Fatigue-Related Changes in Running Gait Patterns Persist in the Days Following a Marathon Race

2020 ◽  
Vol 29 (7) ◽  
pp. 934-941 ◽  
Author(s):  
Christian A. Clermont ◽  
Andrew J. Pohl ◽  
Reed Ferber
Keyword(s):  
Musculoskeletal Pain ◽  
Center Of Mass ◽  
Vertical Direction ◽  
Measurement Unit ◽  
Post Intervention ◽  
Marathon Runners ◽  
Gait Patterns ◽  
Marathon Race ◽  
Track Surface ◽  
Running Biomechanics

Context: The risk of experiencing an overuse running-related injury can increase with atypical running biomechanics associated with neuromuscular fatigue and/or training errors. While it is important to understand the changes in running biomechanics within a fatigue-inducing run, it may be more clinically relevant to assess gait patterns in the days following a marathon to better evaluate the effects of inadequate recovery on injury. Objective: To use center of mass (CoM) acceleration patterns to investigate changes in running patterns prior to (PRE) and at 2 (POST2) and 7 (POST7) days following a marathon race. Design: Pre–post intervention study. Setting: A 200-m oval track surface. Participants: Seventeen recreational marathon runners (10 females, age = 34.2 [5.67] y; 7 males, age = 47.41 [15.32] y). Intervention: Marathon race. Main Outcome Measures: An inertial measurement unit was placed near the CoM to collect triaxial acceleration data during overground running for PRE, POST2, and POST7 sessions. Twenty-two features were extracted from the acceleration waveforms to characterize different aspects of running gait. Lower-limb musculoskeletal pain was also recorded at each session with a visual analog scale. Results: At POST2, runners reported higher self-reported pain and exhibited elevated peak mediolateral acceleration with an increased mediolateral ratio of acceleration root mean square compared with PRE. At POST7, pain was reduced and more similar to PRE, with runners demonstrating increased stride regularity in the vertical direction and decreased peak resultant acceleration. Conclusions: The observed changes in CoM motion at POST2 may be associated with atypical running biomechanics that can translate to greater mediolateral impulses, potentially increasing the risk of injury. This study demonstrates the use of an accelerometer as an effective tool to detect atypical CoM motion for runners due to fatigue, recovery, and musculoskeletal pain in real-world environments.

Dynamic and Reactive Walking for Humanoid Robots Based on Foot Placement Control

2016 ◽  
Vol 13 (02) ◽  
pp. 1550041 ◽  
Author(s):  
Juan Alejandro Castano ◽  
Zhibin Li ◽  
Chengxu Zhou ◽  
Nikos Tsagarakis ◽  
Darwin Caldwell
Keyword(s):  
Center Of Mass ◽  
Humanoid Robots ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Foot Placement ◽  
Inverted Pendulum Model ◽  
Gait Parameters ◽  
Pendulum Model ◽  
Single Mass ◽  
Walking Velocity

This paper presents a novel online walking control that replans the gait pattern based on our proposed foot placement control using the actual center of mass (COM) state feedback. The analytic solution of foot placement is formulated based on the linear inverted pendulum model (LIPM) to recover the walking velocity and to reject external disturbances. The foot placement control predicts where and when to place the foothold in order to modulate the gait given the desired gait parameters. The zero moment point (ZMP) references and foot trajectories are replanned online according to the updated foothold prediction. Hence, only desired gait parameters are required instead of predefined or fixed gait patterns. Given the new ZMP references, the extended prediction self-adaptive control (EPSAC) approach to model predictive control (MPC) is used to minimize the ZMP response errors considering the acceleration constraints. Furthermore, to ensure smooth gait transitions, the conditions for the gait initiation and termination are also presented. The effectiveness of the presented gait control is validated by extensive disturbance rejection studies ranging from single mass simulation to a full body humanoid robot COMAN in a physics based simulator. The versatility is demonstrated by the control of reactive gaits as well as reactive stepping from standing posture. We present the data of the applied disturbances, the prediction of sagittal/lateral foot placements, the replanning of the foot/ZMP trajectories, and the COM responses.

