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16 pages, 1463 KiB

Open AccessArticle

Effect of Sampling Rate, Filtering, and Torque Onset Detection on Quadriceps Rate of Torque Development and Torque Steadiness

by McKenzie S. White, Megan C. Graham, Tereza Janatova, Gregory S. Hawk, Katherine L. Thompson and Brian Noehren

Sensors 2024, 24(13), 4250; https://doi.org/10.3390/s24134250 - 30 Jun 2024

Viewed by 522

Abstract

Quadriceps rate of torque development (RTD) and torque steadiness are valuable metrics for assessing explosive strength and the ability to control force over a sustained period of time, which can inform clinical assessments of knee function. Despite their widespread use, there is a [...] Read more.

Quadriceps rate of torque development (RTD) and torque steadiness are valuable metrics for assessing explosive strength and the ability to control force over a sustained period of time, which can inform clinical assessments of knee function. Despite their widespread use, there is a significant gap in standardized methodology for measuring these metrics, which limits their utility in comparing outcomes across different studies and populations. To address these gaps, we evaluated the influence of sampling rates, signal filtering, and torque onset detection on RTD and torque steadiness. Twenty-seven participants with a history of a primary anterior cruciate ligament reconstruction (N = 27 (11 male/16 female), age = 23 ± 8 years, body mass index = 26 ± 4 kg/m²) and thirty-two control participants (N = 32 (13 male/19 female), age = 23 ± 7 years, body mass index = 23 ± 3 kg/m²) underwent isometric quadriceps strength testing, with data collected at 2222 Hz on an isokinetic dynamometer. The torque–time signal was downsampled to approximately 100 and 1000 Hz and processed using a low-pass, zero-lag Butterworth filter with a range of cutoff frequencies spanning 10–200 Hz. The thresholds used to detect torque onset were defined as 0.1 Nm, 1 Nm, and 5 Nm. RTD between 0 and 100 ms, 0 and 200 ms, and 40–160 ms was computed, as well as absolute and relative torque steadiness. Relative differences were computed by comparing all outcomes to the “gold standard” values computed, with a sampling rate of 2222 Hz, a cutoff frequency in the low-pass filter of 150 Hz, and torque onset of 1 Nm, and compared utilizing linear mixed models. While all combinations of signal collection and processing parameters reached statistical significance (p < 0.05), these differences were consistent between injured and control limbs. Additionally, clinically relevant differences (+/−10%) were primarily observed through torque onset detection methods and primarily affected RTD between 0 and 100 ms. Although measurements of RTD and torque steadiness were generally robust against diverse signal collection and processing parameters, the selection of torque onset should be carefully considered, especially in early RTD assessments that have shorter time epochs. Full article

(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)

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15 pages, 1554 KiB

Open AccessArticle

The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability

by Pnina Marom, Michael Brik, Nirit Agay, Rachel Dankner, Zoya Katzir, Naama Keshet and Dana Doron

Sensors 2024, 24(11), 3594; https://doi.org/10.3390/s24113594 - 2 Jun 2024

Viewed by 519

Abstract

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower [...] Read more.

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen’s d, comparing the application’s readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89–1) and good-to-excellent reliability (ICC range, 0.77–1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58–1). Only ‘step length of the impaired leg’ showed poor-to-good validity (range, 0.37–0.84). Cohen’s d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability. Full article

(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)

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11 pages, 4545 KiB

Open AccessCommunication

Testing Dynamic Balance in People with Multiple Sclerosis: A Correlational Study between Standard Posturography and Robotic-Assistive Device

by Jessica Podda, Giorgia Marchesi, Alice Bellosta, Valentina Squeri, Alice De Luca, Ludovico Pedullà, Andrea Tacchino and Giampaolo Brichetto

Sensors 2024, 24(11), 3325; https://doi.org/10.3390/s24113325 - 23 May 2024

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Abstract

Background: Robotic devices are known to provide pivotal parameters to assess motor functions in Multiple Sclerosis (MS) as dynamic balance. However, there is still a lack of validation studies comparing innovative technologies with standard solutions. Thus, this study’s aim was to compare the [...] Read more.

Background: Robotic devices are known to provide pivotal parameters to assess motor functions in Multiple Sclerosis (MS) as dynamic balance. However, there is still a lack of validation studies comparing innovative technologies with standard solutions. Thus, this study’s aim was to compare the postural assessment of fifty people with MS (PwMS) during dynamic tasks performed with the gold standard EquiTest^® and the robotic platform hunova^®, using Center of Pressure (COP)-related parameters and global balance indexes. Methods: Pearson’s ρ correlations were run for each COP-related measure and the global balance index was computed from EquiTest^® and hunova^® in both open (EO) and closed-eyes (EC) conditions. Results: Considering COP-related parameters, all correlations were significant in both EO (0.337 ≤ ρ ≤ 0.653) and EC (0.344 ≤ ρ ≤ 0.668). Furthermore, Pearson’s analysis of global balance indexes revealed relatively strong for visual and vestibular, and strong for somatosensory system associations (ρ = 0.573; ρ = 0.494; ρ = 0.710, respectively). Conclusions: Findings confirm the use of hunova^® as a valid device for dynamic balance assessment in MS, suggesting that such a robotic platform could allow for a more sensitive assessment of balance over time, and thus a better evaluation of the effectiveness of personalized treatment, thereby improving evidence-based clinical practice. Full article

(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)

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11 pages, 1172 KiB

Open AccessArticle

A Statistical and AI Analysis of the Frequency Spectrum in the Measurement of the Center of Pressure Track in the Seated Position in Healthy Subjects and Subjects with Low Back Pain

by Jan Jens Koltermann, Philipp Floessel, Franziska Hammerschmidt and Alexander C. Disch

Sensors 2024, 24(10), 3011; https://doi.org/10.3390/s24103011 - 9 May 2024

Viewed by 510

Abstract

Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and [...] Read more.

Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and consideration of the results in the spectrum in conjunction with an AI-supported evaluation could represent an alternative diagnostic method quantifying neuromuscular control. In a prospective cross-sectional study, 188 subjects aged between 18 and 60 years were recruited and divided into two groups: “LowBackPain” vs. “Healthy”. Subsequently, measurements of postural control in a seated position were carried out for 60 s using a modified balance board. A spectrum per trail was calculated using the measured CoP tracks in the range from 0.01 to 10 Hz. Various algorithms for data classification and prediction of these classes were tested for the parameter combination with the highest proven static influence on the parameter pain. The best results were found in a frequency spectrum of 0.001 Hz and greater than 1 Hz. After transforming the track from the time domain to the image domain for representation as power density, the influence of pain was highly significant (effect size 0.9). The link between pain and gender (p = 0.015) and pain and height (p = 0.012) also demonstrated significant results. The assessment of postural control in a seated position allows differentiation between “LowBackPain” and “Healthy” subjects. Using the AI algorithm of neural networks, the data set can be correctly differentiated into “LowBackPain” and “Healthy” with a probability of 81%. Full article

(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)

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11 pages, 1233 KiB

Open AccessArticle

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 733

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

Open AccessArticle

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 - 6 Feb 2024

Cited by 1 | Viewed by 1080

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

Open AccessArticle

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 1012

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

Open AccessArticle

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

Cited by 2 | Viewed by 1266

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

Open AccessArticle

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 1911

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

Open AccessArticle

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 1900

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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