Towards an Automated Assessment of Musculoskeletal Insufficiencies

Dressler, Danny; Liapota, Pavlo; Löwe, Welf

doi:10.1007/978-981-13-8311-3_22

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…The first type focuses on detecting abnormalities in relatively long, repetitive move-ments [1,2,3,4,5,6,7,8,9], such as gait, as visualized in Figure 1. The second type of movements, on the other hand, focuses on assessing structured movements [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27], such as movements from the Fugl-Meyer Assessment (FMA) [28] or Berg Balance Scale (BBS) [29] medical assessments, as visualized in Figure 2, which usually have clear definitions of starting positions, ending positions, objectives and constraints. Fig.…”

Section: Movement Assessmentmentioning

confidence: 99%

“…No Powerlifting 3 [11] Rehabilitation - [12,13] FMA 2 [14,15] FMA 3 [16] BBS 14 [17,18] Deep Squats 1 - [19] Qigong+others 4+6 [20,21] Physiotherapy 5 [22] Shoulder Abduction 1 [23] General 3 [24] Brunnstrom Scale 9 [25] Rehabilitation 2 [26] Tai Chi 1 [27] Olympic Sports 9…”

Section: Movementmentioning

confidence: 99%

“…Hakim and Shimshoni [14,15] introduced a novel warping algorithm, which was based on the detection of temporal points-of-interest (PoIs) and on linearly warping the sequences between them, as illustrated in Figure 7. Dressler et al [17,18] introduced a novel DTW variation with skips, similarly to Hu et al [19]. Other novel approaches were introduced by Devanne et al [5], by Baptista et al [6] and by Yu and Xiong [26].…”

Section: Dataset Movement Typesmentioning

confidence: 99%

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A Comprehensive Review of Skeleton-based Movement Assessment Methods

Hakim

2020

Preprint

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The raising availability of 3D cameras and dramatic improvement of computer vision algorithms in the recent decade, accelerated the research of automatic movement assessment solutions. Such solutions can be implemented at home, using affordable equipment and dedicated software. In this paper, we divide the movement assessment task into secondary tasks and explain why they are needed and how they can be addressed. We review the recent solutions for automatic movement assessment from skeleton videos, comparing them by their objectives, features, movement domains and algorithmic approaches. In addition, we discuss the status of the research on this topic in a high level.

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Section: Movement Assessmentmentioning

confidence: 99%

Section: Movementmentioning

confidence: 99%

Section: Dataset Movement Typesmentioning

confidence: 99%

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A Comprehensive Review of Skeleton-based Movement Assessment Methods

Hakim

2020

Preprint

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“…In our previous studies, [2][3][4][5] we have developed a tool called the skeleton avatar technique (SAT) for automated assessment of human movements. In 3D SAT, a 3D sensor camera (Kinect) records human movements, estimates the joint position in each frame of the motion recording, and maps this information to a movement quality score with help of AI techniques.…”

Section: Introductionmentioning

confidence: 99%

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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“…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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Towards an Automated Assessment of Musculoskeletal Insufficiencies

Cited by 5 publications

References 17 publications

A Comprehensive Review of Skeleton-based Movement Assessment Methods

A Comprehensive Review of Skeleton-based Movement Assessment Methods

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

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

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