A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Khan, Taha; Lundgren, Lina; Järpe, Eric; Olsson, M. Charlotte; Viberg, Pelle

doi:10.3390/s19214729

Cited by 17 publications

(18 citation statements)

References 18 publications

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“…A machine learning approach to estimate the level of fatigue during running has been demonstrated previously by Khan et al using sEMG and blood lactate analysis [ 50 ]. However, this study only segmented fatigue into three classes (aerobic, anaerobic, and recovery phases based on blood lactate levels) whereas we were able to estimate fatigue accurately over a range of RPE from 13 to 20.…”

Section: Resultsmentioning

confidence: 99%

“…One major limitation of estimating fatigue level is the variability of kinematic changes between individuals [ 17 , 26 ]. For this reason, most of the previous studies have proposed intra-participant models [ 37 , 50 , 52 ]. However, using a motor learning approach may overcome this limitation and we aim to investigate this in future studies with larger and more diverse samples.…”

Section: Resultsmentioning

confidence: 99%

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Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Gholami

Napier

Patiño

et al. 2020

Sensors

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Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Gholami

Napier

Patiño

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…A reason to neglect the time course of the axial peak tibial acceleration may be that relevant changes in such a signal are usually not easily discernible by sight. The technique of change-point analysis may be of use to detect event(s) at which the underlying dynamics of a signal changes over time [17][18][19][20][21][22][23]. Several types of control statistics have been used for change-point discovery.…”

Section: Introductionmentioning

confidence: 99%

Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining

Berghe

Gosseries

Gerlo

et al. 2020

Sensors

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A method is presented for detecting changes in the axial peak tibial acceleration while adapting to self-discovered lower-impact running. Ten runners with high peak tibial acceleration were equipped with a wearable auditory biofeedback system. They ran on an athletic track without and with real-time auditory biofeedback at the instructed speed of 3.2 m·s −1 . Because inter-subject variation may underline the importance of individualized retraining, a change-point analysis was used for each subject. The tuned change-point application detected major and subtle changes in the time series. No changes were found in the no-biofeedback condition. In the biofeedback condition, a first change in the axial peak tibial acceleration occurred on average after 309 running gait cycles (3 40"). The major change was a mean reduction of 2.45 g which occurred after 699 running gait cycles (8 04") in this group. The time needed to achieve the major reduction varied considerably between subjects. Because of the individualized approach to gait retraining and its relatively quick response due to a strong sensorimotor coupling, we want to highlight the potential of a stand-alone biofeedback system that provides real-time, continuous, and auditory feedback in response to the axial peak tibial acceleration for lower-impact running.

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“…Another critical step of human motion classification is the selection of classification technology. Based on the above feature extraction methods, researchers mainly used support a vector machine (SVM), decision tree (DT), random forest (RF), nearest neighbor (KNN), and naive Bayes (NB) to classify human motion [ 27 , 28 , 29 , 30 , 31 , 32 ]. Rohit Gupta et al used a time-domain analysis method to classify the movement of the lower limbs, and concluded that the linear discriminant analysis (LDA) classifier had the highest accuracy, and for different feature subsets, the classification accuracy was between 89% and 99% [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Qin

Shi

2020

Entropy

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The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

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A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Cited by 17 publications

References 18 publications

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

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