Research

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10 pages, 1232 KiB

Open AccessArticle

Correlations of Aerobic Capacity with External and Internal Load of Young Football Players during Small-Sided Games

by Yiannis Michailidis

Sensors 2024, 24(7), 2258; https://doi.org/10.3390/s24072258 - 01 Apr 2024

Viewed by 853

Aerobic capacity plays a crucial role in football performance, making it a focal point in training processes. Small-sided games (SSGs) are widely used in football training, but the relationship between aerobic capacity and running performance during SSGs remains unclear. The aim of this [...] Read more.

Aerobic capacity plays a crucial role in football performance, making it a focal point in training processes. Small-sided games (SSGs) are widely used in football training, but the relationship between aerobic capacity and running performance during SSGs remains unclear. The aim of this study was to investigate possible correlations between maximum oxygen uptake (VO₂max) and running performance in youth football players in SSGs (4:4, 3:3, 2:2, 1:1) with three different pitch sizes per player (150, 100, 75 m²/player). Sixteen male U15 football players participated in the study. Players underwent the Yo-Yo intermittent recovery test level 1, and their VO₂max was estimated based on their performance. Subsequently, players participated in SSGs wearing GPS devices to measure internal and external load. Pearson or Spearman correlation was applied for statistical analysis depending on the normal distribution of the data. The results reveal that, for 4:4 and 3:3 relationships, larger pitches led to a greater impact of aerobic capacity (total distance (TD): 4:4, 150 m²/pl, r = 0.715, p = 0.002; 100 m²/pl, r = 0.656, p = 0.006; 75 m²/pl, r = 0.586, p = 0.017). In the 2:2 relationship, the opposite was observed, with more correlations appearing on smaller pitches (TD: 2:2, 100 m²/pl, r = 0.581, p = 0.018; 75 m²/pl, r = 0.747, p < 0.001). In the 1:1 relationship, correlations with VO₂max, total distance, and speed were observed only on the larger pitch. In conclusion, the aerobic capacity of young football players can influence running performance indicators in SSGs. Therefore, aerobic capacity could serve as a criterion for team composition, making SSGs more competitive. Additionally, the variation in correlations in the 2:2 relationship and their limited presence in the 1:1 relationship may be attributed to technical–tactical factors, such as increased ball contacts and one-on-one situations typically occurring in smaller setups. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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20 pages, 4380 KiB

Open AccessArticle

Estimating Rotational Acceleration in Shoulder and Elbow Joints Using a Transformer Algorithm and a Fusion of Biosignals

by Yu Bai, Xiaorong Guan, Long He, Zheng Wang, Zhong Li and Meng Zhu

Sensors 2024, 24(6), 1726; https://doi.org/10.3390/s24061726 - 07 Mar 2024

Viewed by 424

Abstract

In the present study, we used a transformer model and a fusion of biosignals to estimate rotational acceleration in elbow and shoulder joints. To achieve our study objectives, we proposed a mechanomyography (MMG) signal isolation technique based on a variational mode decomposition (VMD) [...] Read more.

In the present study, we used a transformer model and a fusion of biosignals to estimate rotational acceleration in elbow and shoulder joints. To achieve our study objectives, we proposed a mechanomyography (MMG) signal isolation technique based on a variational mode decomposition (VMD) algorithm. Our results show that the VMD algorithm delivered excellent performance in MMG signal extraction compared to the commonly used technique of empirical mode decomposition (EMD). In addition, we found that transformer models delivered estimates of joint acceleration that were more precise than those produced by mainstream time series forecasting models. The average R² values of transformer are 0.967, 0.968, and 0.935, respectively. Finally, we found that using a fusion of signals resulted in more precise estimation performance compared to using MMG signals alone. The differences between the average R² values are 0.041, 0.053, and 0.043, respectively. Taken together, the VMD isolation method, the transformer algorithm and the signal fusion technique described in this paper can be seen as supplying a robust framework for estimating rotational acceleration in upper-limb joints. Further study is warranted to examine the effectiveness of this framework in other musculoskeletal contexts. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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17 pages, 7387 KiB

Open AccessArticle

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting

by Yuxuan Xia, Wei Wei, Xichuan Lin and Jiaqian Li

Sensors 2024, 24(5), 1505; https://doi.org/10.3390/s24051505 - 26 Feb 2024

Viewed by 609

Abstract

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human–machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model [...] Read more.

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human–machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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14 pages, 1899 KiB

Open AccessArticle

Does Overhead Squat Performance Affect the Swing Kinematics and Lumbar Spine Loads during the Golf Downswing?

by Zi-Han Chen, Marcus Pandy, Tsung-Yu Huang and Wen-Tzu Tang

Sensors 2024, 24(4), 1252; https://doi.org/10.3390/s24041252 - 15 Feb 2024

Viewed by 995

Abstract

The performance of the overhead squat may affect the golf swing mechanics associated with golf-related low back pain. This study investigates the difference in lumbar kinematics and joint loads during the golf downswing between golfers with different overhead squat abilities. Based on the [...] Read more.

