Binary classification of running fatigue using a single inertial measurement unit

Buckley, Cillian; O’Reilly, Martin; Whelan, Darragh; Farrell, A. Vallely; Clark, Lyra; Longo, Valter D.; Gilchrist, Michael D.; Caulfield, Brian

doi:10.1109/bsn.2017.7936040

Cited by 42 publications

(38 citation statements)

References 22 publications

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“…Semantic descriptors were reported for classification models that predicted athlete training background, experience and fatigue level. These included running (Buckley et al, 2017;Kobsar, Osis, Hettinga, & Ferber, 2014), rating of gymnastic routines (Reily, Zhang, & Hoff, 2017), soccer pass classification based on its quality (Horton, Gudmundsson, Chawla, & Estephan, 2014), cricket bowling legality (Qaisar et al, 2013;Salman, Qaisar, & Qamar, 2017), ski jump error analysis Brock, Ohgi, & Lee, 2017) and strength training technique deviations (M. A. O'Reilly, Whelan, Ward, Delahunt, & Caulfield, 2017a;M. O'Reilly, Whelan, Ward, Delahunt, & Caulfield, 2017).…”

Section: Experimental Designmentioning

confidence: 99%

“…Sample frequency and, or pixel resolution were reported in seven of the studies (Couceiro et al, 2013;Hachaj et al, 2015;Kasiri-Bidhendi et al, 2015;Kasiri et al, 2017;Montoliu et al, 2015;Victor et al, 2017;Zhu et al, 2006), with sample frequencies ranging from 30 Hz to 210 Hz. Buckley et al, 2017;Buthe et al, 2016;Groh et al, 2016Groh et al, , 2017Groh et al, , 2015Jensen et al, 2016;Kautz et al, 2017;Kelly et al, 2012;Ó Conaire et al, 2010;Pernek et al, 2015;Salman et al, 2017;Schuldhaus et al, 2015;Whiteside et al, 2017). The next highest tested were Naïve Bayesian (NB) (n = 8) (Buckley et al, 2017;Connaghan et al, 2011;Groh et al, 2016Groh et al, , 2017Groh et al, , 2015Kautz et al, 2017;Salman et al, 2017;Schuldhaus et al, 2015) and k-Nearest Neighbour (kNN) (n = 8) (Buckley et al, 2017;Groh et al, 2016Groh et al, , 2017Groh et al, , 2015Kautz et al, 2017;Ó Conaire et al, 2010;Salman et al, 2017;Whiteside et al, 2017), followed by Random Forests (RF) (n = 7) (Buckley et al, 2017;Groh et al, 2017;Salman et al, 2017;…”

Section: Vision Capture Specificationmentioning

confidence: 99%

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Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Cust

Sweeting

Ball

et al. 2018

Journal of Sports Sciences

219

148

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Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sportspecific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data preprocessing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental setup , data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).

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Section: Experimental Designmentioning

confidence: 99%

Section: Vision Capture Specificationmentioning

confidence: 99%

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Cust

Sweeting

Ball

et al. 2018

Journal of Sports Sciences

219

148

View full text Add to dashboard Cite

show abstract

“…Use of gait analysis for identifying fatigue level has been studied in prior studies [5,6,15]. Buckley et al were able to classify high-level fatigue using IMU attached to each of the lumbar spine and both shanks with a maximum of 77% accuracy [5]. However, they utilized running tasks for fatiguing the athletes, which had a fast pace fatiguing protocol with limited applicability in an industrial environment.…”

Section: Resultsmentioning

confidence: 99%

“…Borg's ratings of perceived exertion is one of the frequently used subjective measures to assess the exertion level [5,6], which combines the feelings of physical stress, and fatigue [7]. To reduce the inherent errors in this subjective measure, participants' perception of exertion was first calibrated by conducting the knee test similar to our earlier studies, where participants were asked to lean against the wall with their knees bent at 90° until their RPE reached a value of ≥18 [8,9].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, few researchers have used similar technologies to quantify human muscle fatigue. In a study to estimate fatigue during running, Buckley et al placed one IMU on each of the lumbar spine and both shanks [5]. They collected the IMU data before and after the fatiguing exercise and utilized the Borg's RPE of ≥18 as the fatigued level, where they have achieved a maximum accuracy of 77% in identifying fatigue.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

Karvekar

Abdollahi

Rashedi

2019

Preprint

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The fatigue due to repetitive and physically challenging jobs may result in workers' poor performance and Work-related Musculoskeletal Disorder (WMSD). Thus, it is imperative to frequently monitor fatigue and take necessary recovery actions. Our purpose was to develop a methodology to objectively classify subjects' fatigue level in the workplace utilizing the motion sensors embedded in the smartphones. An experiment consisting of twenty-four participants (12 M, 12 F) with a smartphone attached to their right shank was conducted using a fatiguing exercise (squatting), targeted mainly the lower extremity musculature. After each set of an exercise (2-min squatting), participants were asked about their ratings of perceived exertion (RPE), then a reference gait data were collected during a straight walk of 20-32 steps. This process was continued until they reported strong fatigue (≥17). Using the RPE to label the gait data, we have developed machine learning algorithms (i.e., binary and multi-class SVM models) to classify the individuals' gait into two (no-vs. strong-fatigue) and four levels (no-, low-, medium-, and strong-fatigue). The models reached the accuracies of 91% and 61% for two and four-level classification, respectively. The outcomes of this study may facilitate the implementation of a proactive approach in continuous monitoring of operators' fatigue level, which may subsequently increase the workers' performance and reduce the risk of WMSDs.

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Feature Subset Selection for Detecting Fatigue in Runners Using Time Series Sensor Data

Kathirgamanathan

Buckley

Caulfield

et al. 2022

Pattern Recognition and Artificial Intelligence

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Binary classification of running fatigue using a single inertial measurement unit

Cited by 42 publications

References 22 publications

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

Feature Subset Selection for Detecting Fatigue in Runners Using Time Series Sensor Data

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