Effective Quantization Evaluation Method of Functional Movement Screening with Improved Gaussian Mixture Model

Hong, Ruiwei; Xing, Qingjun; Shen, Yuanyuan; Shen, Yanfei

doi:10.3390/app13137487

Cited by 3 publications

(6 citation statements)

References 31 publications

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“…Unless otherwise specified, the AM used in this work defaults to Simple-AM. This paper conducts comparative experiments on a single action between the FMS dataset and the improved Gaussian mixture model (GMM) [1]. It also conducts comparative experiments with the advanced methods of video action quality evaluation in this paper, verifying that the proposed method can effectively predict the FMS dataset and achieve excellent results.…”

Section: Analysis Of Prediction Resultsmentioning

confidence: 79%

“…In recent years, researchers have introduced a multitude of methods to automate the evaluation of FMS. Hong et al [1] utilized a Gaussian mixture model and explored various machine learning techniques, such as naïve Bayes, AdaBoost, M1, and traditional Gaussian models, to address this issue. However, these methods face difficulties in modeling complex human motion patterns, which can lead to a decrease in accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Evaluation of Functional Movement Screening Based on Attention Mechanism and Score Distribution Prediction

Lin,

Huang,

Ruan

et al. 2023

Mathematics

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Functional movement screening (FMS) is a crucial testing method that evaluates fundamental movement patterns in the human body and identifies functional limitations. However, due to the inherent complexity of human movements, the automated assessment of FMS poses significant challenges. Prior methodologies have struggled to effectively capture and model critical human features in video data. To address this challenge, this paper introduces an automatic assessment approach for FMS by leveraging deep learning techniques. The proposed method harnesses an I3D network to extract spatiotemporal video features across various scales and levels. Additionally, an attention mechanism (AM) module is incorporated to enable the network to focus more on human movement characteristics, enhancing its sensitivity to diverse location features. Furthermore, the multilayer perceptron (MLP) module is employed to effectively discern intricate patterns and features within the input data, facilitating its classification into multiple categories. Experimental evaluations conducted on publicly available datasets demonstrate that the proposed approach achieves state-of-the-art performance levels. Notably, in comparison to existing state-of-the-art (SOTA) methods, this approach exhibits a marked improvement in accuracy. These results corroborate the efficacy of the I3D-AM-MLP framework, indicating its significance in extracting advanced human movement feature expressions and automating the assessment of functional movement screening.

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Section: Analysis Of Prediction Resultsmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Functional Movement Screening Based on Attention Mechanism and Score Distribution Prediction

Lin,

Huang,

Ruan

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…However, it increases the computational complexity of the network, especially in situations where a large quantity of computational resources are needed. Hong et al [7] proposed an automatic FMS evaluation method based on an improved Gaussian mixture model (GMM). The authors trained the GMM using feature data with various scores.…”

Section: Introductionmentioning

confidence: 99%

“…The study combines the feature fusion methods of optical flow and RGB stream to address [5,6] the challenge of fusing RGB with dual streams in the case of an insufficient dataset scale and diversity, which makes training data features become more specific. According to [7,8], the deficiencies of poor training outcomes on unknown training sets, and unsatisfactory feature extraction, this study aimed to enhance the robustness of training results by achieving RGB and dual-stream fusion using some fusion methods from feature fusion. The core contributions of this paper can be summarized in the following three aspects, which involve innovation in data fusion techniques and its significant effects on performance improvement:…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation Method for Functional Movement Screening Based on a Dual-Stream Network and Feature Fusion

Lin,

Chen,

Feng

et al. 2024

Mathematics

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Functional Movement Screening (FMS) is a movement pattern quality assessment system used to assess basic movement capabilities such as flexibility, stability, and pliability. Movement impairments and abnormal postures can be identified through peculiar movements and postures of the body. The reliability, validity, and accuracy of functional movement screening are difficult to test due to the subjective nature of the assessment. In this sense, this paper presents an automatic evaluation method for functional movement screening based on a dual-stream network and feature fusion. First, the RAFT algorithm is used to estimate the optical flow of a video, generating a set of optical flow images to represent the motion between consecutive frames. By inputting optical flow images and original video frames separately into the I3D model, it can better capture spatiotemporal features compared to the single-stream method. Meanwhile, this paper introduces a simple but effective attention fusion method that combines features extracted from optical flow with the original frames, enabling the network to focus on the most relevant parts of the input data, thereby improving prediction accuracy. The prediction of the four categories of FMS results was performed. It produced better correlation results compared to other more complex fusion protocols, with an accuracy improvement of 3% over the best-performing fusion method. Tests on public datasets showed that the evaluation metrics of the method proposed in this paper were the most advanced, with an accuracy improvement of approximately 4% compared to the currently superior methods. The use of deep learning methods makes it more objective and reliable to identify human movement impairments and abnormal postures.

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“…These methods require less data and offer interpretability through manual feature extraction. Moreover, Hong et al [13] demonstrated the feasibility of using depth cameras in FMS assessment by collecting a dataset using Azure Kinect depth sensors. They improved the accuracy of FMS assessment by forming a robust classifier that combines three Gaussian mixture models, each trained on datasets with different scores.…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder

Lin,

Liu,

Feng

et al. 2024

Electronics

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Functional Movement Screening (FMS) is a test used to evaluate fundamental movement patterns in the human body and identify functional limitations. However, the challenge of carrying out an automated assessment of FMS is that complex human movements are difficult to model accurately and efficiently. To address this challenge, this paper proposes an automatic evaluation method for FMS based on a multi-scale lightweight 3D convolution encoder–decoder (ML3D-ED) architecture. This method adopts a self-built multi-scale lightweight 3D convolution architecture to extract features from videos. The extracted features are then processed using an encoder–decoder architecture and probabilistic integration technique to effectively predict the final score distribution. This architecture, compared with the traditional Two-Stream Inflated 3D ConvNet (I3D) network, offers a better performance and accuracy in capturing advanced human movement features in temporal and spatial dimensions. Specifically, the ML3D-ED backbone network reduces the number of parameters by 59.5% and the computational cost by 77.7% when compared to I3D. Experiments have shown that ML3D-ED achieves an accuracy of 93.33% on public datasets, demonstrating an improvement of approximately 9% over the best existing method. This outcome demonstrates the effectiveness of and advancements made by the ML3D-ED architecture and probabilistic integration technique in extracting advanced human movement features and evaluating functional movements.

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Effective Quantization Evaluation Method of Functional Movement Screening with Improved Gaussian Mixture Model

Cited by 3 publications

References 31 publications

Automatic Evaluation of Functional Movement Screening Based on Attention Mechanism and Score Distribution Prediction

Automatic Evaluation of Functional Movement Screening Based on Attention Mechanism and Score Distribution Prediction

Automatic Evaluation Method for Functional Movement Screening Based on a Dual-Stream Network and Feature Fusion

Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder

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