Compressive sensing based recognition of human upper limb motions with kinect skeletal data

Ashwini, K.; Amutha, R.

doi:10.1007/s11042-020-10327-4

Cited by 16 publications

(5 citation statements)

References 49 publications

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“…The accuracy of MPL-CNN in classifying upper limb rehabilitation movements is 99.22%. The upper limb movement detection network structure based on MPL-CNN proposed in this study has an accuracy increase of 2% compared with the method proposed by Ashwini [ 38 ]. Compared with machine learning methods [ 39 ], the accuracy has also been improved to a certain extent.…”

Section: Methodsmentioning

confidence: 94%

Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN

Shi,

Wang,

Zhao

et al. 2024

Sensors

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Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe’s action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient’s upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method’s recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient’s rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.

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Section: Methodsmentioning

confidence: 94%

Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN

Shi,

Wang,

Zhao

et al. 2024

Sensors

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“…First, we conduct thorough ablation research on KARD to evaluate the efficacy of the dependence feature matrix we have suggested and the fully gated attention. On the dataset (as shown in Table 3), we compare our model to state-of-the-art models [28,30,33], before analyzing how much efficiency gain there is on target datasets. We show that pretraining has a major impact on how effectively our model generalizes.…”

Section: Methods Accuracy (%)mentioning

confidence: 99%

“…Table 1 has the values UTD-MHAD [18][19][20][21][22][23][24]. In Table 2, cuttingedge methods [25][26][27][28][29] and state-of-the-art approaches [28,[30][31][32][33] are contrasted in Table 3. The state-of-the-art approaches [34][35][36][37][38][39] are contrasted in Table 4.…”

Section: Evaluation Of Fgp-3dmentioning

confidence: 99%

Employing FGP-3D, a Fully Gated and Anchored Methodology, to Identify Skeleton-Based Action Recognition

et al. 2023

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Recent years have seen an explosion in interest in and development of action recognition based on skeletal data. Contemporary methods using fully gated units can successfully extract characteristics from human skeletons by relying on the human topology that has been predefined. Despite advancements, fully gated unit-based techniques have trouble generalizing to other domains, particularly when dealing with various human topological structures. In this context, we introduce FGP-3D, a novel skeleton-based action recognition technique that can generalize across datasets while being effective at learning spatiotemporal features from human skeleton sequences. This is accomplished via a multi-head attention technique to learn an ideal dependence feature matrix from the uniform distribution. We next re-evaluate state-of-the-art techniques as well as the suggested novel descriptor FGP-3D in order to examine the cross-domain generalizability of skeleton-based action recognition in real-world video skeleton statistics. After being applied to commonly used action categorization datasets, experimental results demonstrate that the proposed FGP-3D, with pre-training, generalizes well and outperforms the state-of-the-art.

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“…It provides a total of 567 depth map sequences and corresponding skeleton coordinates. It is often regarded as a challenging common benchmark dataset in the field of action recognition based on skeleton depth data [11,12].…”

Section: Human Action Datasetsmentioning

confidence: 99%

“…It is composed of 2169 highquality 3D basketball sports videos labeled by action categories. Unlike with the common 12 Side throw Run with ball One-hand shoot Chest pass and catch or shoot Side throw…”

Section: Npu Rgb+d Dataset For Basketball Playersmentioning

confidence: 99%

NPU RGBD Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players+

Jiang

Yao

et al. 2021

Applied Sciences

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Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

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Compressive sensing based recognition of human upper limb motions with kinect skeletal data

Cited by 16 publications

References 49 publications

Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN

Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN

Employing FGP-3D, a Fully Gated and Anchored Methodology, to Identify Skeleton-Based Action Recognition

NPU RGBD Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players+

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