The performance of the overhead squat may affect the golf swing mechanics associated with golf-related low back pain. This study investigates the difference in lumbar kinematics and joint loads during the golf downswing between golfers with different overhead squat abilities. Based on the performance of the overhead squat test, 21 golfers aged 18 to 30 years were divided into the highest-scoring group (HS, N = 10, 1.61 ± 0.05 cm, and 68.06 ± 13.67 kg) and lowest-scoring group (LS, N = 11, 1.68 ± 0.10 cm, and 75.00 ± 14.37 kg). For data collection, a motion analysis system, two force plates, and TrackMan were used. OpenSim 4.3 software was used to simulate the joint loads for each lumbar joint. An independent t-test was used for statistical analysis. Compared to golfers demonstrating limitations in the overhead squat test, golfers with better performance in the overhead squat test demonstrated significantly greater angular extension displacement on the sagittal plane, smaller lumbar extension angular velocity, and smaller L4-S1 joint shear force. Consequently, the overhead squat test is a useful index to reflect lumbar kinematics and joint loading patterns during the downswing and provides a good training guide reference for reducing the risk of a golf-related lower back injury. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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16 pages, 2571 KiB

Open AccessArticle

Validity of Inertial Measurement Units to Measure Lower-Limb Kinematics and Pelvic Orientation at Submaximal and Maximal Effort Running Speeds

by Yi-Chung Lin, Kara Price, Declan S. Carmichael, Nirav Maniar, Jack T. Hickey, Ryan G. Timmins, Bryan C. Heiderscheit, Silvia S. Blemker and David A. Opar

Sensors 2023, 23(23), 9599; https://doi.org/10.3390/s23239599 - 04 Dec 2023

Viewed by 1188

Abstract

Inertial measurement units (IMUs) have been validated for measuring sagittal plane lower-limb kinematics during moderate-speed running, but their accuracy at maximal speeds remains less understood. This study aimed to assess IMU measurement accuracy during high-speed running and maximal effort sprinting on a curved [...] Read more.

Inertial measurement units (IMUs) have been validated for measuring sagittal plane lower-limb kinematics during moderate-speed running, but their accuracy at maximal speeds remains less understood. This study aimed to assess IMU measurement accuracy during high-speed running and maximal effort sprinting on a curved non-motorized treadmill using discrete (Bland–Altman analysis) and continuous (root mean square error [RMSE], normalised RMSE, Pearson correlation, and statistical parametric mapping analysis [SPM]) metrics. The hip, knee, and ankle flexions and the pelvic orientation (tilt, obliquity, and rotation) were captured concurrently from both IMU and optical motion capture systems, as 20 participants ran steadily at 70%, 80%, 90%, and 100% of their maximal effort sprinting speed (5.36 ± 0.55, 6.02 ± 0.60, 6.66 ± 0.71, and 7.09 ± 0.73 m/s, respectively). Bland–Altman analysis indicated a systematic bias within ±1° for the peak pelvic tilt, rotation, and lower-limb kinematics and −3.3° to −4.1° for the pelvic obliquity. The SPM analysis demonstrated a good agreement in the hip and knee flexion angles for most phases of the stride cycle, albeit with significant differences noted around the ipsilateral toe-off. The RMSE ranged from 4.3° (pelvic obliquity at 70% speed) to 7.8° (hip flexion at 100% speed). Correlation coefficients ranged from 0.44 (pelvic tilt at 90%) to 0.99 (hip and knee flexions at all speeds). Running speed minimally but significantly affected the RMSE for the hip and ankle flexions. The present IMU system is effective for measuring lower-limb kinematics during sprinting, but the pelvic orientation estimation was less accurate. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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21 pages, 1494 KiB

Open AccessArticle

A Privacy and Energy-Aware Federated Framework for Human Activity Recognition

by Ahsan Raza Khan, Habib Ullah Manzoor, Fahad Ayaz, Muhammad Ali Imran and Ahmed Zoha

Sensors 2023, 23(23), 9339; https://doi.org/10.3390/s23239339 - 22 Nov 2023

Viewed by 850

Abstract

Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating [...] Read more.

Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. The hybrid spiking-LSTM (S-LSTM) model synergistically combines the event-driven efficiency of SNNs and the sequence modelling capability of LSTMs. The model is trained using surrogate gradient learning and backpropagation through time, enabling fully supervised end-to-end learning. Extensive evaluations of two public datasets demonstrate that the proposed approach outperforms LSTM, CNN, and S-CNN models in accuracy and energy efficiency. For instance, the proposed S-LSTM achieved an accuracy of 97.36% and 89.69% for indoor and outdoor scenarios, respectively. Furthermore, the results also showed a significant improvement in energy efficiency of 32.30%, compared to simple LSTM. Additionally, we highlight the significance of personalisation in HAR, where fine-tuning with local data enhances model accuracy by up to 9% for individual users. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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19 pages, 6472 KiB

Open AccessArticle

Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit

by Cheng-Hao Yu, Chih-Ching Yeh, Yi-Fu Lu, Yi-Ling Lu, Ting-Ming Wang, Frank Yeong-Sung Lin and Tung-Wu Lu

Sensors 2023, 23(22), 9040; https://doi.org/10.3390/s23229040 - 08 Nov 2023

Viewed by 841

Abstract

Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young [...] Read more.

Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent t-tests were used for between-group comparisons. The sensitivity, specificity, and Pearson’s r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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13 pages, 3383 KiB

Open AccessArticle

A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells

by Junggil Kim, Hyeon Kang, Seulgi Lee, Jinseung Choi and Gyerae Tack

Sensors 2023, 23(7), 3428; https://doi.org/10.3390/s23073428 - 24 Mar 2023

Cited by 1 | Viewed by 1845

Abstract

Ground reaction force (GRF) is essential for estimating muscle strength and joint torque in inverse dynamic analysis. Typically, it is measured using a force plate. However, force plates have spatial limitations, and studies of gaits involve numerous steps and thus require a large [...] Read more.

Ground reaction force (GRF) is essential for estimating muscle strength and joint torque in inverse dynamic analysis. Typically, it is measured using a force plate. However, force plates have spatial limitations, and studies of gaits involve numerous steps and thus require a large number of force plates, which is disadvantageous. To overcome these challenges, we developed a deep learning model for estimating three-axis GRF utilizing shoes with three uniaxial load cells. GRF data were collected from 81 people as they walked on two force plates while wearing shoes with three load cells. The three-axis GRF was calculated using a seq2seq approach based on long short-term memory (LSTM). To conduct the learning, validation, and testing, random selection was performed based on the subjects. The 60 selected participants were divided as follows: 37 were in the training set, 12 were in the validation set, and 11 were in the test set. The estimated GRF matched the force plate-measured GRF with correlation coefficients of 0.97, 0.96, and 0.90 and root mean square errors of 65.12 N, 15.50 N, and 9.83 N for the vertical, anterior–posterior, and medial–lateral directions, respectively, and there was a mid-stance timing error of 5.61% in the test dataset. A Bland–Altman analysis showed good agreement for the maximum vertical GRF. The proposed shoe with three uniaxial load cells and seq2seq LSTM can be utilized for estimating the 3D GRF in an outdoor environment with level ground and/or for gait research in which the subject takes several steps at their preferred walking speed, and hence can supply crucial data for a basic inverse dynamic analysis. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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12 pages, 3493 KiB

Open AccessArticle

Sensitiveness of Variables Extracted from a Fitness Smartwatch to Detect Changes in Vertical Impact Loading during Outdoors Running

by Cristina-Ioana Pirscoveanu and Anderson Souza Oliveira

Sensors 2023, 23(6), 2928; https://doi.org/10.3390/s23062928 - 08 Mar 2023

Cited by 3 | Viewed by 1626

Abstract

Accelerometry is becoming a popular method to access human movement in outdoor conditions. Running smartwatches may acquire chest accelerometry through a chest strap, but little is known about whether the data from these chest straps can provide indirect access to changes in vertical [...] Read more.

Accelerometry is becoming a popular method to access human movement in outdoor conditions. Running smartwatches may acquire chest accelerometry through a chest strap, but little is known about whether the data from these chest straps can provide indirect access to changes in vertical impact properties that define rearfoot or forefoot strike. This study assessed whether the data from a fitness smartwatch and chest strap containing a tri-axial accelerometer (FS) is sensible to detect changes in running style. Twenty-eight participants performed 95 m running bouts at ~3 m/s in two conditions: normal running and running while actively reducing impact sounds (silent running). The FS acquired running cadence, ground contact time (GCT), stride length, trunk vertical oscillation (TVO), and heart rate. Moreover, a tri-axial accelerometer attached to the right shank provided peak vertical tibia acceleration (PK_ACC). The running parameters extracted from the FS and PK_ACC variables were compared between normal and silent running. Moreover, the association between PK_ACC and smartwatch running parameters was accessed using Pearson correlations. There was a 13 ± 19% reduction in PK_ACC (p < 0.005), and a 5 ± 10% increase in TVO from normal to silent running (p < 0.01). Moreover, there were slight reductions (~2 ± 2%) in cadence and GCT when silently running (p < 0.05). However, there were no significant associations between PK_ACC and the variables extracted from the FS (r < 0.1, p > 0.05). Therefore, our results suggest that biomechanical variables extracted from FS have limited sensitivity to detect changes in running technique. Moreover, the biomechanical variables from the FS cannot be associated with lower limb vertical loading. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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Review

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24 pages, 2514 KiB

Open AccessReview

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

by Zhou Fang, Sarah Woodford, Damith Senanayake and David Ackland

Sensors 2023, 23(14), 6535; https://doi.org/10.3390/s23146535 - 19 Jul 2023

Cited by 5 | Viewed by 2666

Abstract

Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert [...] Read more.

Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; and secondly, to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of

4^{°}

across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion–extension as low as

2^{°}

. For the glenohumeral joint, 3D joint motion has been described with RMS errors of

6^{°}

and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of

10^{°}

in the protraction–retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral joint motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapula locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology)

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