Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

Prigent, Gaëlle; Apte, Salil; Paraschiv-Ionescu, Anisoara; Besson, Cyril; Grémeaux, Vincent; Aminian, Kamiar

doi:10.3389/fphys.2022.814172

Cited by 13 publications

(29 citation statements)

References 86 publications

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“…The error for cycle time prediction is the lowest, as cycle time prediction benefits from the cycle-time feature computed from the wrist motion patterns peaks. However, the contact time estimation should be improved, since contact time is an important marker for acute fatigue [13]. The error values show a good agreement with the errors for foot-based estimation [5].…”

Section: Model Validationsupporting

confidence: 54%

Estimation of Temporal Parameters During Running with a Wrist-worn Inertial Sensor: an In-field Validation

Kammoun

Apte

Karami

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The aim of this study was to estimate the temporal gait parameters using a wrist-worn Inertial Measurement Unit (IMU) during an outdoor run. While it is easier to compute running gait parameters using foot IMUs, a wrist IMU is more convenient and less obtrusive when it comes to data acquisition. During a track run of 12 minutes, we equipped 14 highly-trained male runners with one IMU on the wrist and one on each foot. We trained machine learning models based on CNN, GPR, and Lasso regression using wrist IMU signals and validated them with a foot-worn IMU reference system. Lasso model performed the best, with the accuracy for cycle time, swing time, flight time, and contact time being 0.27 % ± 0.1 %, 2.6 % ± 1.7 %, 7.3 % ± 4.9 %, and 10.6 % ± 5.5 %, respectively.

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Section: Model Validationsupporting

confidence: 54%

Estimation of Temporal Parameters During Running with a Wrist-worn Inertial Sensor: an In-field Validation

Kammoun

Apte

Karami

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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“…We first computed SI for the gait cycle time to check the validity of the SI, as the cycle time should present an SI close to zero. Following that, we used SI ( Figure 1 ) with four gait parameters, CT (CT SI ), flight time (FT SI ), swing time (ST SI ), and PSV (PSV SI ), based on their evolution with acute fatigue during endurance running (Apte et al, 2021 ; Prigent et al, 2022 ). All the computations were done using MATLAB R2020b, and the plots showing the evolution of biomechanical parameters and running speed during the Cooper test were created using the Gramm package (Morel, 2018 ) and smoothing (Eilers, 2003 ) for averaging the trajectories.…”

Section: Methodsmentioning

confidence: 99%

“…Fatigue : Acute fatigue has an adverse effect on technique during prolonged running, by increasing the CT, DF, reducing FSA, VS, and so on (Apte et al, 2021 ; Meyer et al, 2021 ; Prigent et al, 2022 ). The ability to maintain an efficient running technique for a longer duration can thus improve the endurance performance.…”

Section: Methodsmentioning

confidence: 99%

“…Apart from the sensor setup ( Figure 2C ), the participants dressed as they would for an endurance running race. The Fieldwiz and Physilog 5 wearable sensors were chosen because they have already been used successfully for continuous analysis of running in the field and do not hinder the running movement (Prigent et al, 2022 ). Fieldwiz was used with a sampling frequency of 200 Hz for the IMU, 250 Hz for the ECG, and 10 Hz for the GNSS receiver.…”

Section: Materials and Equipmentmentioning

confidence: 99%

“…Preece et al extended this approach to run overground over a distance of 32 m at four different fixed speeds and analyzed the running kinetics and kinematics at three different steps during the run (Preece et al, 2019 ). While these studies showed promising results and highlighted important biomechanical characteristics of high-performance runners, they did not account for the natural variability (Meardon et al, 2011 ; Mo and Chow, 2018 ) and asymmetry (Radzak et al, 2017 ; Beck et al, 2018 ) that occur at self-selected speeds, nor did they consider the effects of fatigue when running longer distances (Prigent et al, 2022 ), which are common in field tests of endurance capacity. The use of wearable IMU and global navigation satellite systems (GNSS) has shown promise in the improvement and augmentation of field testing for countermovement jump (Picerno et al, 2011 ), single-leg hop (Ahmadian et al, 2020 ), sprint (Apte et al, 2020 ), balance (Johnston et al, 2016 ), and so on.…”

Section: Introductionmentioning

confidence: 99%

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Augmented Cooper test: Biomechanical contributions to endurance performance

Apte

Troxler²,

Besson

et al. 2022

Front. Sports Act. Living

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Running mechanics are modifiable with training and adopting an economical running technique can improve running economy and hence performance. While field measurement of running economy is cumbersome, running mechanics can be assessed accurately and conveniently using wearable inertial measurement units (IMUs). In this work, we extended this wearables-based approach to the Cooper test, by assessing the relative contribution of running biomechanics to the endurance performance. Furthermore, we explored different methods of estimating the distance covered in the Cooper test using a wearable global navigation satellite system (GNSS) receiver. Thirty-three runners (18 highly trained and 15 recreational) performed an incremental laboratory treadmill test to measure their maximum aerobic speed (MAS) and speed at the second ventilatory threshold (sVT2). They completed a 12-minute Cooper running test with foot-worm IMUs and a chest-worn GNSS-IMU on a running track 1–2 weeks later. Using the GNSS receiver, an accurate estimation of the 12-minute distance was obtained (accuracy of 16.5 m and precision of 1.1%). Using this distance, we showed a reliable estimation [R2 > 0.9, RMSE ϵ (0.07, 0.25) km/h] of the MAS and sVT2. Biomechanical metrics were extracted using validated algorithm and their association with endurance performance was estimated. Additionally, the high-/low-performance runners were compared using pairwise statistical testing. All performance variables, MAS, sVT2, and average speed during Cooper test, were predicted with an acceptable error (R2 ≥ 0.65, RMSE ≤ 1.80 kmh−1) using only the biomechanical metrics. The most relevant metrics were used to develop a biomechanical profile representing the running technique and its temporal evolution with acute fatigue, identifying different profiles for runners with highest and lowest endurance performance. This profile could potentially be used in standardized functional capacity measurements to improve personalization of training and rehabilitation programs.

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Wearables for Running Gait Analysis: A Systematic Review

et al. 2022

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Background Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance. Objectives We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults. Methods A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings. Results A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (n = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers (n = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n = 93), using a treadmill (n = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards. Conclusions This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes. Clinical Trial Registration CRD42021235527.

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Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

Cited by 13 publications

References 86 publications

Estimation of Temporal Parameters During Running with a Wrist-worn Inertial Sensor: an In-field Validation

Estimation of Temporal Parameters During Running with a Wrist-worn Inertial Sensor: an In-field Validation

Augmented Cooper test: Biomechanical contributions to endurance performance

Wearables for Running Gait Analysis: A Systematic Review

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