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.

Figures/Tables 10

References 20

1	ZHU D, ZHANG G, GUO X, et al. A new hope in spinal degenerative diseases: Piezo1 [J]. BioMed research international, 2021: 6645193. DOI:10.1155/2021/6645193 DOI
2	HU J. The therapeutic and health effects of moxibustion on Degeneration of Joint Disease (in Chinese) [J]. Journal of Hunan University of Chinese Medicine, 2011, 31(2): 32–33
3	YANG D G, LI P Z, SHAO C L, et al. Li Yefu’s treatment of Degeneration of Joint Disease by applying the idea of “Jin Gu Bing Ju, Chan Xuan Xiang Ji” (in Chinese) [J]. Journal of Anhui university of Chinese medicine, 2018, 37(02): 43–45
4	LIU Y X, DAI L B. Therapeutic Effect of Chinese Medicine Packet Therapeutic Instrument Plus Low‑frequency Pulsed Electromagnetic Field Therapeutic Instrument for Lumbar Disc Herniation (in Chinese) [J]. Journal of Guangzhou university of traditional Chinese medicine, 2018, 35(04): 655–658
5	CUI Y Z. Clinical care study on the treatment of lumbar intervertebral disc herniation by combining specific electromagnetic wave irradiation with external application of Chinese medicine (in Chinese) [J]. Shanxi medical journal, 2016, 45(19): 2336–2338
6	YAN H Z, MAO G H. Effect of three‑dimensional curvature traction instrument combined with intermediate frequency therapy instrument in patients with cervical spondylosis (in Chinese) [J]. Shandong medical journal, 2017, 57(13): 90–92
7	QIN Y, WANG Y X. Diode Laser irradiation on ganglion stellatum and traction in treatment of vertebral artery type cervical spondylosis [J]. Chinese journal of laser medicine & surgery, 2010, 19(02): 102–105
8	CHEN M Y, YU Zhongshun. On the Application of Traditional Chinese Daoyin in the Elderly Health (in Chinese) [J]. Wushu studies, 2021,6 (01): 119–122
9	ZHAO L Z, CHEN Y G. Research progress on TCM five‑note therapy [J]. China journal of traditional Chinese medicine and pharmacy, 2016, 31(11): 4666–4668
10	GONG Z Z, DU Y Y, XU M H, et al. Theoretical Analysis of TCM Wuyin Therapy for insomnia (in Chinese) [J]. Chinese journal of medicinal guide, 2021, 23(02): 96–99
11	CAO Z, SIMON T, WEI S H, et al. Realtime multi‑person 2D pose estimation using part affinity fields [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2017: 1302–1310. DOI: 10.1109/CVPR.2017.143 DOI
12	ZHANG P F, LAN C L, XING J L, et al. View adaptive neural networks for high performance skeleton‑based human action recognition [J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 41(8): 1963–1978. DOI: 10.1109/TPAMI.2019.2896631 DOI
13	MORYOSSEF A, TSOCHANTARIDIS I, AHARONI R, et al. Real‑time sign language detection using human pose estimation [EB/OL]. [2021‑03‑25].
14	LUCAS B D, KANADE T. An iterative image registration technique with an application to stereo vision [C]//The 7th International Joint Conference on Artificial Intelligence. Vancouver, Canada: IJCAI. 1981
15	HOCHREITER S, SCHMIDHUBER J. Long short‑term memory [J]. Neural computation, 1997, 9(8): 1735–1780. DOI: 10.1162/neco.1997.9.8.1735 DOI
16	KEOGH E, RATANAMAHATANA C A. Exact indexing of dynamic time warping [J]. Knowledge and information systems, 2005, 7(3): 358–386. DOI: 10.1007/s10115-004-0154-9 DOI
17	CHOI H S, LEE B, YOON S. Biometric authentication using noisy electrocardiograms acquired by mobile sensors [J]. IEEE access, 2016, 4: 1266–1273. DOI: 10.1109/ACCESS.2016.2548519 DOI
18	YANG K, CONG L, HU W D, et al. Embedded wireless ECG monitoring system based on BMD101 [J]. Application of electronic technique, 2014, 40(1): 122–124. DOI: 10.16157/j.issn.0258-7998.2014.01.036 DOI
19	SARKAR S, BHATTACHERJEE S, PAL S. Extraction of respiration signal from ECG for respiratory rate estimation [C]//Michael Faraday IET International Summit. Kolkata, India: IET, 2016
20	GUO Y, SI Y J. Respiratory signal extraction algorithm based on ECG [J]. Journal of Jilin University (Information Science Edition), 2016, 34(03): 327–333

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

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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Figures/Tables 10

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Metrics

	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