Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning

Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.

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Joint Loading Effect on Kinematics of the Natural Knee During a Range of Simulated Walking Profiles

Volume 3: Biomedical and Biotechnology Engineering ◽

10.1115/imece2014-38995 ◽

2014 ◽

Author(s):

Fallon Fitzwater ◽

Kim Cole ◽

Lorin Maletsky

Keyword(s):

Soft Tissue ◽

Knee Joint ◽

Direct Relationship ◽

Compressive Load ◽

Knee Kinematics ◽

Loading Effect ◽

Minimal Error ◽

Joint Forces ◽

Joint Loads ◽

Anterior Posterior

The physiological ratio of compression to anterior-posterior (A-P) knee joint loads has substantial effects on the loading of soft tissue structures, patellofemoral loads, and knee kinematics [1, 2]. There is also a direct relationship between resultant kinematics and joint forces. D’lima et al. was also able to compute A-P kinematics at a given flexion angle with minimal error using measured A-P and compressive load acquired from the instrumented tibia [3]. The direction of A-P load measured at the tibia is associated with the direction of translations of the femur relative to the tibia.

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Rechtsventrikuläre Auswurffraktion Vergleich verschiedener Methoden der Angiographie und der Äquilibrium-Radionuklidventrikulographie

Nuklearmedizin ◽

10.1055/s-0038-1624359 ◽

1987 ◽

Vol 26 (01) ◽

pp. 39-45

Author(s):

U. Tebbe ◽

E. Voth ◽

R. Sciagra ◽

W. Schultz ◽

G. Neuhaus ◽

...

Keyword(s):

Right Atrium ◽

Pulmonary Valve ◽

Heart Diseases ◽

Correlation Coefficients ◽

Radionuclide Ventriculography ◽

Background Correction ◽

Minor Effect ◽

The Right ◽

A Minor ◽

Good Agreement

In 21 patients with various heart diseases RVEF was measured angiographically and by radionuclide ventriculography. Using biplane angiocardiography evaluation was performed by 7 different methods (Simpson’s rule, Dogde, Arcilla, Ferlinz, Duebel). Using equilibrium RNV, evaluation was performed by 9 modifications of analysis. Problems were evident to separate the right atrium from the ventricle and to define the site of the pulmonary valve. The results show that when using the various methods of angiography considerable variations of the absolute volumes occur, but least so with RVEF. When using RNV with one single enddiastolic ROI, the RVEF was much too low. By means of the enddiastolic/endsystolic Double-ROI-method a good agreement with angiography was found, with correlation coefficients up to r = 0.85. There was only a minor effect of background correction.

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Validity of Wearable Sensors at the Shoulder Joint: Combining Wireless Electromyography Sensors and Inertial Measurement Units to Perform Physical Workplace Assessments

Sensors ◽

10.3390/s19081885 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1885 ◽

Cited By ~ 6

Author(s):

Isabelle Poitras ◽

Mathieu Bielmann ◽

Alexandre Campeau-Lecours ◽

Catherine Mercier ◽

Laurent J. Bouyer ◽

...

Keyword(s):

Wearable Sensors ◽

Correlation Coefficients ◽

Emg Activity ◽

Complex Task ◽

Inertial Measurement Units ◽

Simple Task ◽

Inertial Measurement ◽

Work Related ◽

Measurement Units ◽

Shoulder Kinematics

Background: Workplace adaptation is the preferred method of intervention to diminish risk factors associated with the development of work-related shoulder disorders. However, the majority of the workplace assessments performed are subjective (e.g., questionnaires). Quantitative assessments are required to support workplace adaptations. The aims of this study are to assess the concurrent validity of inertial measurement units (IMUs; MVN, Xsens) in comparison to a motion capture system (Vicon) during lifting tasks, and establish the discriminative validity of a wireless electromyography (EMG) system for the evaluation of muscle activity. Methods: Sixteen participants performed 12 simple tasks (shoulder flexion, abduction, scaption) and 16 complex lifting tasks (lifting crates of different weights at different heights). A Delsys Trigno EMG system was used to record anterior and middle deltoids’ EMG activity, while the Xsens and Vicon simultaneously recorded shoulder kinematics. Results: For IMUs, correlation coefficients were high (simple task: >0.968; complex task: >0.84) and RMSEs were low (simple task: <6.72°; complex task: <11.5°). For EMG, a significant effect of weight, height and a weight x height interaction (anterior: p < 0.001; middle: p < 0.03) were observed for RMS EMG activity. Conclusions: These results suggest that wireless EMG and IMUs are valid units that can be used to measure physical demand in workplace assessments.

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New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?

