Usefulness of Muscle Synergy Analysis in Individuals With Knee Osteoarthritis During Gait

Kubota, Keiichi; Hanawa, Hiroki; Yokoyama, Moeka; Kita, Sotaro; Hirata, Keisuke; Fujino, Tsutomu; Kokubun, Takanori; Ishibashi, Takehiko; Kanemura, Naohiko

doi:10.1109/tnsre.2020.3043831

Cited by 21 publications

(24 citation statements)

References 55 publications

(94 reference statements)

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“…In particular, simulated data have been used to assess the accuracy of NAIC with a controlled and varying number of muscle synergies, while the test on experimental data has been introduced to check if our criterion is able to give precise results in different tasks, experimental conditions and pre-processing steps. (12) and has been chosen to simulate bell-shaped physiological activations. The corresponding C profiles have then a length that is increasing when the number of synergies is higher in order to maintain separation across the different activations.…”

Section: Comparison With Other Model Selection Criteriamentioning

confidence: 99%

“…Some papers have shown that the level of motor impairment in pathological conditions [10]- [12] and the evolution of functional recovery [13]- [16] are related to the number of synergies describing the motor control structure. Thus, the objective and accurate assessment of such a number is mandatory to guarantee the repeatability of the results and the applicability of the approach to a clinical environment.…”

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confidence: 99%

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An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization

Ranaldi¹,

Marchis

Severini

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps.

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Section: Comparison With Other Model Selection Criteriamentioning

confidence: 99%

mentioning

confidence: 99%

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization

Ranaldi¹,

Marchis

Severini

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…"Conclusion" section concludes the research. Sprains [29] Muscular dystrophy [30] Epilepsy [31] Examples Tendinitis [32] Spinal muscular atrophy [33] Alzheimer's disease [34] Osteoarthritis [35] Muscle fatigue [36] Parkinson's disease [37] Data preparation…”

Section: Introductionmentioning

confidence: 99%

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

Zhu

Men²,

Ho³

et al. 2022

J Med Syst

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Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.

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“…Second, it supports clinicians make a more robust diagnosis by providing a computer-aided indicator and helps them effectively in monitoring patients' health conditions. Examples Sprains [3] Muscular dystrophy [4] Epilepsy [5] Tendinitis [6] Spinal muscular atrophy [7] Alzheimer's disease [8] Osteoarthritis [9] Muscle fatigue [10] Parkinson's disease [11] Machine learning (ML) and deep learning (DL) have been widely used for automatically identifying health issues [12][13][14][15][16][17]. For example, by using 3D motion analysis, support vector machines (SVM) were applied for Parkinson's disease classification in [18] from gait signals.…”

Section: Introductionmentioning

confidence: 99%

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

Zhu¹,

Men²,

Ho³

et al. 2022

Preprint

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Usefulness of Muscle Synergy Analysis in Individuals With Knee Osteoarthritis During Gait

Cited by 21 publications

References 55 publications

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

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