Deep Learning for Sensor-based Rehabilitation Exercise Recognition and Evaluation

doi:10.3390/s19040887

機構典藏 > College of Engineering > Department of Artificial Intelligence > Journal Article > Item 987654321/126791

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/126791

Title:	Deep Learning for Sensor-based Rehabilitation Exercise Recognition and Evaluation
Authors:	Zhu, Zheng-an
Keywords:	rehabilitation exercises;recognition;evaluation;deep learning;sensor data
Date:	2019-02-20
Issue Date:	2025-03-20 09:25:08 (UTC+8)
Publisher:	MDPI
Abstract:	In this paper, a multipath convolutional neural network (MP-CNN) is proposed for rehabilitation exercise recognition using sensor data. It consists of two novel components: a dynamic convolutional neural network (D-CNN) and a state transition probability CNN (S-CNN). In the D-CNN, Gaussian mixture models (GMMs) are exploited to capture the distribution of sensor data for the body movements of the physical rehabilitation exercises. Then, the input signals and the GMMs are screened into different segments. These form multiple paths in the CNN. The S-CNN uses a modified Lempel–Ziv–Welch (LZW) algorithm to extract the transition probabilities of hidden states as discriminate features of different movements. Then, the D-CNN and the S-CNN are combined to build the MP-CNN. To evaluate the rehabilitation exercise, a special evaluation matrix is proposed along with the deep learning classifier to learn the general feature representation for each class of rehabilitation exercise at different levels. Then, for any rehabilitation exercise, it can be classified by the deep learning model and compared to the learned best features. The distance to the best feature is used as the score for the evaluation. We demonstrate our method with our collected dataset and several activity recognition datasets. The classification results are superior when compared to those obtained using other deep learning models, and the evaluation scores are effective for practical applications.
Relation:	Sensors 19(4),　887
DOI:	10.3390/s19040887
Appears in Collections:	[Department of Artificial Intelligence] Journal Article

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