A two-dimensional video based quantification method and clinical application research of motion disorders.
10.7507/1001-5515.202203052
- Author:
Yubo SUN
1
;
Peipei LIU
2
;
Yuchen YANG
1
;
Yang YU
3
;
Huan YU
2
;
Xiaoyi SUN
2
;
Jialing WU
2
;
Jianda HAN
1
;
Ningbo YU
1
Author Information
1. College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China.
2. Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, P. R. China.
3. Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, P. R. China.
- Publication Type:Journal Article
- Keywords:
Gait features;
Machine learning;
Motion disorders;
Telemedicine;
Two-dimensional video
- MeSH:
Humans;
Aged;
Parkinson Disease/diagnosis*;
Aging;
Algorithms;
Gait;
Lower Extremity
- From:
Journal of Biomedical Engineering
2023;40(3):499-507
- CountryChina
- Language:Chinese
-
Abstract:
The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.