Sensors ◽

10.3390/s19112516 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2516 ◽

Cited By ~ 3

Author(s):

Nizam U. Ahamed ◽

Lauren C. Benson ◽

Christian A. Clermont ◽

Andrew J. Pohl ◽

Reed Ferber

Keyword(s):

Wearable Sensors ◽

Correlation Coefficients ◽

Gait Pattern ◽

Future Research ◽

Stability Point ◽

Testing Data ◽

Measurement Units ◽

Ground Contact Time ◽

Stable Gait ◽

Running Pattern

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a ‘typical’ or ‘stable’ gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2–4 runs were needed to define a stable running pattern for univariate, and 4–5 days were necessary for multivariate analysis across all inclination conditions. Pearson’s correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63–0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual’s stable gait pattern.

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Three-Dimensional Lower Extremity Joint Loading in a Carved Ski and Snowboard Turn: A Pilot Study

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/340272 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Miriam Klous ◽

Erich Müller ◽

Hermann Schwameder

Keyword(s):

Knee Joint ◽

Lower Extremity ◽

Kinetic Data ◽

Three Dimensional ◽

Longitudinal Axis ◽

Joint Loading ◽

Inverse Dynamic ◽

Kinematic Data ◽

Joint Forces ◽

Anterior Posterior

A large number of injuries to the lower extremity occur in skiing and snowboarding. Due to the difficulty of collecting 3D kinematic and kinetic data with high accuracy, a possible relationship between injury statistic and joint loading has not been studied. Therefore, the purpose of the current study was to compare ankle and knee joint loading at the steering leg between carved ski and snowboard turns. Kinetic data were collected using mobile force plates mounted under the toe and heel part of the binding on skies or snowboard (KISTLER). Kinematic data were collected with five synchronized, panning, tilting, and zooming cameras. An extended version of the Yeadon model was applied to calculate inertial properties of the segments. Ankle and knee joint forces and moments were calculated using inverse dynamic analysis. Results showed higher forces along the longitudinal axis in skiing and similar forces for skiing and snowboarding in anterior-posterior and mediolateral direction. Joint moments were consistently greater during a snowboard turn, but more fluctuations were observed in skiing. Hence, when comparing joint loading between carved ski and snowboard turns, one should differentiate between forces and moments, including the direction of forces and moments and the turn phase.

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Agreement between the inertial measurement unit and optoelectronic system to measure postural sway

Technology and Health Care ◽

10.3233/thc-212924 ◽

2021 ◽

pp. 1-6

Author(s):

Alessandro Vagnini ◽

Roberta Furone ◽

Giulia Zanotti ◽

Paola Adamo ◽

Federico Temporiti ◽

...

Keyword(s):

Inertial Measurement Unit ◽

Postural Sway ◽

Measurement Unit ◽

Confidence Ellipse ◽

Optoelectronic System ◽

Inertial Measurement ◽

Measurement Units ◽

User Friendly ◽

Anterior Posterior ◽

Good Agreement

BACKGROUND: Optoelectronic systems and force platforms represent the gold standard for postural sway assessment, but pose disadvantages in terms of equipment, cost and preparation time. OBJECTIVE: Wearable inertial measurement units (IMUs) have been proposed to overcome these issues, but have never been compared to an optoelectronic system. The study aim was therefore to investigate agreement between inertial measurement unit and optoelectronic system in postural sway assessment. METHODS: Thirty healthy volunteers performed four balance tasks. IMU was placed on the sacrum (S2) with a retroreflective marker over the sensor and subjects’ performance was simultaneously recorded by both systems. Total (TOT), anterior-posterior (AP) and medial-lateral (ML) length of trace, range, speed, root mean squared (RMS), and confidence ellipse were computed. RESULTS: ICCs revealed excellent correlations for Length-TOT, Length-AP and Speed-AP, good correlation for Length-ML, Speed-ML, Confidence Ellipse, Range-AP and RMS-AP, and moderate correlation for range-ML and RMS-ML. Bland-Altman plot showed greater estimation for Length-TOT, Length-AP, Speed-AP, confidence ellipse and RMS-AP using optoelectronic system, and for Length-ML, Range-AP, Range-ML, Speed-ML, RMS-ML using IMU. Both systems revealed the same differences among tasks. CONCLUSION: The excellent to good agreement of IMU for length of trace and speed parameters and its user-friendly application suggest its potential implementations in clinical practice.

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Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽

10.3390/s21010194 ◽

2020 ◽

Vol 21 (1) ◽

pp. 194

Author(s):

Sarah Gonzalez ◽

Paul Stegall ◽

Harvey Edwards ◽

Leia Stirling ◽

Ho Chit Siu

Keyword(s):

Activity Recognition ◽

Principal Components ◽

Classification Accuracy ◽

Wearable Sensors ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Measurement Units ◽

The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

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The patellofemoral morphology and the normal predicted value of tibial tuberosity-trochlear groove distance in the Chinese population

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04454-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zhe Li ◽

Guanzhi Liu ◽

Run Tian ◽

Ning Kong ◽

Yue Li ◽

...