scholarly journals 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Binbin Wang ◽  
Tingli Su ◽  
Xuebo Jin ◽  
Jianlei Kong ◽  
Yuting Bai
Keyword(s):  
Learning Algorithm ◽  
Gait Recognition ◽  
Vertical Direction ◽  
Recognition Algorithm ◽  
Measurement Unit ◽  
Complex Environments ◽  
Position Change ◽  
Vertical Movements ◽  
Moving Direction ◽  
Optimal Gait

An inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments, such as those involving fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an optimal gait recognition algorithm to improve the accuracy of gait detection. Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. According to the recognition results, the position change in the vertical direction could be reasonably corrected. The obtained 3D trajectory involving both horizontal and vertical movements has shown that the accuracy is significantly improved in practical complex environments.

scholarly journals Three-axis air-bearing based platform for small satellite attitude determination and control simulation

2005 ◽  
Vol 3 (03) ◽  
Author(s):  
J. Prado ◽  
G. Bisiacchi ◽  
L. Reyes ◽  
E. Vicente ◽  
F. Contreras ◽  
...  
Keyword(s):  
Attitude Control ◽  
Attitude Determination ◽  
Center Of Mass ◽  
Measurement Unit ◽  
Simulation Platform ◽  
Air Bearing ◽  
Small Satellites ◽  
Main Components ◽  
Free Environment ◽  
And Control

A frictionless environment simulation platform, utilized for accomplishing three-axis attitude control tests in small satellites, is introduced. It is employed to develop, improve, and carry out objective tests of sensors, actuators, and algorithms in the experimental framework. Different sensors (i.e. sun, earth, magnetometer, and an inertial measurement unit) are utilized to assess three-axis deviations. A set of three inertial wheels is used as primary actuators for attitude control, together with three mutually perpendicular magnetic coils intended for desaturation purposes, and as a backup control system. Accurate balancing, through the platform’s center of mass relocation into the geometrical center of the spherical air-bearing, significatively reduces gravitational torques, generating a virtually torque-free environment. A very practical balancing procedure was developed for equilibrating the table in the local horizontal plane, with a reduced final residual torque. A wireless monitoring system was developed for on-line and post-processing analysis; attitude data are displayed and stored, allowing properly evaluate the sensors, actuators, and algorithms. A specifically designed onboard computer and a set of microcontrollers are used to carry out attitude determination and control tasks in a distributed control scheme. The main components and subsystems of the simulation platform are described in detail.

scholarly journals Impact of Decreased Night Work on Workers’ Musculoskeletal Symptoms: A Quasi-Experimental Intervention Study

2020 ◽  
Vol 17 (23) ◽  
pp. 9092
Author(s):  
Hye-Eun Lee ◽  
Min Choi ◽  
Hyoung-Ryoul Kim ◽  
Ichiro Kawachi
Keyword(s):  
Musculoskeletal Pain ◽  
Night Shift ◽  
Night Work ◽  
Musculoskeletal Symptoms ◽  
Control Group ◽  
Body Parts ◽  
Difference In Difference ◽  
Post Intervention ◽  
Shift Workers ◽  
The Impact

A possible association between night shift work and musculoskeletal disorder has been suggested. This study aimed to evaluate the impact of decreased night work on musculoskeletal pain. Difference-in-difference estimation was used to compare changes in musculoskeletal pain between shift workers (N = 122) and non-shift workers (N = 170) in a manufacturing company before and after the introduction of a new shift system eliminating overnight work. Musculoskeletal pain was measured by a questionnaire asking if workers had symptoms in specific body parts, including the neck, shoulder, arm/elbow, wrist/hand, back, and leg/foot, over the past year. Generalized estimating equation models were used to estimate changes in pre- versus post-intervention musculoskeletal pain rates between the treated and control group. In the difference-in-difference (DID) models, prevalence of musculoskeletal pain for shoulder (−10.3%), arm (−12.9%), all sites combined (−9.2%), and upper extremity combined (−14.8%) showed significant decreases from pre- to post-intervention among the treated group (shift workers) compared to the control group (non-shift workers) after controlling for age and weekly working hours. Decreasing night work was related to improvement in musculoskeletal pain in shift workers.

