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Advanced Sensors in Biomechanics and Rehabilitation
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Advanced Sensors in Biomechanics and Rehabilitation

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 6429

Special Issue Editor

Prof. Dr. Thurmon Lockhart

E-Mail Website
Guest Editor
School of Biological and Health Systems Engineering, Ira A Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
Interests: gait and posture; activity monitoring; fall risk assessment; nonlinear dynamics; biodynamics; wireless inertial sensors; wearables; musculoskeletal and neuro-rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to unraveling the transformative role of sensor technologies in the field of biomechanics and rehabilitation. In an era where precision and personalized care are paramount, these sophisticated tools significantly enhance our ability to understand human motion mechanics and develop effective therapeutic interventions.

The adoption of sensors in biomechanical studies allows for detailed analyses of body posture, gait, muscle activation, and joint kinematics, leading to more accurate diagnoses and tailored treatments. Moreover, in the context of rehabilitation, sensors facilitate the real-time tracking of patients' functional abilities, thus informing clinicians on the efficacy of therapeutic approaches and guiding necessary adjustments. Wearable sensors, for instance, can provide valuable insights into patients' daily activities, helping determine the effectiveness of prescribed exercises and further enhancing rehabilitation outcomes.

This Special Issue aims to shed light on current research exploring the application of sensing technology in biomechanics and rehabilitation. We invite you to submit original research papers to the issue.

You may choose our Joint Special Issue in J.

Prof. Dr. Thurmon Lockhart
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

  • wearable sensors
  • biomechanics
  • rehabilitation
  • gait analysis
  • posture

Published Papers (6 papers)

