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Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement

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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: 30 November 2024 | Viewed by 16689

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Special Issue Editors

Dr. Pedro Pezarat-Correia

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Guest Editor

Neuromuscular Research Lab, Faculty of Human Kinetics, Lisbon University, 1499-002 Lisbon, Portugal
Interests: neuromuscular function; muscle coordination; electromyography; strength training; kinesiology
Special Issues, Collections and Topics in MDPI journals

Dr. João R. Vaz

E-Mail Website
Guest Editor

Neuromuscular Research Lab, Faculty of Human Kinetics, Lisbon University, 1499-002 Lisbon, Portugal
Interests: sports medicine; biomechanics; motor control; human movement variability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The study of neuromuscular coordination involves investigating neural and muscular mechanisms to explain physiological and biomechanical adaptations that result from training and rehabilitation, injury and/or disease. It covers a broad area in human movement: from sports and exercise, rehabilitation and motor learning to ergonomics and orthotic devices development.

The massive ongoing technological development, namely in sensors development and computer science, has been allowing the continuous in-depth understanding of the neuromuscular function. For example, high-density surface electromyography and shear wave elastography are two technologies that are expanding the current knowledge of neural and muscular mechanisms. Similarly, machine learning and deep learning algorithms also significantly contribute to understanding complex physiological phenomena that relate neuromuscular coordination with disease.

This Special Issue aims to invite contributions on the more recent developments and advances of biosensors and innovative methodological approaches applied to investigate neuromuscular mechanisms and how they adapt to different contexts of human movement: e.g., physical exercise, strength training, motor learning, rehabilitation, movement disorders and injury, fatigue, and aging.

We are open to receiving submissions of both review and original research articles that involve the use of sensors that quantify neural- and muscular-related parameters with a particular emphasis on neuromuscular coordination.

Dr. Pedro Pezarat-Correia
Dr. João R. Vaz
Guest Editors

Manuscript Submission Information

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Keywords

biosensors
signal processing
machine learning
central nervous system
neurophysiology
biomechanics
sports and exercise
sports medicine
strength training
rehabilitation
motor control

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Published Papers (7 papers)

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Research

14 pages, 7272 KiB

Open AccessArticle

Distributed Wearable Ultrasound Sensors Predict Isometric Ground Reaction Force

by Erica L. King, Shriniwas Patwardhan, Ahmed Bashatah, Meghan Magee, Margaret T. Jones, Qi Wei, Siddhartha Sikdar and Parag V. Chitnis

Sensors 2024, 24(15), 5023; https://doi.org/10.3390/s24155023 - 3 Aug 2024

Viewed by 1202

Abstract

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R² of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation. Full article

(This article belongs to the Special Issue Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement)

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16 pages, 1791 KiB

Open AccessArticle

Comparing Video Analysis to Computerized Detection of Limb Position for the Diagnosis of Movement Control during Back Squat Exercise with Overload

by André B. Peres, Andrei Sancassani, Eliane A. Castro, Tiago A. F. Almeida, Danilo A. Massini, Anderson G. Macedo, Mário C. Espada, Víctor Hernández-Beltrán, José M. Gamonales and Dalton M. Pessôa Filho

Sensors 2024, 24(6), 1910; https://doi.org/10.3390/s24061910 - 16 Mar 2024

Cited by 2 | Viewed by 1144

Abstract

Incorrect limb position while lifting heavy weights might compromise athlete success during weightlifting performance, similar to the way that it increases the risk of muscle injuries during resistance exercises, regardless of the individual’s level of experience. However, practitioners might not have the necessary background knowledge for self-supervision of limb position and adjustment of the lifting position when improper movement occurs. Therefore, the computerized analysis of movement patterns might assist people in detecting changes in limb position during exercises with different loads or enhance the analysis of an observer with expertise in weightlifting exercises. In this study, hidden Markov models (HMMs) were employed to automate the detection of joint position and barbell trajectory during back squat exercises. Ten volunteers performed three lift movements each with a 0, 50, and 75% load based on body weight. A smartphone was used to record the movements in the sagittal plane, providing information for the analysis of variance and identifying significant position changes by video analysis (p < 0.05). Data from individuals performing the same movements with no added weight load were used to train the HMMs to identify changes in the pattern. A comparison of HMMs and human experts revealed between 40% and 90% agreement, indicating the reliability of HMMs for identifying changes in the control of movements with added weight load. In addition, the results highlighted that HMMs can detect changes imperceptible to the human visual analysis. Full article

