Tibial acceleration-based prediction of maximal vertical loading rate during overground running : a machine learning approach

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 +/- 2.04 BW.s(-1), mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 +/- 7.90 BW.s(-1) (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA

NEURAL NETWORK METHOD TO PREDICTING STANCE-PHASE GROUND REACTION FORCE IN DISTANCE RUNNERS

The purpose of this study was to use machine learning (i.e., artificial neural network – ANN), to predict vertical ground reaction force (vGRF) from tibial accelerations in runners with different foot strike patterns and at different running speeds. Thirty-eight healthy runners ran at three different speeds: the pace at which the runner spends most of their training time (LSD), 15% faster than LSD (LSD15), and 30% faster than LSD (LSD30). vGRF and IMU-based accelerations from the tibia were collected during the last 30 seconds at each speed. Tibial accelerations were used to calculate the resultant tibial acceleration (RTA). Time-series stance-phase vGRF and RTA from 34 subjects at all three speeds were used to train the ANN. Trials from two males and two females, who exhibited different foot-strike patterns, were used to test the ANN. The prediction error of the ANN was 102.4 N (1.6 N/kg or 0.16 BW) across the entire stance phase of running. The ability to predict GRF with an ANN and only RTA as input appears to be practical and feasible

Both a single sacral marker and the whole-body center of mass accurately estimate peak vertical ground reaction force in running.

While running, the human body absorbs repetitive shocks with every step. These shocks can be quantified by the peak vertical ground reaction force (F &lt;sub&gt;v,max&lt;/sub&gt; ). To measure so, using a force plate is the gold standard method (GSM), but not always at hand. In this case, a motion capture system might be an alternative if it accurately estimates F &lt;sub&gt;v,max&lt;/sub&gt; . The purpose of this study was to estimate F &lt;sub&gt;v,max&lt;/sub&gt; based on motion capture data and validate the obtained estimates with force plate-based measures. One hundred and fifteen runners participated at this study and ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body kinematics (200 Hz) were acquired with an instrumented treadmill and an optoelectronic system, respectively. The vertical ground reaction force was reconstructed from either the whole-body center of mass (COM-M) or sacral marker (SACR-M) accelerations, calculated as the second derivative of their respective positions, and further low-pass filtered using several cutoff frequencies (2-20 Hz) and a fourth-order Butterworth filter. The most accurate estimations of F &lt;sub&gt;v,max&lt;/sub&gt; were obtained using 5 and 4 Hz cutoff frequencies for the filtering of COM and sacral marker accelerations, respectively. GSM, COM-M, and SACR-M were not significantly different at 11 km/h but were at 9 and 13 km/h. The comparison between GSM and COM-M or SACR-M for each speed depicted root mean square error (RMSE) smaller or equal to 0.17BW (≤6.5 %) and no systematic bias at 11 km/h but small systematic biases at 9 and 13 km/h (≤0.09 BW). COM-M gave systematic biases three times smaller than SACR-M and two times smaller RMSE. The findings of this study support the use of either COM-M or SACR-M using data filtered at 5 and 4 Hz, respectively, to estimate F &lt;sub&gt;v,max&lt;/sub&gt; during level treadmill runs at endurance speeds

The Comparison of Treadmill and Overground Running with the Use of Inertial Measurement Units (IMUs)