scholarly journals Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4385 ◽  
Author(s):  
Carlo Dindorf ◽  
Wolfgang Teufl ◽  
Bertram Taetz ◽  
Gabriele Bleser ◽  
Michael Fröhlich
Keyword(s):  
Total Hip Arthroplasty ◽  
Hip Arthroplasty ◽  
Measurement Unit ◽  
Practical Application ◽  
Gait Patterns ◽  
Waveform Data ◽  
Total Hip ◽  
Model Interpretation ◽  
Gait Classification ◽  
Interpretable Model

Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy Macc = 100%), followed by features based on simple descriptive statistics (Macc = 97.38%) and waveform data (Macc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns.

scholarly journals Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5553
Author(s):  
Mohsen Sharifi Renani ◽  
Casey A. Myers ◽  
Rohola Zandie ◽  
Mohammad H. Mahoor ◽  
Bradley S. Davidson ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Gait Patterns ◽  
Movement Data ◽  
Gait Parameters ◽  
Activity Types ◽  
Spatial Parameters ◽  
Total Knee ◽  
Insight Into ◽  
Sensor Locations

Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the ability of multiple deep neural network (DNN) architectures to predict 12 STGPs from inertial measurement unit (IMU) data and to identify an optimal sensor combination, which has yet to be studied for OA and TKA subjects. DNNs were trained using movement data from 29 subjects, walking at slow, normal, and fast paces and evaluated with cross-fold validation over the subjects. Optimal sensor locations were determined by comparing prediction accuracy with 15 IMU configurations (pelvis, thigh, shank, and feet). Percent error across the 12 STGPs ranged from 2.1% (stride time) to 73.7% (toe-out angle) and overall was more accurate in temporal parameters than spatial parameters. The most and least accurate sensor combinations were feet-thighs and singular pelvis, respectively. DNNs showed promising results in predicting STGPs for OA and TKA subjects based on signals from IMU sensors and overcomes the dependency on sensor locations that can hinder the design of patient monitoring systems for clinical application.

scholarly journals Auditory feedback alters postural control and functional ability in patients with chronic stroke

2020 ◽  
Author(s):  
Hee Sung Lim ◽  
Jiseon Ryu ◽  
Sihyun Ryu
Keyword(s):  
White Noise ◽  
Inclination Angle ◽  
Auditory Feedback ◽  
Exercise Intervention ◽  
Center Of Pressure ◽  
Dynamic Balance ◽  
Effective Means ◽  
Center Of Mass ◽  
Chronic Stroke ◽  
Post Intervention

Abstract Background: This study aimed to investigate the effect of white noise on dynamic balance in patients with stroke and the pre- and post-intervention changes in dynamic balance during walking by analyzing the anterior-posterior (A-P) and medial-lateral (M-L) center of pressure (CoP) range and velocity, center of mass (CoM), and A-P/M-L inclination angle using CoM-CoP and to establish the basis for using auditory feedback as an effective means of exercise intervention by bringing changes in dynamic balance abilities of patients with chronic stroke and retain the necessary abilities for maintaining independent and functional daily living.Methods: Nineteen patients with chronic stroke (age: 61.2±9.8 years, height: 164.4±7.4 cm, weight: 61.1±9.4 kg, paretic side (R/L): 11/8, duration: 11.6±4.9 years) were included as study participants. Auditory feedback used white noise, and all participants listened for 20 minutes mixing six types of natural sounds with random sounds. The dynamic balancing ability was evaluated during the walking, and the variables were the center of pressure (CoP), the center of mass (CoM), CoP-CoM inclined angle.Results: There is a significant increase in the A-P CoP range, A-P inclination angle, and gait speed on the paretic and non-paretic sides following white noise intervention (p<.05). In addition, the changes in CoP velocity on the paretic and non-paretic sides increased in both the A-P and M-L directions but not significantly.Conclusion: Our findings confirmed the positive effect of using white noise as auditory feedback through a more objective and quantitative assessment using CoP-CoM inclination angle as an evaluation indicator for assessing dynamic balance in patients with chronic stroke. The A-P and M-L inclination angle can be employed as a useful indicator for evaluating other exercise programs and intervention methods for functional enhancement of patients with chronic stroke in terms of their effects on dynamic balance and effectiveness.

