and an initial condition, we employ a machine-learning model to identify consistencies in each survivor’s walking pattern using the centroids for each feature. In this sample, our approach is at least 86% accurate for identifying the correct individual using their pressure dynamics, whether using the right or left foot, or data from trials walked at usual or fast speeds. In future work, we suggest that persistent deviation from a survivor’s pre-chemotherapy step consistencies could be used to automate the identification of peripheral neuropathy and other chemotherapy side effects that impact mobility. Full article
(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)
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Review

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14 pages, 644 KiB  
Review
Transcranial Direct Current Stimulation in the Treatment of Gait Disturbance in Post-Stroke Patients: An Overview of Systematic Reviews
by Juan Antonio Chamorro-Hinojosa, Francisco Molina-Rueda and María Carratalá-Tejada
Sensors 2023, 23(23), 9301; https://doi.org/10.3390/s23239301 - 21 Nov 2023
Viewed by 910
Abstract
Introduction: Transcranial direct current stimulation (tDCS) is a promising technique for brain modulation after a cerebrovascular accident (CVA). This treatment modality has been previously studied in the recovery of patients. The aim of this review is to analyse the evidence for the application [...] Read more.
Introduction: Transcranial direct current stimulation (tDCS) is a promising technique for brain modulation after a cerebrovascular accident (CVA). This treatment modality has been previously studied in the recovery of patients. The aim of this review is to analyse the evidence for the application of tDCS in the recovery of gait disturbance in stroke patients. Methods: This review was conducted according to the recommendations of the PRISMA statement. Three different electronic databases were searched for relevant results: PubMed, Scopus, and Cochrane, from 2015 to January 2022. We included reviews and meta-analyses that only considered randomised controlled trials (RCTs) that investigated the effects of transcranial direct current stimulation, in combination or not with other physiotherapy treatments, compared to no treatment, usual care, or alternative treatment on gait recovery. Our primary outcomes of interest were walking speed, mobility, and endurance; secondary outcomes included motor function. Results: Thirteen studies with a total of 195 RCTs were included. Data on population, outcome measures, protocols, and outcomes were extracted. The Amstar-2 scale and the GRADE system of certainty of evidence were used. Only one study received high certainty of evidence, 5 received low certainty of evidence, and 7 received critically low certainty of evidence. Moderate to low-quality evidence showed a beneficial effect of tDCS on gait parameters, but not significantly. Conclusions: Although the tDCS produces positive changes in gait recovery in spatio-temporal parameters such as mobility, endurance, strength, and motor function, there is insufficient evidence to recommend this treatment. Higher-quality studies with larger sample sizes are needed for stronger conclusions. Full article
(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)
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30 pages, 2323 KiB  
Review
Clinical Static Balance Assessment: A Narrative Review of Traditional and IMU-Based Posturography in Older Adults and Individuals with Incomplete Spinal Cord Injury
by Alireza Noamani, Negar Riahi, Albert H. Vette and Hossein Rouhani
Sensors 2023, 23(21), 8881; https://doi.org/10.3390/s23218881 - 01 Nov 2023
Viewed by 1166
Abstract
Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual’s quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms [...] Read more.
Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual’s quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms of the balance deficiencies, and developing targeted interventions to improve standing balance and prevent falls. This review paper first explores the methods to quantify standing balance. Then, it reviews traditional posturography and recent advancements in using wearable inertial measurement units (IMUs) to assess static balance in two populations: older adults and those with incomplete spinal cord injury (iSCI). The inclusion of these two groups is supported by their large representation among individuals with balance impairments. Also, each group exhibits distinct aspects in balance assessment due to diverse underlying causes associated with aging and neurological impairment. Given the high vulnerability of both demographics to balance impairments and falls, the significance of targeted interventions to improve standing balance and mitigate fall risk becomes apparent. Overall, this review highlights the importance of static balance assessment and the potential of emerging methods and technologies to improve our understanding of postural control in different populations. Full article
(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)
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Other

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23 pages, 2517 KiB  
Systematic Review
Effectiveness of Water-Based Exercise in Patients with Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis
by María Jesús Benzo-Iglesias, Patricia Rocamora-Pérez, María Ángeles Valverde-Martínez, Amelia Victoria García-Luengo and Remedios López-Liria
Sensors 2023, 23(20), 8557; https://doi.org/10.3390/s23208557 - 18 Oct 2023
Viewed by 1439
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disease that, due to dyspnea, decreases patients’ physical function and quality of life. The aim of the research was to evaluate the effectiveness of water-based exercise (WE) in improving functional capacity and respiratory muscle [...] Read more.
Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disease that, due to dyspnea, decreases patients’ physical function and quality of life. The aim of the research was to evaluate the effectiveness of water-based exercise (WE) in improving functional capacity and respiratory muscle strength in patients with COPD. It consisted of a systematic review and meta-analysis of eight randomized clinical trials (RCTs) from the last 10 years, found in PubMed, PEDro, Scopus and Web of Science databases. Methodological quality was analyzed using the PEDro scale and the Cochrane Collaboration Risk of Bias Tool. Regarding the evaluation of functional capacity, mainly assessed were lung function, respiratory muscle strength, and maximal or aerobic exercise. The results showed that WE improves functional capacity compared to a non-exercising control group (SMD: 73.42; IC 95%: 40.40 to 106.45; I2: 0%). There are no statistically significant differences between a WE treatment and a land exercise (LE) treatment (p = 0.24) in functional capacity, nor with respect to respiratory muscle strength (p = 0.97). These data should be interpreted with caution, as more RCTs with aquatic intervention in COPD patients are needed to elucidate whether there are differences between WE or LE according to patient characteristics and comorbidities. Full article
(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)
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