(This article belongs to the Special Issue Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement)

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9 pages, 1978 KiB

Open AccessCommunication

Novel 3D Force Sensors for a Cost-Effective 3D Force Plate for Biomechanical Analysis

by Jonathan D. Miller, Dimitrije Cabarkapa, Andrew J. Miller, Lance L. Frazer, Tylan N. Templin, Travis D. Eliason, Samuel K. Garretson, Andrew C. Fry and Cory J. Berkland

Sensors 2023, 23(9), 4437; https://doi.org/10.3390/s23094437 - 2 May 2023

Cited by 4 | Viewed by 3713

Abstract

Three-dimensional force plates are important tools for biomechanics discovery and sports performance practice. However, currently, available 3D force plates lack portability and are often cost-prohibitive. To address this, a recently discovered 3D force sensor technology was used in the fabrication of a prototype force plate. Thirteen participants performed bodyweight and weighted lunges and squats on the prototype force plate and a standard 3D force plate positioned in series to compare forces measured by both force plates and validate the technology. For the lunges, there was excellent agreement between the experimental force plate and the standard force plate in the X-, Y-, and Z-axes (r = 0.950–0.999, p < 0.001). For the squats, there was excellent agreement between the force plates in the Z-axis (r = 0.996, p < 0.001). Across axes and movements, root mean square error (RMSE) ranged from 1.17% to 5.36% between force plates. Although the current prototype force plate is limited in sampling rate, the low RMSEs and extremely high agreement in peak forces provide confidence the novel force sensors have utility in constructing cost-effective and versatile use-case 3D force plates. Full article

(This article belongs to the Special Issue Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement)

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9 pages, 1186 KiB

Open AccessArticle

Torque Regulation Is Influenced by the Nature of the Isometric Contraction

by Philipp Bauer, João Sá Gomes, João Oliveira, Paulo Santos, Pedro Pezarat-Correia and João R. Vaz

Sensors 2023, 23(2), 726; https://doi.org/10.3390/s23020726 - 9 Jan 2023

Cited by 2 | Viewed by 1899

Abstract

The present study aimed to investigate the effects of a continuous visual feedback and the isometric contraction nature on the complexity and variability of force. Thirteen healthy and young male adults performed three MVCs followed by three submaximal isometric force tasks at a target force of 40% of their MVC for 30 s, as follows: (i) push isometric task with visual feedback (P_visual); (ii) hold isometric task with visual feedback (H_visual); (iii) hold isometric task without visual feedback (H_non-visual). Force complexity was evaluated through sample entropy (SampEn) of the force output. Force variability was analyzed through the coefficient of variation (CV). Results showed that differences were task-related, with P_visual showing higher complexity (i.e., higher SampEn) and decreased variability (i.e., lower CV) when compared with the remaining tasks. Additionally, no significant differences were found between the two hold isometric force tasks (i.e., no influence of visual feedback). Our results are promising as we showed these two isometric tasks to induce different motor control strategies. Furthermore, we demonstrated that visual feedback’s influence is also dependent on the type of isometric task. These findings should motivate researchers and physiologists to shift training paradigms and incorporate different force control evaluation tasks. Full article

(This article belongs to the Special Issue Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement)

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12 pages, 978 KiB

Open AccessArticle

Effects of Motor Task Difficulty on Postural Control Complexity during Dual Tasks in Young Adults: A Nonlinear Approach

by Marina Saraiva, João Paulo Vilas-Boas, Orlando J. Fernandes and Maria António Castro

Sensors 2023, 23(2), 628; https://doi.org/10.3390/s23020628 - 5 Jan 2023

Cited by 7 | Viewed by 2425

Abstract

Few studies have evaluated the effect of a secondary motor task on the standing posture based on nonlinear analysis. However, it is helpful to extract information related to the complexity, stability, and adaptability to the environment of the human postural system. This study aimed to analyze the effect of two motor tasks with different difficulty levels in motor performance complexity on the static standing posture in healthy young adults. Thirty-five healthy participants (23.08 ± 3.92 years) performed a postural single task (ST: keep a quiet standing posture) and two motor dual tasks (DT). i.e., mot-DT(A)—perform the ST while performing simultaneously an easy motor task (taking a smartphone out of a bag, bringing it to the ear, and putting it back in the bag)—and mot-DT(T)—perform the ST while performing a concurrent difficult motor task (typing on the smartphone keyboard). The approximate entropy (ApEn), Lyapunov exponent (LyE), correlation dimension (CoDim), and fractal dimension (detrending fluctuation analysis, DFA) for the mediolateral (ML) and anterior-posterior (AP) center-of-pressure (CoP) displacement were measured with a force plate while performing the tasks. A significant difference was found between the two motor dual tasks in ApEn, DFA, and CoDim-AP (p < 0.05). For the ML CoP direction, all nonlinear variables in the study were significantly different (p < 0.05) between ST and mot-DT(T), showing impairment in postural control during mot-DT(T) compared to ST. Differences were found across ST and mot-DT(A) in ApEn-AP and DFA (p < 0.05). The mot-DT(T) was associated with less effectiveness in postural control, a lower number of degrees of freedom, less complexity and adaptability of the dynamic system than the postural single task and the mot-DT(A). Full article

