The Performance of Some Machine Learning Approaches in Human Movement Assessment

Hagelbäck, Johan; Liapota, Pavlo; Lincke, Alisa; Löwe, Welf

doi:10.33965/eh2019_201910l005

Cited by 3 publications

(15 citation statements)

References 20 publications

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“…As only the optimal executions of an exercise are required for the assessment, it can be implemented even with a few representative data samples. However, if there are samples across a wider range of motion quality, a model can also be developed by learning associations of features with different scores [46]. Regression assessment can be applied using a unified regression model across a set of different motions.…”

Section: A: Regression-based Assessmentsmentioning

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%

“…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 systems [54], [98], [105], [107], [108].…”

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: A: Regression-based Assessmentsmentioning

confidence: 99%

Section: ) Preprocessingmentioning

confidence: 99%

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 Kinect sensor camera was placed horizontally (no tilt angle) in 50-cm height. The participants were asked to stand in front of the camera before each test, so that the SAT could detect the person's full body (Dressler et al, 2019a;Hagelbäck et al, 2019a) in order to assess functional ability (mobility and balance). SAT data are 3D skeleton avatar sequences of the movements of the person performing FT.…”

Section: Data Collectionmentioning

confidence: 99%

“…Machine learning approaches map recorded movement instances to expert scores providing an automated assessment of the movements (Dressler et al, 2019a;Hagelbäck et al, 2019a). The so-called skeleton avatar technique (Dressler et al, 2019b) refers to the pipeline of hardware, software, and artificial intelligence components that records human movements with a 3D sensor, estimates the position of joints in each frame of the movement recording, and maps this information to a movement quality score.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Skeleton Avatar Technique for Assessment of Mobility and Balance Among Older Adults

Backåberg¹,

Hellström²,

Fagerström³

et al. 2020

Front. Comput. Sci.

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Background: Mobility and balance is essential for older adults' well-being and independence and the ability to maintain physically active. Early identification of functional impairment may enable early risk-of-fall assessments and preventive measures. There is a need to find new solutions to assess functional ability in easy, efficient, and accurate ways, which can be clinically used frequently and repetitively. Therefore, we need to understand how functional tests and expert assessments (EAs) correlate with new techniques.Objective: To explore whether the skeleton avatar technique (SAT) can predict the results of functional tests (FTs) of mobility and balance: Timed Up and Go (TUG), the 30-s chair stand test (30sCST), the 4-stage balance test (4SBT), and EA scoring of movement quality.Methods: Fifty-four older adults (+65 years) were recruited through pensioners' associations. The test procedure contained three standardized FTs: TUG, 30sCST, and 4SBT. The test performances were recorded using a three-dimensional SAT camera. EA scoring was performed based on the video recordings of the 30sCST. Functional ability scores were aggregated from balance and mobility scores. Probability theory-based statistical analyses were used on the data to aggregate sets of individual variables into scores, with correlation analysis used to assess the dependency between variables and between scores. Machine learning techniques were used to assess the appropriateness of easily observable variables/scores as predictors of the other variables included.Results: The results indicate that SAT data of the fourth 4SBT stage could be used to predict the aggregated results of all stages of 4SBT (with 7.82% mean absolute error), the results of the 30sCST (11.0%), the TUG test (8.03%), and the EA of the sit-to-stand movement (8.79%). There is a moderate (significant) correlation between the 30sCST and the 4SBT (0.31, p = 0.03), but not between the EA and the 30sCST.Conclusion: SAT can predict the results of the 4SBT, the 30sCST (moderate accuracy), and the TUG test and might add important qualitative information to the assessment of movement performance in active older adults. SAT might in the future provide the means for a simple, easy, and accessible assessment of functional ability among older adults.

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A comparative study of the 2D- and 3D-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults

Lincke,

Fagerström,

Ekstedt

et al. 2023

Health Informatics J

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Background: Maintaining physical activity (PA) and functioning (mobility, balance) is essential for older adults’ well-being and quality of life. However, current methods (functional tests, self-reports) and available techniques (accelerometers, sensors, advanced movement analysis systems) for assessing physical activity and functioning have shown to be less reliable, time- and resource-consuming with limited routine usage in clinical practice. There is a need to simplify the assessment of physical activity and functioning among older adults both in health care and clinical studies. This work presents a study on using Skeleton Avatar Technology (SAT) for this assessment. SAT analyzes human movement videos using artificial intelligence (AI). The study compares handy SAT based on 2D camera technology (2D SAT) with previously studied 3D SAT for assessing physical activity and functioning in older adults. Objective: To explore whether 2D SAT yields accurate results in physical activity and functioning assessment in healthy older adults, statistically compared to the accuracy of 3D SAT. Method: The mobile pose estimation model provided by Tensorflow was used to extract 2D skeletons from the video recordings of functional test movements. Deep neural networks were used to predict the outcomes of functional tests (FT), expert-based movement quality assessment (EA), accelerometer-based assessments (AC), and self-assessments of PA (SA). To compare the accuracy with 3D SAT models, statistical analysis was used to test whether the difference in the predictions between 2D and 3D models is significant or not. Results: Overall, the accuracy of 2D SAT is lower than 3D SAT in predicting FTs and EA. 2D SAT was able to predict AC with 7% Mean Absolute Error (MAE), and self-assessed PA (SA) with 16% MAE. On average MAE was 4% higher for 2D than for 3D SAT. There was no significant difference found between the 2D and the 3D model for AC and for two FTs (30 seconds chair stand test, 30sCST and Timed up and go, TUG). A significant difference was found for the 2D- and 3D-model of another FT (4-stage balance test, 4SBT). Conclusion: Altogether, the results show that handy 2D SAT might be used for assessing physical activity in older adults without a significant loss of accuracy compared to time-consuming standard tests and to bulky 3D SAT-based assessments. However, the accuracy of 2D SAT in assessing physical functioning should be improved. Taken together, this study shows promising results to use 2D SAT for assessing physical activity in healthy older adults in future clinical studies and clinical practice.

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The Performance of Some Machine Learning Approaches in Human Movement Assessment

Cited by 3 publications

References 20 publications

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

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

Evaluation of the Skeleton Avatar Technique for Assessment of Mobility and Balance Among Older Adults

A comparative study of the 2D- and 3D-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults

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