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Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy

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

Deadline for manuscript submissions: closed (25 January 2025) | Viewed by 23779

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Dr. Vance Bergeron

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

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Keywords

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

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Related Special Issue

Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy—2nd Edition in Sensors

Published Papers (10 papers)

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Research

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17 pages, 873 KiB

Open AccessArticle

Multi-Class Detection of Neurodegenerative Diseases from EEG Signals Using Lightweight LSTM Neural Networks

by Laura Falaschetti, Giorgio Biagetti, Michele Alessandrini, Claudio Turchetti, Simona Luzzi and Paolo Crippa

Sensors 2024, 24(20), 6721; https://doi.org/10.3390/s24206721 - 19 Oct 2024

Cited by 2 | Viewed by 1982

Abstract

Neurodegenerative diseases severely impact the life of millions of patients worldwide, and their occurrence is more and more increasing proportionally to longer life expectancy. Electroencephalography has become an important diagnostic tool for these diseases, due to its relatively simple procedure, but it requires analyzing a large number of data, often carrying a small fraction of informative content. For this reason, machine learning tools have gained a considerable relevance as an aid to classify potential signs of a specific disease, especially in its early stages, when treatments can be more effective. In this work, long short-term memory-based neural networks with different numbers of units were properly designed and trained after accurate data pre-processing, in order to perform a multi-class detection. To this end, a custom dataset of EEG recordings from subjects affected by five neurodegenerative diseases (Alzheimer’s disease, frontotemporal dementia, dementia with Lewy bodies, progressive supranuclear palsy, and vascular dementia) was acquired. Experimental results show that an accuracy up to 98% was achieved with data belonging to different classes of disease, up to six including the control group, while not requiring particularly heavy computational resources. Full article

(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)

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15 pages, 4183 KiB

Open AccessArticle

Pressure Sensors for Evaluating Hand Grasp and Pinch

by Vance Bergeron and Petar Kajganic

Sensors 2024, 24(17), 5768; https://doi.org/10.3390/s24175768 - 5 Sep 2024

Cited by 1 | Viewed by 2731

Abstract

This study addresses the need for highly sensitive tools to evaluate hand strength, particularly grasp and pinch strength, which are vital for diagnosing and rehabilitating conditions affecting hand function. Current devices like the Jamar dynamometer and Martin Vigorimeter, although reliable, fail to measure extremely low force or pressure values required for individuals with severe hand impairments. This research introduces a novel device, a modified Martin Vigorimeter, utilizing an ultra-soft latex chamber and differential pressure measurement to detect minute pressure changes, thus significantly enhancing sensitivity. The device offers a cost-effective solution, making advanced hand strength evaluation more accessible for clinical and research applications. Future research should validate its accuracy across diverse populations and settings, exploring its broader implications for hand rehabilitation and occupational health. Full article

(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)

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13 pages, 1455 KiB

Open AccessArticle

Object Weight and Hand Dominance Impact Kinematics in a Functional Reach-to-Drink Task in School-Aged Children

by Julia Mazzarella, Daniel Richie, Ajit M. W. Chaudhari, Xueliang Pan, Eloisa Tudella, Colleen K. Spees and Jill C. Heathcock

Sensors 2024, 24(16), 5421; https://doi.org/10.3390/s24165421 - 22 Aug 2024

Viewed by 1447

Abstract

This study evaluates the effects of object weight and hand dominance on the end-point kinematics of the hand-to-mouth (withdrawal) movement in a functional reach-to-drink task for typically developing school-aged children. Using 3D motion capture, speed (average velocity and peak velocity), straightness (ratio), and smoothness (number of velocity peaks and log dimensionless jerk) of hand movements were calculated for the withdrawal motion with three different bottle weights (empty, half-filled, and full). Average velocity (550.4 ± 142.0 versus 512.1 ± 145.6 mm/s) and peak velocity (916.3 ± 234 versus 842.7 ± 198.4 mm/s) were significantly higher with the empty versus half-filled bottle and with the non-dominant (average: 543.5 ± 145.2 mm/s; peak: 896.5 ± 207 mm/s) versus dominant (average: 525.2 ± 40.7 mm/s; peak: 864.2 ± 209.2 mm/s) hand. There were no differences in straightness or smoothness. These findings indicate that increasing weight in reach-to-drink task puts greater constraints on the task. The slower movements with the dominant hand might denote better precision control than the non-dominant hand. The quantitative motion capture results show average values for the kinematic variables for a functional reach-to-drink task in a typically developing population of school-aged children with changing weights of the bottles that are relevant to a real-life scenario. These results could inform the design of individualized therapeutic interventions to improve functional upper-extremity use in children with neurodevelopmental motor disorders. Full article

(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)

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17 pages, 8105 KiB

Open AccessArticle

Synchronization of Neurophysiological and Biomechanical Data in a Real-Time Virtual Gait Analysis System (GRAIL): A Proof-of-Principle Study

by Stefan A. Maas, Tim Göcking, Robert Stojan, Claudia Voelcker-Rehage and Dieter F. Kutz

Sensors 2024, 24(12), 3779; https://doi.org/10.3390/s24123779 - 11 Jun 2024

Cited by 3 | Viewed by 1955

Abstract

The investigation of gait and its neuronal correlates under more ecologically valid conditions as well as real-time feedback visualization is becoming increasingly important in neuro-motor rehabilitation research. The Gait Real-time Analysis Interactive Lab (GRAIL) offers advanced opportunities for gait and gait-related research by creating more naturalistic yet controlled environments through immersive virtual reality. Investigating the neuronal aspects of gait requires parallel recording of brain activity, such as through mobile electroencephalography (EEG) and/or mobile functional near-infrared spectroscopy (fNIRS), which must be synchronized with the kinetic and /or kinematic data recorded while walking. This proof-of-concept study outlines the required setup by use of the lab streaming layer (LSL) ecosystem for real-time, simultaneous data collection of two independently operating multi-channel EEG and fNIRS measurement devices and gait kinetics. In this context, a customized approach using a photodiode to synchronize the systems is described. This study demonstrates the achievable temporal accuracy of synchronous data acquisition of neurophysiological and kinematic and kinetic data collection in the GRAIL. By using event-related cerebral hemodynamic activity and visually evoked potentials during a start-to-go task and a checkerboard test, we were able to confirm that our measurement system can replicate known physiological phenomena with latencies in the millisecond range and relate neurophysiological and kinetic data to each other with sufficient accuracy. Full article

(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)

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21 pages, 2350 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 2249

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 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

Open AccessArticle

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 1956

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 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

Open AccessReview

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

Cited by 1 | Viewed by 2092

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 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

Open AccessReview

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 - 1 Nov 2023

Cited by 6 | Viewed by 3940

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 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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35 pages, 5838 KiB

Open AccessSystematic Review

A Bibliometric Review of Brain–Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics

by Nayibe Chio and Eduardo Quiles-Cucarella

Sensors 2025, 25(1), 154; https://doi.org/10.3390/s25010154 - 30 Dec 2024

Viewed by 1135

Abstract

In this paper, a bibliometric review is conducted on brain–computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain–computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024. Full article

(This article belongs to the Special Issue Advanced Sensors for Neurorehabilitation: Empowering Precision and Personalized Therapy)

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23 pages, 2517 KiB

Open AccessSystematic 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

Cited by 3 | Viewed by 3780

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 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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