Keyword(s):

Individual Differences ◽

Chinese Population ◽

Pearson Correlation ◽

Three Dimensional ◽

Correlation Coefficients ◽

Tibial Tuberosity ◽

Trochlear Groove ◽

Lasso Regression ◽

Cross Sectional ◽

Anterior Posterior

Abstract Background Our objective was to obtain normal patellofemoral measurements to analyse sex and individual differences. In addition, the absolute values and indices of tibial tuberosity-trochlear groove (TT-TG) distances are still controversial in clinical application. A better method to enable precise prediction is still needed. Methods Seventy-eight knees of 78 participants without knee pathologies were included in this cross-sectional study. A CT scan was conducted for all participants and three-dimensional knee models were constructed using Mimics and SolidWorks software. We measured and analysed 19 parameters including the TT-TG distance and dimensions and shapes of the patella, femur, tibia, and trochlea. LASSO regression was used to predict the normal TT-TG distances. Results The dimensional parameters, TT-TG distance, and femoral aspect ratio of the men were significantly larger than those of women (all p values < 0.05). However, after controlling for the bias from age, height, and weight, there were no significant differences in TT-TG distances and anterior-posterior dimensions between the sexes (all p values > 0.05). The Pearson correlation coefficients between the anterior femoral offset and other indexes were consistently below 0.3, indicating no relationship or a weak relationship. Similar results were observed for the sulcus angle and the Wiberg index. Using LASSO regression, we obtained four parameters to predict the TT-TG distance (R2 = 0.5612, p < 0.01) to achieve the optimal accuracy and convenience. Conclusions Normative data of patellofemoral morphology were provided for the Chinese population. The anterior-posterior dimensions of the women were thicker than those of men for the same medial-lateral dimensions. More attention should be paid to not only sex differences but also individual differences, especially the anterior condyle and trochlea. In addition, this study provided a new method to predict TT-TG distances accurately.

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Reliability of activity monitors for physical activity assessment in patients with musculoskeletal disorders: A systematic review

Journal of Back and Musculoskeletal Rehabilitation ◽

10.3233/bmr-200348 ◽

2021 ◽

pp. 1-9

Author(s):

Francesco Negrini ◽

Alessandro de Sire ◽

Stefano Giuseppe Lazzarini ◽

Federico Pennestrì ◽

Salvatore Sorce ◽

...

Keyword(s):

Physical Activity ◽

Systematic Review ◽

Musculoskeletal Disorders ◽

Wearable Sensors ◽

Activity Monitors ◽

Activity Assessment ◽

Measurement Tools ◽

Different Types ◽

Measurement Units ◽

Full Text Screening

BACKGROUND: Activity monitors have been introduced in the last years to objectively measure physical activity to help physicians in the management of musculoskeletal patients. OBJECTIVE: This systematic review aimed at describing the assessment of physical activity by commercially available portable activity monitors in patients with musculoskeletal disorders. METHODS: PubMed, Embase, PEDro, Web of Science, Scopus and CENTRAL databases were systematically searched from inception to June 11th, 2020. We considered as eligible observational studies with: musculoskeletal patients; physical activity measured by wearable sensors based on inertial measurement units; comparisons performed with other tools; outcomes consisting of number of steps/day, activity/inactivity time, or activity counts/day. RESULTS: Out of 595 records, after removing duplicates, title/abstract and full text screening, 10 articles were included. We noticed a wide heterogeneity in the wearable devices, that resulted to be 10 different types. Patients included suffered from rheumatoid arthritis, osteoarthritis, juvenile idiopathic arthritis, polymyalgia rheumatica, and fibromyalgia. Only 3 studies compared portable activity trackers with objective measurement tools. CONCLUSIONS: Taken together, this systematic review showed that activity monitors might be considered as useful to assess physical activity in patients with musculoskeletal disorders, albeit, to date, the high device heterogeneity and the different algorithms still prevent their standardization.

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Application of an Artificial Neural Network to Automate the Measurement of Kinematic Characteristics of Punches in Boxing

Applied Sciences ◽

10.3390/app11031223 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1223

Author(s):

Ilshat Khasanshin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Kinematic Parameters ◽

Constant Control ◽

Measurement Units ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Effective Development ◽

High Level ◽

Different Levels

This work aimed to study the automation of measuring the speed of punches of boxers during shadow boxing using inertial measurement units (IMUs) based on an artificial neural network (ANN). In boxing, for the effective development of an athlete, constant control of the punch speed is required. However, even when using modern means of measuring kinematic parameters, it is necessary to record the circumstances under which the punch was performed: The type of punch (jab, cross, hook, or uppercut) and the type of activity (shadow boxing, single punch, or series of punches). Therefore, to eliminate errors and accelerate the process, that is, automate measurements, the use of an ANN in the form of a multilayer perceptron (MLP) is proposed. During the experiments, IMUs were installed on the boxers’ wrists. The input parameters of the ANN were the absolute acceleration and angular velocity. The experiment was conducted for three groups of boxers with different levels of training. The developed model showed a high level of punch recognition for all groups, and it can be concluded that the use of the ANN significantly accelerates the collection of data on the kinetic characteristics of boxers’ punches and allows this process to be automated.

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