scholarly journals Development and Validation of Prediction Equation of “Athens Authentic Marathon” Men’s Race Speed

2021 ◽  
Vol 12 ◽  
Author(s):  
Pantelis T. Nikolaidis ◽  
Thomas Rosemann ◽  
Beat Knechtle
Keyword(s):  
Body Mass ◽  
Prediction Equation ◽  
Physiological Characteristics ◽  
Control Group ◽  
Running Speed ◽  
Marathon Runners ◽  
Race Time ◽  
Marathon Race ◽  
Weekly Training

AimDespite the increasing popularity of outdoor endurance running races of different distances, little information exists about the role of training and physiological characteristics of recreational runners. The aim of the present study was (a) to examine the role of training and physiological characteristics on the performance of recreational marathon runners and (b) to develop a prediction equation of men’s race time in the “Athens Authentic Marathon.”MethodsRecreational male marathon runners (n = 130, age 44.1 ± 8.6 years)—who finished the “Athens Authentic Marathon” 2017—performed a series of anthropometry and physical fitness tests including body mass index (BMI), body fat percentage (BF), maximal oxygen uptake (VO2max), anaerobic power, squat, and countermovement jump. The variation of these characteristics was examined by quintiles (i.e., five groups consisting of 26 participants in each) of the race speed. An experimental group (EXP, n = 65) was used to develop a prediction equation of the race time, which was verified in a control group (CON, n = 65).ResultsIn the overall sample, a one-way ANOVA showed a main effect of quintiles on race speed on weekly training days and distance, age, body weight, BMI, BF, and VO2max (p ≤ 0.003, η2 ≥ 0.121), where the faster groups outscored the slower groups. Running speed during the race correlated moderately with age (r = −0.36, p &lt; 0.001) and largely with the number of weekly training days (r = 0.52, p &lt; 0.001) and weekly running distance (r = 0.58, p &lt; 0.001), but not with the number of previously finished marathons (r = 0.08, p = 0.369). With regard to physiological characteristics, running speed correlated largely with body mass (r = −0.52, p &lt; 0.001), BMI (r = −0.60, p &lt; 0.001), BF (r = −0.65, p &lt; 0.001), VO2max (r = 0.67, p &lt; 0.001), moderately with isometric muscle strength (r = 0.42, p &lt; 0.001), and small with anaerobic muscle power (r = 0.20, p = 0.021). In EXP, race speed could be predicted (R2 = 0.61, standard error of the estimate = 1.19) using the formula “8.804 + 0.111 × VO2max + 0.029 × weekly training distance in km −0.218 × BMI.” Applying this equation in CON, no bias was observed (difference between observed and predicted value 0.12 ± 1.09 km/h, 95% confidence intervals −0.15, 0.40, p = 0.122).ConclusionThese findings highlighted the role of aerobic capacity, training, and body mass status for the performance of recreational male runners in a marathon race. The findings would be of great practical importance for coaches and trainers to predict the average marathon race time in a specific group of runners.

scholarly journals Stride-to-Stride Variability of the Center of Mass in Male Trained Runners After an Exhaustive Run: A Three Dimensional Movement Variability Analysis With a Subject-Specific Anthropometric Model

2021 ◽  
Vol 3 ◽  
Author(s):  
Felix Möhler ◽  
Bernd Stetter ◽  
Hermann Müller ◽  
Thorsten Stein
Keyword(s):  
Center Of Mass ◽  
Three Dimensional ◽  
Vertical Direction ◽  
Uncontrolled Manifold ◽  
Position Vector ◽  
Movement Variability ◽  
Lateral Direction ◽  
Relevant Variable ◽  
The Right ◽  
Trained Runners

