Deep Learning-Based Image Automatic Assessment and Nursing of Upper Limb Motor Function in Stroke Patients

Chen, Xue; Shi, Yuanyuan; Wang, Yanjun; Cheng, Yuanjuan

doi:10.1155/2021/9059411

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…A traditional RNN [102], and a Gated Recurrent Unity Network (GRU) [45] were used in one publication each. CNNs and RNNs were used together in eight publications, seven of which used a CNN with an LSTM [40], [75], [78], [96], [116], [117], [120], and another used a CNN with an RNN [67]. Finally, four publications used generative DL algorithms.…”

Section: ) Model Trainingmentioning

confidence: 99%

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: ) Model Trainingmentioning

confidence: 99%

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

et al. 2022

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“…Upper limb movement is closely related to daily life. One of the important research directions of upper limb rehabilitation is whether patients with upper limb dysfunction can gradually regain normal upper limb motor function through rehabilitation training [7][8][9][10][11][12]. To a certain extent, the internal activity and state of muscles can be reflected by the surface electromyographic (sEMG) signal, a bioelectrical signal transmitted between surface muscles and the surrounding neurons [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Upper Limb Action Identification Based on Physiological Signals and Its Application in Limb Rehabilitation Training

Zhang¹,

Ji²,

Ma³

et al. 2021

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pper limb motor dysfunction brings huge pain and burden to patients with brain trauma, stroke, and cerebral palsy, as well as their relatives. Physiological signals are closely related to the recovery of patients with limb dysfunction. The joint analysis of two key physiological signals, namely, surface electromyographic (sEMG) signal and acceleration signal, enables the scientific and effective evaluation of upper limb rehabilitation. However, the existing indices of upper limb rehabilitation are incomplete, and the current evaluation approaches are not sufficiently objective or quantifiable. To solve the problems, this paper explores upper limb action identification based on physiological signals, and tries to apply the approach to limb rehabilitation training. Specifically, the upper limb action features during limb rehabilitation training were extracted and identified by time-domain feature method, frequency-domain feature method, time-frequency domain feature method, and entropy feature method. Then, the evaluation flow of upper limb rehabilitation, plus the relevant evaluation indices, were given. Experimental results demonstrate the effectiveness of the proposed composite feature identification of upper limb actions, and the proposed evaluation method for limb rehabilitation.

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Deep Learning-Based Image Automatic Assessment and Nursing of Upper Limb Motor Function in Stroke Patients

Cited by 2 publications

References 8 publications

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

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

Upper Limb Action Identification Based on Physiological Signals and Its Application in Limb Rehabilitation Training

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