Treadmill running has historically been utilized in the laboratory to mimic outdoor running. Recent developments in portable technology, such as Inertial Measurement Units (IMUs) allow researchers to assess runners in their natural environment. PURPOSE: The primary purpose of the study was to compare peak tibial acceleration, stance time, stride frequency, and rearfoot eversion velocity between treadmill and overground running (asphalt, track, and grass). The secondary purpose was to test the reliability of IMU-based estimations of maximum rearfoot eversion velocity during treadmill running. METHODS: Twenty subjects (Age: 22.1 ± 2.0 yrs, Mass: 70.8 ± 11.9 kg, Height: 174.5 ± 10.0 cm, 8F/12M) participated. After consent and a warm-up period, IMUs were placed on the anteromedial aspect of the right distal tibia (1600Hz) and the posterior heel cap of the right shoe (1125 Hz) to record 3D linear accelerations and angular velocities. Subjects then ran three 30-meter trials on each overground surface (grass, track, and asphalt) at their self-selected speed. A timing system was used to ensure the same running speed was used between conditions. Subsequently, all subjects ran on a treadmill while their rearfoot motion was recorded through high-speed videography (240 Hz). Only one continuous trial was performed for each subject on the treadmill. In each of these trials, the variables of stride frequency, tibial acceleration, maximum rearfoot eversion velocity, and stance time were collected from the IMUs. Maximum rearfoot eversion velocity was the only variable processed from the rearfoot motion video data. A total of 30 steps from each condition were extracted and analyzed (10 steps from each trial for overground surfaces). In our statistical analysis, separate repeated measures ANOVAs or Friedman tests were performed on the mean and variability of each variable, depending on data normality, to examine differences between surface conditions. Post-hoc analysis was performed when appropriate through either Fisher’s LSD (α = 0.05) or Wilcoxon signed-rank tests (α = 0.008). RESULTS: The means of stride frequency, peak tibial acceleration, and maximum rearfoot eversion velocity were significantly different (p \u3c 0.001) between surfaces. Specifically, stride frequency was the fastest during treadmill running (1.39 (0.09) strides/sec) and slowest while running on grass (1.35 (0.07) strides/sec). Peak tibial acceleration was not different between the outdoor running conditions (asphalt: 11.0 (2.7) G, grass: 10.7 (3.4) G, and track: 10.6 (2.3) G), but significantly less during treadmill running (7.9 (1.6) G). Maximum rearfoot eversion velocity was lowest on grass (394.9 (256.5) deg/s) and greatest on track (623.5 (299.3) deg/s) and asphalt (620.7 (289.1) deg/sec). There was no difference in stance time between surfaces (p = 0.231). The variabilities of stance time, stride frequency, maximum rearfoot eversion velocity, and peak tibial acceleration were all found to be significantly different (p \u3c 0.001) between running surface. Treadmill running presented with the lowest levels of variability in stride frequency (0.016 (0.005) strides/sec), maximum rearfoot eversion velocity (70.10 (35.58) deg/s), and peak tibial acceleration (0.79 (0.32) G) across all conditions. Due to this, all variables other than stance time were significantly less variable during treadmill running than any of the overground conditions. In contrast, running on grass displayed significantly larger variabilities across all conditions in stance time (0.014 (0.009) sec), stride frequency (0.029 (0.010) strides/sec), maximum rearfoot eversion velocity (123.71 (36.16) deg/s), and peak tibial acceleration (2.41 (1.21) G). Lastly, maximum rearfoot eversion velocities acquired from the heel mounted IMU obtained a moderate reliability with the high-speed videography (ICC = 0.739, CI: 0.322 – 0.899). CONCLUSION: A single IMU appears to only be moderately reliable when recording eversion velocities during running, so caution may be warranted when using a single IMU to estimate rearfoot motion during running. With the use of IMUs, treadmill running has been shown to be different than overground in terms of both mean differences and variability. As a result, laboratory-based studies on running biomechanics may not truly reflect the “natural” gait used on outdoor running surfaces

Validation of a Device to Accurately Monitor Knee Kinematics during Dynamic Movements