The motion of the human body can be described by the motion of its center of mass (CoM). Since the trajectory of the CoM is a crucial variable during running, one can assume that trained runners would try to keep their CoM trajectory constant from stride to stride. However, when exposed to fatigue, runners might have to adapt certain biomechanical parameters. The Uncontrolled Manifold approach (UCM) and the Tolerance, Noise, and Covariation (TNC) approach are used to analyze changes in movement variability while considering the overall task of keeping a certain task relevant variable constant. The purpose of this study was to investigate if and how runners adjust their CoM trajectory during a run to fatigue at a constant speed on a treadmill and how fatigue affects the variability of the CoM trajectory. Additionally, the results obtained with the TNC approach were compared to the results obtained with the UCM analysis in an earlier study on the same dataset. Therefore, two TNC analyses were conducted to assess effects of fatigue on the CoM trajectory from two viewpoints: one analyzing the CoM with respect to a lab coordinate system (PVlab) and another one analyzing the CoM with respect to the right foot (PVfoot). Full body kinematics of 13 healthy young athletes were captured in a rested and in a fatigued state and an anthropometric model was used to calculate the CoM based on the joint angles. Variability was quantified by the coefficient of variation of the length of the position vector of the CoM and by the components Tolerance, Noise, and Covariation which were analyzed both in 3D and the projections in the vertical, anterior-posterior and medio-lateral coordinate axes. Concerning PVlab we found that runners increased their stride-to-stride variability in medio-lateral direction (1%). Concerning PVfoot we found that runners lowered their CoM (4 mm) and increased their stride-to-stride variability in the absorption phase in both 3D and in the vertical direction. Although we identified statistically relevant differences between the two running states, we have to point out that the effects were small (CV ≤ 1%) and must be interpreted cautiously.

scholarly journals The Potential of Smartstone Probes in Landslide Experiments: How to Read Motion Data

2020 ◽  
Author(s):  
Bastian Dost ◽  
Oliver Gronz ◽  
Markus Casper ◽  
Andreas Krein
Keyword(s):  
Radio Frequency Identification ◽  
High Speed ◽  
Sampling Rate ◽  
Vertical Direction ◽  
Measurement Unit ◽  
External Information ◽  
Distance Measurements ◽  
Plastic Tube ◽  
Motion Data ◽  
Landslide Mass

Abstract. Currently, findings in landslide laboratory experiments are limited by observation techniques, which either deliver only external information (e.g. using high-speed videos), or internal information using wired sensors that confine the free motion of the mass. However, an unconfined internal observation of the internal dynamics of a moving landslide mass is essential for an adequate understanding of these natural hazards. The present study introduces an autonomous and wireless probe to characterise motion features of single clasts within artificial laboratory-scale landslides. The Smartstone probe is based on an inertial measurement unit (IMU) and records acceleration and rotation at a sampling rate of 100 Hz. The recording ranges are ± 16 g (accelerometer) and ± 2000° s−1 (gyroscope). The plastic tube housing is 55 mm long with a diameter of 10 mm. The probe is controlled and data is read out via active radio frequency identification (active RFID) technology. Due to this technique, the probe works under low-power conditions enabling the use of small button cell batteries and minimising its size. Using the Smartstone probe, the motion of approx. 520 kg of an uniformly-graded pebble material was observed in a laboratory experiment. Single pebbles were equipped with probes and placed embedded and superficially in/on the mass. In a first analysis step, the data of one pebble is interpreted qualitatively, allowing for the determination of different transport modes, such as translation, rotation and saltation. In a second step, the motion was quantified my means of derived movement characteristics: The analysed pebble moved mainly in vertical direction during the first motion phase with a maximal vertical velocity of approx. 1.7 m s−1. A strong acceleration peak of approx. 36 m s−2 was interpreted as pronounced hit and led to a complex rotational motion pattern. In a third step, displacement was derived and amounts to approx. 1.1 m in vertical direction. The deviation compared to laser distance measurements was approx. −10 %. Furthermore, a full 3-dimensional spatiotemporal trajectory of the pebble was reconstructed and visualised supporting the interpretations. Finally, it is demonstrated that multiple pebbles can be analysed simultaneously within one experiment, allowing for motion sampling of different parts of a moving landslide.

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