Artificial Intelligence Rehabilitation Evaluation and Training System for Degeneration of Joint Disease

doi:10.12142/ZTECOM.202103006

ZTE Communications ›› 2021, Vol. 19 ›› Issue (3): 46-55.doi: 10.12142/ZTECOM.202103006

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Artificial Intelligence Rehabilitation Evaluation and Training System for Degeneration of Joint Disease

LIU Weichen¹, SHEN Mengqi², ZHANG Anda¹, CHENG Yiting², ZHANG Wenqiang^1,²()

^1.Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
^2.School of Computer Science, Fudan University, Shanghai 200433, China

Received:2021-06-15 Online:2021-09-25 Published:2021-10-11
About author:LIU Weichen is pursuing his master’s degree in the Academy for Engineering & Technology (AET), Fudan University, China. His research interests include machine learning and signal processing.|SHEN Mengqi is a graduate student of the School of Computer Science and Technology, Fudan University, China. His main research interests include action recognition, video understanding and human pose estimation.|ZHANG Anda is pursuing his master’s degree of electronic information in AET, Fudan University, China. His research interests include artificial intelligence and deep learning related to Chinese Medicine.|CHENG Yiting received the M.S. degree in computer science from Fudan University, China in 2021. Her main research interests include unsupervised learning and computer vision.|ZHANG Wenqiang (wqzhang@fudan.edu.cn) is a professor with the School of Computer Science, Fudan University, China. He received his Ph.D. degree in mechanical engineering from Shanghai Jiao Tong University, China in 2004. His current research interests include computer vision and robot intelligence.
Supported by:
National Key R&D Program of China(2019YFC1711800);National Natural Science Foundation of China(62072112);Fudan University-CIOMP Joint Fund(FC2019-005)

Abstract

Abstract:

Degeneration of joint disease is one of the problems that threaten global public health. Currently, the therapies of the disease are mainly conservative but not very effective. To solve the problem, we need to find effective, convenient and inexpensive therapies. With the rapid development of artificial intelligence, we innovatively propose to combine Traditional Chinese Medicine (TCM) with artificial intelligence to design a rehabilitation assessment system based on TCM Daoyin. Our system consists of four subsystems: the spine movement assessment system, the posture recognition and correction system, the background music recommendation system, and the physiological signal monitoring system. We incorporate several technologies such as keypoint detection, posture estimation, heart rate detection, and deriving respiration from electrocardiogram (ECG) signals. Finally, we integrate the four subsystems into a portable wireless device so that the rehabilitation equipment is well suited for home and community environment. The system can effectively alleviate the problem of an inadequate number of physicians and nurses. At the same time, it can promote our TCM culture as well.

Key words: rehabilitation, Traditional Chinese Medicine, artificial intelligence, degeneration of joint disease

LIU Weichen, SHEN Mengqi, ZHANG Anda, CHENG Yiting, ZHANG Wenqiang. Artificial Intelligence Rehabilitation Evaluation and Training System for Degeneration of Joint Disease[J]. ZTE Communications, 2021, 19(3): 46-55.

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References 20

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Different Joints	True Value/°	Measured Value/°	Error/°
Cervical anterior extension	20.00	18.48	1.52
Cervical posterior extension	22.50	24.55	2.05
Cervical left bending	14.00	13.85	0.15
Cervical right bending	20.00	20.69	0.69
Cervical left rotation	30.00	28.82	1.18
Cervical Right Rotation	42.00	41.92	0.08
Lumbar Anterior Extension	32.00	31.06	1.64
Lumbar Posterior Extension	25.00	25.53	0.53
Lumbar Left Bending	27.00	22.52	4.48
Lumbar Right Bending	19.00	20.59	1.59
Lumbar Left Rotation	42.00	41.09	0.91
Lumbar Right Rotation	38.00	36.38	1.62

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Artificial Intelligence Rehabilitation Evaluation and Training System for Degeneration of Joint Disease

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	Estimated Heart Rate/(beat/m)	Real Heart Rate/(beat/m)	Estimated Respiratory Rate/(breath/m)	Patient Status
1	89	86	16	Stabilization
2	85	87	16	Stabilization
3	87	85	17	Stabilization
4	84	83	15	Stabilization
5	82	83	16	Stabilization
6	83	85	16	Stabilization
7	87	85	17	Stabilization
8	90	85	18	Stabilization
9	87	86	16	Stabilization
10	82	86	15	Stabilization
11	83	86	16	Stabilization
12	88	85	17	Stabilization
13	94	85	18	Stabilization
14	85	88	16	Stabilization
15	85	89	16	Stabilization
16	90	90	18	Stabilization
17	93	90	18	Stabilization
18	95	90	18	Stabilization
19	87	88	16	Stabilization
20	86	86	17	Stabilization