The incidence of anterior cruciate ligament (ACL) injury in athletes who play multidirectional sports has increased over recent times. Female athletes are at a higher risk of sustaining the ACL injury when compared to their male counterparts involved in the same sport. Various intrinsic (anatomical and hormonal) and extrinsic (biomechanical) factors have been identified that contribute to the increased risk of injury. Sex differences in the kinematics and kinetics of the lower extremity between males and females have been identified while performing various physical tasks has been a topic of discussion since a long time. While it’s difficult to control the anatomical and hormonal factors, identifying and modifying the biomechanical factors that contribute to the ACL injury is possible. Wearable sensors involving inertial measurement units (IMUs) have been developed to monitor lower extremity motion and help in assistance with rehabilitation. The purpose of this study was to validate a set of wearable IMUs against a 3D motion analysis system to monitor the lower extremity motion during jumps and runs in a laboratory and to determine whether IMUs could be used to estimate ground reaction force at landing. An average difference of 5°-10° for flexion, 4°-6° abduction and internal rotation was reported during jump and run. The results of this study showed that correlation between ground reaction force and tibial acceleration is poor when data from all the subjects were included together. However, the correlation was improved when subjects were examined individually. A strong correlation was observed between the resultant ground reaction force and the resultant tibial acceleration during jumping and running between both the legs for the eight subjects when examined individually.Master of Science in EngineeringMechanical Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/146786/1/49698122_Thesis report_Ruchika Tadakala_36771515 edited (2).pdfDescription of 49698122_Thesis report_Ruchika Tadakala_36771515 edited (2).pdf : Thesi

THE EFFECTS OF MATURATION ON ACL LOADING, MUSCLE COORDINATION, AND METABOLIC COST IN ADOLESCENT FEMALE SOCCER PLAYERS

Anterior cruciate ligament (ACL) injuries remain a significant, season-ending injury in youth soccer, with young female athletes exhibiting higher incidence rates compared to young male athletes. ACL injury rates increase with older ages, with female soccer players 15 years or older being at an almost twofold increased rate of injury compared to young female soccer players. Several biomechanical and neuromuscular changes occur during the maturation process, becoming more prevalent in young females, that can place them at an increased risk for ACL injury. These biomechanical and neuromuscular changes can affect how efficient young females are when completing high-risk, dynamic tasks where injuries are prone to happen. Musculoskeletal modeling can provide researchers detailed information about how elements in the musculoskeletal system interact to produce movement and assist in identifying causal relationships between movement strategies and abnormal biomechanics. Specifically, this method offers an approach to estimate ACL loading, understand how individual muscles contribute to whole-body center of mass acceleration during risky movements, and analyze individual muscle energy consumption during dynamic tasks. Understanding how lower extremity musculature and maturation status affects ACL loading and movement efficiency during high-risk movements in young female soccer athletes can aid researchers and clinicians in creating improved injury prevention programs at the musculature level that may better target those who are at an increased of injury during high-risk tasks. The purpose of this study is to examine the effects of maturation on ACL loading, muscle coordination, and movement efficiency during unanticipated sidestep cutting and drop vertical jump in young female soccer players

COMPARISON OF TIBIAL IMPACT ACCELERATIONS: VIDEO VS ACCELEROMETER

This study compared tibial axial accelerations measured by video analysis and accelerometry. Twenty-two recreationally active adults performed three countermovement jumps. The landing tibial axial accelerations were assessed with video and an accelerometer. High reliability was demonstrated for the root mean square error between the assessment methods (ICCave = 0.872). Repeated measures ANOVA results revealed no instrumentation differences in the magnitude of the two acceleration peaks (toe and heel contact) and no difference between trials. However, first and second peaks occurred 9.6 and 4.0 ms earlier, respectively, when assessed by video. Accelerometry is a valid and reliable alternative to video analysis for the assessment of tibial impact accelerations if temporal characteristics are not of interest

PREDICTORS OF PELVIC ACCELERATION DURING TREADMILL RUNNING AT DIFFERENT STRIDE FREQUENCIES

The aim of this study was to examine predictors of peak vertical and anteroposterior pelvic acceleration during treadmill running. Participants ran at 9 km∙h-1 at their preferred stride frequency and at ± 5% of their preferred stride frequency. Coordinate and acceleration data were collected using a motion capture system and inertial measurement units. Linear mixed models showed that for every one standard deviation increase in the anteroposterior displacement from knee to ankle at initial contact, vertical pelvic acceleration increased by 2.18 m∙s-2 (p = 0.046). Additionally, for every one standard deviation increase in stride frequency, peak anteroposterior pelvic acceleration increased by 0.68 m∙s-2 (p = 0.035). Runners who suffer from injuries or pain at the pelvis may benefit from decreasing the anteroposterior displacement from their knee to their ankle at initial contact and reducing their stride frequency