The Use of a Single Trunk-Mounted Accelerometer to Detect Changes in Center of Mass Motion Linked to Lower-Leg Overuse Injuries: A Prospective Study

Pla, Gerard Aristizábal; Hollville, Enzo; Schütte, Kurt; Vanwanseele, Benedicte

doi:10.3390/s21217385

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…RMSR VT decreased, while RMSR ML and RMSR AP values increased; although, for RMSR AP , only the ALL method was significant. These results are similar to previous results [47,54], such as Winter et al [55], who reported decreased RMSR VT and increased RMSR ML . Schütte et al [48] also reported an increase in RMSR ML , and in another study, [12] reported increased RMSR ML and RMSR AP .…”

Section: Discussionsupporting

confidence: 93%

“…There were also main effects for fatigue state for SE, with SE VT and SE RES increasing significantly, SE ML decreasing significantly, and no changes observed in SE AP . These results are similar to Pla et al [47], who demonstrated increases in SE VT with fatigue, but also observed an increase in SE AP . However, Schütte et al [12] reported contrary results, with no fatigue-related changes in SE VT or SE ML , but significant increases in SE AP .…”

Section: Discussionsupporting

confidence: 92%

“…Acceleration data were not filtered, and raw signals were maintained for feature creation [9,11,34]. Features were chosen based on previous literature [8,29] and were extracted from the three primary axes (VT, ML, AP) and the resultant (RES) of the acceleration signal: mean, standard deviation, median, 25th percentile, 75th percentile, RMS [5,6,46], maximum, minimum [5,11], and sample entropy (SE) [11,12,[47][48][49]. The SE signal stabilizes over 2000 data points [50], and the minimum number of data points in each of our samples was ~3400 (corresponding to a cadence of 180 steps per minute), indicating that there were sufficient data for the calculation.…”

Section: Feature Extractionmentioning

confidence: 99%

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Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed

Dimmick

Rassel

Ferber

2022

Sensors

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The purpose of this study was to determine if fatigue-related changes in biomechanics derived from an inertial measurement unit (IMU) placed at the center of mass (CoM) are reliable day-to-day. Sixteen runners performed two runs at maximal lactate steady state (MLSS) on a treadmill, one run 5% above MLSS speed, and one run 5% below MLSS speed while wearing a CoM-mounted IMU. Trials were performed to volitional exhaustion or a specified termination time. IMU features were derived from each axis and the resultant. Feature means were calculated for each subject during non-fatigued and fatigued states. Comparisons were performed between the two trials at MLSS and between all four trials. The only significant fatigue state × trial interaction was the 25th percentile of the results when comparing all trials. There were no main effects for trial for either comparison method. There were main effects for fatigue state for most features in both comparison methods. Reliability, measured by an intraclass coefficient (ICC), was good-to-excellent for most features. These results suggest that fatigue-related changes in biomechanics derived from a CoM-mounted IMU are reliable day-to-day when participants ran at or around MLSS and are not significantly affected by slight deviations in speed.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 92%

Section: Feature Extractionmentioning

confidence: 99%

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Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed

Dimmick

Rassel

Ferber

2022

Sensors

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“…Thirty-nine features were extracted from the acceleration signals of each sample: mean, standard deviation, median, 25th percentile, 75th percentile, root mean square (RMS), maximum, minimum, sample entropy calculated from the three primary axes (vertical, medio-lateral, anterior-posterior) and the resultant (17,28,39,(42)(43)(44)(45)(46)(47)(48), and the ratio of single-axis RMS to resultant RMS (49). These features were selected based on previous analyses (31) and were identified due to their typical inclusion as features in machine learning models.…”

Section: Data Processingmentioning

confidence: 99%

Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach

Dimmick,

van Rassel,

MacInnis

et al. 2023

Front. Sports Act. Living

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Running biomechanics are affected by fatiguing or prolonged runs. However, no evidence to date has conclusively linked this effect to running-related injury (RRI) development or performance implications. Previous investigations using subject-specific models in running have demonstrated higher accuracy than group-based models, however, this has been infrequently applied to fatigue. In this study, two experiments were conducted to determine whether subject-specific models outperformed group-based models to classify running biomechanics during non-fatigued and fatigued conditions. In the first experiment, 16 participants performed four treadmill runs at or around the maximal lactate steady state. In the second experiment, nine participants performed five prolonged runs using commercial wearable devices. For each experiment, two segments were extracted from each trial from early and late in the run. For each participant, a random forest model was applied with a leave-one-run-out cross-validation to classify between the early (non-fatigued) and late (fatigued) segments. Additionally, group-based classifiers with a leave-one-subject-out cross validation were constructed. For experiment 1, mean classification accuracies for the single-subject and group-based classifiers were 68.2 ± 8.2% and 57.0 ± 8.9%, respectively. For experiment 2, mean classification accuracies for the single-subject and group-based classifiers were 68.9 ± 17.1% and 61.5 ± 11.7%, respectively. Variable importance rankings were consistent within participants, but these rankings differed from each participant to those of the group. Although the classification accuracies were relatively low, these findings highlight the advantage of subject-specific classifiers to detect changes in running biomechanics with fatigue and indicate the potential of using big data and wearable technology approaches in future research to determine possible connections between biomechanics and RRI.

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“…By analysing the patterns of the acceleration and gyroscope signals of the IMUs, initial contacts and toe-offs can be detected [6,[12][13][14]. With the detection of these gait events, it is possible to subsequently calculate spatiotemporal variables, such as step rate, ground contact time, flight time, and step time [15][16][17][18][19][20], and kinematic variables, such as the vertical center of mass displacement (COM) [21]. In addition, from a kinematic parameter, such as acceleration, kinetic variables, such as ground reaction forces (GRF), shock attenuation (SA), and the peak positive attenuation of the tibia can be estimated [22].…”

Section: Introductionmentioning

confidence: 99%

Parameterization of Biomechanical Variables through Inertial Measurement Units (IMUs) in Occasional Healthy Runners

Pareja-Cano,

Arjona,

Caulfield

et al. 2024

Sensors

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Running is one of the most popular sports practiced today and biomechanical variables are fundamental to understanding it. The main objectives of this study are to describe kinetic, kinematic, and spatiotemporal variables measured using four inertial measurement units (IMUs) in runners during treadmill running, investigate the relationships between these variables, and describe differences associated with different data sampling and averaging strategies. A total of 22 healthy recreational runners (M age = 28 ± 5.57 yrs) participated in treadmill measurements, running at their preferred speed (M = 10.1 ± 1.9 km/h) with a set-up of four IMUs placed on tibias and the lumbar area. Raw data was processed and analysed over selections spanning 30 s, 30 steps and 1 step. Very strong positive associations were obtained between the same family variables in all selections. The temporal variables were inversely associated with the step rate variable in the selection of 30 s and 30 steps of data. There were moderate associations between kinetic (forces) and kinematic (displacement) variables. There were no significant differences between the biomechanics variables in any selection. Our results suggest that a 4-IMU set-up, as presented in this study, is a viable approach for parameterization of the biomechanical variables in running, and also that there are no significant differences in the biomechanical variables studied independently, if we select data from 30 s, 30 steps or 1 step for processing and analysis. These results can assist in the methodological aspects of protocol design in future running research.

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The Use of a Single Trunk-Mounted Accelerometer to Detect Changes in Center of Mass Motion Linked to Lower-Leg Overuse Injuries: A Prospective Study

Cited by 7 publications

References 24 publications

Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed

Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed

Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach

Parameterization of Biomechanical Variables through Inertial Measurement Units (IMUs) in Occasional Healthy Runners

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