Qualitative assessment of recurrent human motion

Ebert, Andre; Beck, Michael Till; Mattausch, Andy; Belzner, Lenz; Linnhoff‐Popien, Claudia

doi:10.23919/eusipco.2017.8081218

Cited by 11 publications

(14 citation statements)

References 13 publications

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“…However, the number of exercise executions that can be used to create rules/templates is limited. This reduces the scalability of these approaches [29]. Consequently, it is difficult to develop personalized solutions that can match the unique characteristics or impairments of each individual.…”

Section: Hmqa Using Machine Learningmentioning

confidence: 99%

“…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%

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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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Section: Hmqa Using Machine Learningmentioning

confidence: 99%

Section: ) Preprocessingmentioning

confidence: 99%

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

et al. 2022

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“…Some of the ideas rely on using sensors attached to the body. In [11], the authors gathered a dataset using five sensor devices attached to the ankles, wrists and chest in order to record six exercises performed by 27 athletes and to label the data with a qualitative rating from one to five.…”

Section: Related Workmentioning

confidence: 99%

IntelliRehabDS (IRDS)—A Dataset of Physical Rehabilitation Movements

Miron

Sadawi

Ismail

et al. 2021

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In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals.

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“…No artigo [34] é abordado o uso de sensores na roupa e a sua interligação com aplicativos para smartphone, de forma a rastrear o movimento do individuo durante o exercício físico abordando a questão da detecção do mau posicionamento, prevenção de lesões ou otimizar o sucesso do treinamento.…”

Section: Métodos Para Monitorizar Atletas De Alta Competiçãounclassified