Kinect-based objective evaluation of bradykinesia in patients with Parkinson's disease

Wu, Zhuang; Gu, Hongkai; Hong, Ronghua; Xing, Ziwen; Zhang, Zhuoyu; Peng, Kangwen; He, Yijing; Xie, Ludi; Zhang, Jingxing; Gao, Yichen; Jin, Yaming; Su, Xiaoyun; Zhi, Hongping; Guan, Qiang; Pan, Lizhen; Jin, Lingjing

doi:10.1177/20552076231176653

Cited by 3 publications

(1 citation statement)

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“…Motion analysis techniques offer a more objective and detailed view of the patient' s motor abilities, which can detect subtle changes and nuances in movement that might not be noticeable to clinical observations. Previous studies have combined motion analysis techniques, such as optical motion capture systems and wearable sensors, with machine learning methods to assess the bradykinesia in PD [28][29][30] . These studies derived the classic motion features and primarily achieved the distinction between healthy controls and PD patients.…”

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%