Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion

Li, Chenguang; Yang, Hongjun; Cheng, Long; Huang, Fubiao; Li, Dongyue; Yan, Ruxiu

doi:10.1109/tnsre.2022.3192479

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…Larger sample sizes with segmented PD types should be considered in future work to further validate our method' s effectiveness and the test-retest reliability. On the other hand, the previous study has explored the potential of integrating multimodal signals, which improved the motion function assessment [35] . The proposed method can be extended to the multimodal signals, which promises to achieve a more accurate assessment of bradykinesia in PD.…”

Section: Discussionmentioning

confidence: 99%

A small-sample time-series signal augmentation and analysis method for quantitative assessment of bradykinesia in Parkinson's disease

Shu,

Liu,

Cheng

et al. 2024

Intelligence & Robotics

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Patients with Parkinson's disease (PD) usually have varying degrees of bradykinesia, and the current clinical assessment is mainly based on the Movement Disorder Society Unified PD Rating Scale, which can hardly meet the needs of objectivity and accuracy. Therefore, this paper proposed a small-sample time series classification method (DTW-TapNet) based on dynamic time warping (DTW) data augmentation and attentional prototype network. Firstly, for the problem of small sample sizes of clinical data, a DTW-based data merge method is used to achieve data augmentation. Then, the time series are dimensionally reorganized using random grouping, and convolutional operations are performed to learn features from multivariate time series. Further, attention mechanism and prototype learning are introduced to optimize the distance of the class prototype to which each time series belongs to train a low-dimensional feature representation of the time series, thus reducing the dependency on data volume. Clinical experiments were conducted to collect motion capture data of upper and lower limb movements from 36 patients with PD and eight healthy controls. For the upper limb movement data, the proposed method improved the classification accuracy, weighted precision, and kappa coefficient by 8.89%-15.56%, 9.22%-16.37%, and 0.13-0.23, respectively, compared with support vector machines, long short-term memory, and convolutional prototype network. For the lower limb movement data, the proposed method improved the classification accuracy, weighted precision, and kappa coefficient by 8.16%-20.41%, 10.01%-23.73%, and 0.12-0.28, respectively. The experiments and results show that the proposed method can objectively and accurately assess upper and lower limb bradykinesia in PD.

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Section: Discussionmentioning

confidence: 99%

A small-sample time-series signal augmentation and analysis method for quantitative assessment of bradykinesia in Parkinson's disease

Shu,

Liu,

Cheng

et al. 2024

Intelligence & Robotics

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“…Each movement was assigned a qualitative rating with a score of 0, 1, or 2, depending on how well it was performed. [ 16 ] In this study, we evaluated the hand functions of the participants using the 7-item FMA-Hand subscale.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of finger strength and spasticity in hemiplegic patients using hand-finger robotic device: A validity and reliability study

Adar,

Demircan,

Akçin

et al. 2023

Medicine

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We aimed to investigate the validity, reliability, and clinical relevance of Amadeo hand-finger robotic rehabilitation system measurements for evaluating spasticity and strength in hemiplegic patients. In total, 161 participants (107 hemiplegic patients and 54 sex- and age-matched healthy controls) were included in this study. Spasticity was evaluated using the Modified Ashworth Scale, hand motor functions were evaluated using the Fugl-Meyer Assessment Hand Subscale, and hand grip and pinch strength were evaluated using the Jamar hand grip and pinch dynamometer. The Amadeo (Tyromotion) hand-finger robotic rehabilitation system was used to evaluate finger spasticity and strength of the participants. A statistically significant difference was found between the median values of the Modified Ashworth Scale (both clinical and robotic evaluation results) and the mean values of hand flexor and extensor strength measured with the robotic device in patients compared to healthy subjects (P < .01). Statistically, excellent agreement was obtained between the clinical and robotic test-retest results of the scale (P < .01) (intra-class correlation coefficient, ICC = .98–.99; ICC = .98–.99, respectively). There was a statistically significant positive correlation between the clinical and robotic device results of the Modified Ashworth Scale (r = .72; P < .01). There was a statistically significant positive correlation between the hand strength values measured with the robotic device, Jamar grip, pinch, and Fugl-Meyer Assessment Hand Subscale scores (P < .01) in the patient group. Hand finger spasticity and strength measurements of the Amadeo hand-finger robotic rehabilitation system were valid, reliable, and clinically correlated in stroke patients.

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“…They heavily rely on clinical expertise and possess subjectivity. Li et al (2022) integrated surface electromyography (sEMG) signals and motion information for the quantitative assessment of hand function. However, these methods face challenges in assessing motion compensation.…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

Liu,

Li,

Zhang

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Stroke is the second leading cause of death globally and a primary factor contributing to disability. Unilateral limb motor impairment caused by stroke is the most common scenario. The bilateral movement pattern plays a crucial role in assisting stroke survivors on the affected side to relearn lost skills. However, motion compensation often lead to decreased coordination between the limbs on both sides. Furthermore, muscle fatigue resulting from imbalanced force exertion on both sides of the limbs can also impact the rehabilitation outcomes.Method: In this study, an assessment method based on muscle synergy indicators was proposed to objectively quantify the impact of motion compensation issues on rehabilitation outcomes. Muscle synergy describes the body’s neuromuscular control mechanism, representing the coordinated activation of multiple muscles during movement. 8 post-stroke hemiplegia patients and 8 healthy subjects participated in this study. During hand-cycling tasks with different resistance levels, surface electromyography signals were synchronously collected from these participants before and after fatigue. Additionally, a simulated compensation experiment was set up for healthy participants to mimic various hemiparetic states observed in patients.Results and discussion: Synergy symmetry and synergy fusion were chosen as potential indicators for assessing motion compensation. The experimental results indicate significant differences in synergy symmetry and fusion levels between the healthy control group and the patient group (p ≤ 0.05), as well as between the healthy control group and the compensation group. Moreover, the analysis across different resistance levels showed no significant variations in the assessed indicators (p > 0.05), suggesting the utility of synergy symmetry and fusion indicators for the quantitative evaluation of compensation behaviors. Although muscle fatigue did not significantly alter the symmetry and fusion levels of bilateral synergies (p > 0.05), it did reduce the synergy repeatability across adjacent movement cycles, compromising movement stability and hindering patient recovery. Based on synergy symmetry and fusion indicators, the degree of bilateral motion compensation in patients can be quantitatively assessed, providing personalized recommendations for rehabilitation training and enhancing its effectiveness.

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Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion

Cited by 14 publications

References 28 publications

A small-sample time-series signal augmentation and analysis method for quantitative assessment of bradykinesia in Parkinson's disease

A small-sample time-series signal augmentation and analysis method for quantitative assessment of bradykinesia in Parkinson's disease

Evaluation of finger strength and spasticity in hemiplegic patients using hand-finger robotic device: A validity and reliability study

Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

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