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Research

11 pages, 1233 KiB  
Article
Evaluating Postural Transition Movement Performance in Individuals with Essential Tremor via the Instrumented Timed Up and Go
by Patrick G. Monaghan, William M. Murrah, Harrison C. Walker, Kristina A. Neely and Jaimie A. Roper
Sensors 2024, 24(7), 2216; https://doi.org/10.3390/s24072216 - 29 Mar 2024
Viewed by 515
Abstract
Flexibility in performing various movements like standing, walking, and turning is crucial for navigating dynamic environments in daily life. Individuals with essential tremor often experience movement difficulties that can affect these postural transitions, limiting mobility and independence. Yet, little research has examined the [...] Read more.
Flexibility in performing various movements like standing, walking, and turning is crucial for navigating dynamic environments in daily life. Individuals with essential tremor often experience movement difficulties that can affect these postural transitions, limiting mobility and independence. Yet, little research has examined the performance of postural transitions in people with essential tremor. Therefore, we assessed postural transition performance using two versions of the timed up and go test: the standard version and a more complex water-carry version. We examined the total duration of the standard and water-carry timed up and go in 15 people with and 15 people without essential tremor. We also compared the time taken for each phase (sit-to-stand phase, straight-line walk phase, stand-to-sit phase) and the turning velocity between groups. Our findings revealed decreased performance across all phases of standard and water-carry timed up and go assessments. Further, both ET and non-ET groups exhibited reduced performance during the water-carry timed up and go compared to the standard timed up and go. Evaluating specific phases of the timed up and go offers valuable insights into functional movement performance in essential tremor, permitting more tailored therapeutic interventions to improve functional performance during activities of daily living. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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13 pages, 2683 KiB  
Article
Propulsive Force Modulation Drives Split-Belt Treadmill Adaptation in People with Multiple Sclerosis
by Andrew C. Hagen, Christopher M. Patrick, Isaac E. Bast and Brett W. Fling
Sensors 2024, 24(4), 1067; https://doi.org/10.3390/s24041067 - 06 Feb 2024
Viewed by 799
Abstract
Most people with multiple sclerosis (PwMS) experience significant gait asymmetries between their legs during walking, leading to an increased risk of falls. Split-belt treadmill training, where the speed of each limb is controlled independently, alters each leg’s stepping pattern and can improve gait [...] Read more.
Most people with multiple sclerosis (PwMS) experience significant gait asymmetries between their legs during walking, leading to an increased risk of falls. Split-belt treadmill training, where the speed of each limb is controlled independently, alters each leg’s stepping pattern and can improve gait symmetry in PwMS. However, the biomechanical mechanisms of this adaptation in PwMS remain poorly understood. In this study, 32 PwMS underwent a 10 min split-belt treadmill adaptation paradigm with the more affected (MA) leg moving twice as fast as the less affected (LA) leg. The most noteworthy biomechanical adaptation observed was increased peak propulsion asymmetry between the limbs. A kinematic analysis revealed that peak dorsiflexion asymmetry and the onset of plantarflexion in the MA limb were the primary contributors to the observed increases in peak propulsion. In contrast, the joints in the LA limb underwent only immediate reactive adjustments without subsequent adaptation. These findings demonstrate that modulation during gait adaptation in PwMS occurs primarily via propulsive forces and joint motions that contribute to propulsive forces. Understanding these distinct biomechanical changes during adaptation enhances our grasp of the rehabilitative impact of split-belt treadmill training, providing insights for refining therapeutic interventions aimed at improving gait symmetry. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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11 pages, 4052 KiB  
Article
Reliability and Validity of Shore Hardness in Plantar Soft Tissue Biomechanics
by Redent Tonna, Panagiotis E. Chatzistergos, Otis Wyatt and Nachiappan Chockalingam
Sensors 2024, 24(2), 539; https://doi.org/10.3390/s24020539 - 15 Jan 2024
Viewed by 725
Abstract
Shore hardness (SH) is a cost-effective and easy-to-use method to assess soft tissue biomechanics. Its use for the plantar soft tissue could enhance the clinical management of conditions such as diabetic foot complications, but its validity and reliability remain unclear. Twenty healthy adults [...] Read more.
Shore hardness (SH) is a cost-effective and easy-to-use method to assess soft tissue biomechanics. Its use for the plantar soft tissue could enhance the clinical management of conditions such as diabetic foot complications, but its validity and reliability remain unclear. Twenty healthy adults were recruited for this study. Validity and reliability were assessed across six different plantar sites. The validity was assessed against shear wave (SW) elastography (the gold standard). SH was measured by two examiners to assess inter-rater reliability. Testing was repeated following a test/retest study design to assess intra-rater reliability. SH was significantly correlated with SW speed measured in the skin or in the microchamber layer of the first metatarsal head (MetHead), third MetHead and rearfoot. Intraclass correlation coefficients and Bland–Altman plots of limits of agreement indicated satisfactory levels of reliability for these sites. No significant correlation between SH and SW elastography was found for the hallux, 5th MetHead or midfoot. Reliability for these sites was also compromised. SH is a valid and reliable measurement for plantar soft tissue biomechanics in the first MetHead, the third MetHead and the rearfoot. Our results do not support the use of SH for the hallux, 5th MetHead or midfoot. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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20 pages, 58464 KiB  
Article
Comparison of Shoulder Range of Motion Quantified with Mobile Phone Video-Based Skeletal Tracking and 3D Motion Capture—Preliminary Study
by Wolbert van den Hoorn, Maxence Lavaill, Kenneth Cutbush, Ashish Gupta and Graham Kerr
Sensors 2024, 24(2), 534; https://doi.org/10.3390/s24020534 - 15 Jan 2024
Viewed by 946
Abstract
Background: The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Methods: Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation [...] Read more.
Background: The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Methods: Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple’s vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis. Results: We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements. Conclusions: Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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23 pages, 990 KiB  
Article
Error Enhancement for Upper Limb Rehabilitation in the Chronic Phase after Stroke: A 5-Day Pre-Post Intervention Study
by Marjan Coremans, Eli Carmeli, Ineke De Bauw, Bea Essers, Robin Lemmens and Geert Verheyden
Sensors 2024, 24(2), 471; https://doi.org/10.3390/s24020471 - 12 Jan 2024
Viewed by 1495
Abstract
A large proportion of chronic stroke survivors still struggle with upper limb (UL) problems in daily activities, typically reaching tasks. During three-dimensional reaching movements, the deXtreme robot offers error enhancement forces. Error enhancement aims to improve the quality of movement. We investigated clinical [...] Read more.
A large proportion of chronic stroke survivors still struggle with upper limb (UL) problems in daily activities, typically reaching tasks. During three-dimensional reaching movements, the deXtreme robot offers error enhancement forces. Error enhancement aims to improve the quality of movement. We investigated clinical and patient-reported outcomes and assessed the quality of movement before and after a 5 h error enhancement training with the deXtreme robot. This pilot study had a pre-post intervention design, recruiting 22 patients (mean age: 57 years, mean days post-stroke: 1571, male/female: 12/10) in the chronic phase post-stroke with UL motor impairments. Patients received 1 h robot treatment for five days and were assessed at baseline and after training, collecting (1) clinical, (2) patient-reported, and (3) kinematic (KINARM, BKIN Technologies Ltd., Kingston, ON, Canada) outcome measures. Our analysis revealed significant improvements (median improvement (Q1–Q3)) in (1) UL Fugl–Meyer assessment (1.0 (0.8–3.0), p < 0.001) and action research arm test (2.0 (0.8–2.0), p < 0.001); (2) motor activity log, amount of use (0.1 (0.0–0.3), p < 0.001) and quality of use (0.1 (0.1–0.5), p < 0.001) subscale; (3) KINARM-evaluated position sense (−0.45 (−0.81–0.09), p = 0.030) after training. These findings provide insight into clinical self-reported and kinematic improvements in UL functioning after five hours of error enhancement UL training. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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13 pages, 1082 KiB  
Article
Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis
by Mailyn Calderón-Díaz, Rony Silvestre Aguirre, Juan P. Vásconez, Roberto Yáñez, Matías Roby, Marvin Querales and Rodrigo Salas
Sensors 2024, 24(1), 119; https://doi.org/10.3390/s24010119 - 25 Dec 2023
Viewed by 1463
Abstract
There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSIs) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days [...] Read more.
There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSIs) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams have difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify the biomarkers of muscle injuries in professional soccer players through biomechanical analysis, employing several ML algorithms such as decision tree (DT) methods, discriminant methods, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble methods, boosted and bagged trees, artificial neural networks (ANNs), and XGBoost. In particular, XGBoost is also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regard to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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