(This article belongs to the Special Issue Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement)

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14 pages, 11705 KiB

Open AccessArticle

Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional Coordinates Estimated with a Deep Learning Smartphone App

by Shigeki Yamada, Yukihiko Aoyagi, Chifumi Iseki, Toshiyuki Kondo, Yoshiyuki Kobayashi, Shigeo Ueda, Keisuke Mori, Tadanori Fukami, Motoki Tanikawa, Mitsuhito Mase, Minoru Hoshimaru, Masatsune Ishikawa and Yasuyuki Ohta

Sensors 2023, 23(2), 617; https://doi.org/10.3390/s23020617 - 5 Jan 2023

Cited by 4 | Viewed by 2683

Abstract

To assess pathological gaits quantitatively, three-dimensional coordinates estimated with a deep learning model were converted into body axis plane projections. First, 15 healthy volunteers performed four gait patterns; that is, normal, shuffling, short-stepped, and wide-based gaits, with the Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) application. Second, gaits of 47 patients with idiopathic normal pressure hydrocephalus (iNPH) and 92 healthy elderly individuals in the Takahata cohort were assessed with the TDPT-GT. Two-dimensional relative coordinates were calculated from the three-dimensional coordinates by projecting the sagittal, coronal, and axial planes. Indices of the two-dimensional relative coordinates associated with a pathological gait were comprehensively explored. The candidate indices for the shuffling gait were the angle range of the hip joint < 30° and relative vertical amplitude of the heel < 0.1 on the sagittal projection plane. For the short-stepped gait, the angle range of the knee joint < 45° on the sagittal projection plane was a candidate index. The candidate index for the wide-based gait was the leg outward shift > 0.1 on the axial projection plane. In conclusion, the two-dimensional coordinates on the body axis projection planes calculated from the 3D relative coordinates estimated by the TDPT-GT application enabled the quantification of pathological gait features. Full article

(This article belongs to the Special Issue Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement)

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12 pages, 1487 KiB

Open AccessArticle

Accuracy of Inertial Measurement Units When Applied to the Countermovement Jump of Track and Field Athletes

by Paulo Miranda-Oliveira, Marco Branco and Orlando Fernandes

Sensors 2022, 22(19), 7186; https://doi.org/10.3390/s22197186 - 22 Sep 2022

Cited by 5 | Viewed by 2482

Abstract

In this study, we aimed to assess the countermovement jump (CMJ) using a developed instrument encompassing an off-the-shelf Inertial Measurement Unit (IMU) in order to analyze performance during the contraction phase, as well as to determine the jump height and the modified reactive strength index (RSImod), using force plate (FP) data as reference. Eight athletes (six males and two females) performed CMJs with the IMU placed on their fifth lumbar vertebra. Accuracy was measured through mean error (standard deviation), correlation, and comparison tests. The results indicated high accuracy, high correlation (r), and no statistical differences between the IMU and the FP for contraction time (r = 0.902; ρ < 0.001), negative impulse phase time (r = 0.773; ρ < 0.001), flight time (r = 0.737; ρ < 0.001), jump time (r = 0.708; ρ < 0.001), RSImod (r = 0.725; ρ < 0.001), nor minimum force (r = 0.758; ρ < 0.001). However, the values related to the positive impulse phase did not have the expected accuracy, as we used different devices and positions. Our results demonstrated that our developed instrument could be utilized to identify the contraction phase, jump height, RSImod, and minimum force in the negative impulse phase with high accuracy, obtaining a signal similar to that of an FP. This information can help coaches and athletes with training monitoring and control, as the device has simpler applicability making it more systematic. Full article

(This article belongs to the Special Issue Advances in Sensors Development and Computer Science: Contributing to Neuromuscular Coordination in Human Movement)

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