Dataset Information

Development of a Human Activity Recognition System for Ballet Tasks.

ABSTRACT: BACKGROUND:Accurate and detailed measurement of a dancer's training volume is a key requirement to understanding the relationship between a dancer's pain and training volume. Currently, no system capable of quantifying a dancer's training volume, with respect to specific movement activities, exists. The application of machine learning models to wearable sensor data for human activity recognition in sport has previously been applied to cricket, tennis and rugby. Thus, the purpose of this study was to develop a human activity recognition system using wearable sensor data to accurately identify key ballet movements (jumping and lifting the leg). Our primary objective was to determine if machine learning can accurately identify key ballet movements during dance training. The secondary objective was to determine the influence of the location and number of sensors on accuracy. RESULTS:Convolutional neural networks were applied to develop two models for every combination of six sensors (6, 5, 4, 3, etc.) with and without the inclusion of transition movements. At the first level of classification, including data from all sensors, without transitions, the model performed with 97.8% accuracy. The degree of accuracy reduced at the second (83.0%) and third (75.1%) levels of classification. The degree of accuracy reduced with inclusion of transitions, reduction in the number of sensors and various sensor combinations. CONCLUSION:The models developed were robust enough to identify jumping and leg lifting tasks in real-world exposures in dancers. The system provides a novel method for measuring dancer training volume through quantification of specific movement tasks. Such a system can be used to further understand the relationship between dancers' pain and training volume and for athlete monitoring systems. Further, this provides a proof of concept which can be easily translated to other lower limb dominant sporting activities.

SUBMITTER: Hendry D

PROVIDER: S-EPMC7007459 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Development of a Human Activity Recognition System for Ballet Tasks.

Hendry Danica D Chai Kevin K Campbell Amity A Hopper Luke L O'Sullivan Peter P Straker Leon L

Sports medicine - open 20200207 1

<h4>Background</h4>Accurate and detailed measurement of a dancer's training volume is a key requirement to understanding the relationship between a dancer's pain and training volume. Currently, no system capable of quantifying a dancer's training volume, with respect to specific movement activities, exists. The application of machine learning models to wearable sensor data for human activity recognition in sport has previously been applied to cricket, tennis and rugby. Thus, the purpose of this ...[more]

PMID: 32034560

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Project description:BackgroundElectrocorticography studies are typically conducted in patients undergoing video EEG monitoring, but these studies are subject to confounds such as the effects of pain, recent anesthesia, analgesics, drug changes, antibiotics, and implant effects.New methodTechniques were developed to obtain electrocorticographic (ECoG) data from freely moving subjects performing navigational tasks using the RNS® System (NeuroPace, Inc., Mountain View, CA), a brain-responsive neurostimulation medical device used to treat focal onset epilepsy, and to align data from the RNS System with cognitive task events with high precision. These subjects had not had recent surgery, and were therefore not confounded by the perioperative variables that affect video EEG studies.ResultsTask synchronization using the synchronization marker technique provides a quantitative measure of clock uncertainty, and can align data to task events with less than 4 ms of uncertainty. Hippocampal ECoG activity was found to change immediately before an incorrect response to a math problem compared to hippocampal activity before a correct response. In addition, subjects were found to have variable but significant changes in theta band power in the hippocampus during navigation compared to when subjects were not navigating. We found that there is theta-gamma phase-amplitude coupling in the right hippocampus while subjects stand still during a navigation task.Comparison with existing methodsAn alignment technique described in this study improves the upper bound on task-ECoG alignment uncertainty from approximately 30 ms to under 4 ms. The RNS System is one of the first platforms capable of providing untethered ambulatory ECoG recording in humans, allowing for the study of real world instead of virtual navigation. Compared to intracranial video EEG studies, studies using the RNS System platform are not subject to confounds caused by the drugs and recent surgery inherent to the perioperative environment. Furthermore, these subjects provide the opportunity to record from the same electrodes over the course of many years.ConclusionsThe RNS System enables us to study human navigation with unprecedented clarity. While RNS System patients have fewer electrodes implanted than video EEG patients, the lack of external artifact and confounds from recent surgery make this system a useful tool to further human electrophysiology research.

Project description:ObjectiveLittle is known about how female adolescent ballet dancers-a group at high-risk for the development of body dissatisfaction and eating disorders-construct body ideals, and how their social identities interact with body ideals to confer risk for disordered eating. Using a novel body figure behavioral task, this study investigated (1) whether degree of body dissatisfaction corresponded to severity of disordered eating thoughts and behaviors, and (2) how ballet identity corresponded with ideal body figure size among adolescent ballet dancers.MethodsParticipants were 188 female ballet dancers ages 13-18 years who completed self-report measures of study constructs and the behavioral task.ResultsLinear regression models indicated that more severe body dissatisfaction was positively associated with increased disordered eating thoughts and behaviors (p < .19), except for muscle building (p = .32). We also found that identifying more strongly as a ballet dancer was correlated with having a smaller ideal body size (p = .017).ConclusionFindings from this study suggest desire to achieve smaller body sizes is correlated with more severe disordered eating endorsement and stronger ballet identity. Instructors and clinicians may consider assessing the extent to which individuals identify as a ballet dancer as a risk factor for disordered eating and encourage adolescent dancers to build and nurture other identities beyond ballet.Public significanceEating disorders are debilitating conditions that can lead to malnutrition, social isolation, and even premature death. Though disordered eating thoughts and behaviors can affect anyone, adolescents in physically demanding and body image-driven activities including ballet dance are particularly vulnerable. Investigating how factors like body dissatisfaction and strength of identity are associated with disordered eating among high-risk groups is crucial for developing effective prevention and intervention methods that minimize harm.

Dataset Information

Development of a Human Activity Recognition System for Ballet Tasks.

Publications

Development of a Human Activity Recognition System for Ballet Tasks.

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