Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data

Ross, Gwyneth B.; Dowling, Brittany; Troje, Nikolaus F.; Fischer, Steven L.; Graham, Ryan B.

doi:10.3389/fbioe.2020.00814

Cited by 12 publications

(18 citation statements)

References 33 publications

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“…Another notable disadvantage that developers aim to mitigate is the issue of drifting, especially when placed near metal or magnetic fields due to the inner magnetometers. 16 , 17 …”

Section: Introductionmentioning

confidence: 99%

“…In many of the previous works on gait analysis, ML models have been applied in ambulatory gait analysis and optical motion capture systems, whether in conjunction or as separate fields of studies. 16 , 17 Ambulatory gait analysis examines ‘normal walking bouts’ in healthy adults, such as running, walking. Regression models are generally preferred to evaluate gait parameters, such as walking speed, in ambulatory gait analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Another notable disadvantage that developers aim to mitigate is the issue of drifting, especially when placed near metal or magnetic fields due to the inner magnetometers. 16,17 Data collected from multiple motion sensors require a computing system to process the complex input. 4 The output from the wearable devices can be processed through either custom algorithms or artificial intelligence (AI) models.…”

Section: Introductionmentioning

confidence: 99%

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The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

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Background The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. Methods Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. Results A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. Conclusions Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

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“…Another notable disadvantage that developers aim to mitigate is the issue of drifting, especially when placed near metal or magnetic fields due to the inner magnetometers. 16 , 17 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

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“…Of the 88 publications, 29 applied a filter to their data (Table 8), with 16 in the healthcare domain, eight in the sports domain, and five in the wellness domain. The most common filter was the Butterworth filter, which was used in 18 publications (16 lowpass [29], [30], [32], [52], [54], [68], [69], [73], [76], [86], [105], [107], [108], [109], [112], [113], one high-pass [84], and one band-pass [50]), and then the moving average used in five publications [46], [92], [93], [94], [95]. Filters are most commonly applied to data captured from inertial sensors, with 14/39 publications that used inertial sensors applying them [29], [32], [50], [52], [68], [69], [73], [76], [102], [103], [109], [112], [113], [119].…”

Section: ) Preprocessingmentioning

confidence: 99%

“…The most common filter was the Butterworth filter, which was used in 18 publications (16 lowpass [29], [30], [32], [52], [54], [68], [69], [73], [76], [86], [105], [107], [108], [109], [112], [113], one high-pass [84], and one band-pass [50]), and then the moving average used in five publications [46], [92], [93], [94], [95]. Filters are most commonly applied to data captured from inertial sensors, with 14/39 publications that used inertial sensors applying them [29], [32], [50], [52], [68], [69], [73], [76], [102], [103], [109], [112], [113], [119]. Among the publications that used optical cameras, 8/39 that used RGBD data applied filters [30], [46], [81], [86], [92], [93], [94], [95], and 5/9 of the ones that used high-end optical based s...…”

Section: ) Preprocessingmentioning

confidence: 99%

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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Wearable electronic devices in parasports: A focused review on para athlete classification

Nikolis,

Graff,

Nikolis

et al. 2024

PM&R

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Wearable electronic devices are being used to evaluate movement patterns, track workload, prevent injuries, and optimize performance in athletes. Para athletes have unique characteristics to consider separately from the general population. One such difference in competitive parasports includes consideration of athlete classification systems. These sport‐specific classification systems consider athletes' eligible medical conditions that lead to permanent physical, visual, and/or intellectual impairments and objectively evaluate the impact of their impairments on sport functions. Para athletes are assigned sport classes and compete with athletes of similar functional levels. This promotes equitable opportunities to compete while minimizing the effect their impairment has on their sport performance. With recent reports of misrepresentation or incorrect and unfair classification providing unfair advantages to some athletes, parasports classification has been controversial. Having an objective, evidence‐based, and fair classification system in parasports is critical to maintain the integrity of parasports competition. Wearable electronic devices have potential to provide more objective analysis of movement patterns in para athletes to assist in sport classification. This review identifies and analyzes the available literature on wearable electronic technology and its role in parasports classification. A comprehensive PubMed and Google Scholar search identified six articles included in this review. These studies used inertial sensors, motion capture systems, or surface electromyography in wheelchair basketball, rugby, tennis, sit skiing, and boccia. This review identifies the potential value of wearable electronic devices to assist in parasports classification. Additional studies are needed to create standardized protocols for each sport and sensor type.

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Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data

Abstract: GR, RG, NT, and SF conceived the study and interpreted the results. GR and BD collected the data and performed the pre-processing of the data. GR implemented the OMAT, analyzed the results, and prepared the manuscript. All authors revised the manuscript.

Cited by 12 publications

References 33 publications

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

Wearable electronic devices in parasports: A focused review on para athlete classification

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