Convolutional neural network in upper limb functional motion analysis after stroke

PeerJ

article

Agnieszka Szczęsna¹  Monika Błaszczyszyn²  Aleksandra Kawala-Sterniuk³ 

[1] Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology;Faculty of Physical Education and Physiotherapy, Opole University of Technology;Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology

关键词: Convolutional neural network;    Hyperparameters;    Functional motion analysis;    Stroke;    Lifting movements;    Optical motion capture;   

DOI  :  10.7717/peerj.10124

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

In this work, implementation of Convolutional Neural Network (CNN) for the purpose of analysis of functional upper limb movement pattern was applied. The main aim of the study was to compare motion of selected activities of daily living of participants after stroke with the healthy ones (in similar age). The optical, marker-based motion capture system was applied for the purpose of data acquisition. There were some attempts made in order to find the existing differences in the motion pattern of the upper limb. For this purpose, the motion features of dominant and non-dominant upper limb of healthy participants were compared with motion features of paresis and non-paresis upper limbs of participants after stroke. On the basis of the newly collected data set, a new CNN application was presented to the classification of motion data in two different class label configurations. Analyzing individual segments of the upper body, it turned out that the arm was the most sensitive segment for capturing changes in the trajectory of the lifting movements of objects.

【 授权许可】

CC BY