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The Journal of Supercomputing

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05-03-2021

Novel side pose classification model of stretching gestures using three-layer LSTM

Authors: Boldmaa Solongontuya, Kyung Joo Cheoi, Mi-Hye Kim

Published in: The Journal of Supercomputing | Issue 9/2021

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Abstract

In recent years, low back pain rehabilitation exercises have been widely performed for spine-related illnesses. To facilitate rehabilitation exercises, pose-based human action recognition technique is used to determine human movement from simple videos. Herein, we propose a new stretching side pose classification system using three-layer long short-term memory (LSTM) that can be used in rehabilitation therapy systems. Four types of rehabilitation treatment exercises are selected: bird dog, cat camel, cobra stretch, and pelvic tilt. Features selected based on the high frequency of use for each exercise resulted in improved classification. Consequently, the recognition rate of the selected feature is 97.50%, as classified by the three-layer LSTM model.

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Spencer J, Degu A, Kalkidan A, Solomon A et al (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392:1789–1858. https://doi.org/10.1016/S0140-6736(18)32279-7CrossRef

Sharma S, Traeger AC, Mishra SR, Sharma S, Maher CG (2019) Delivering the right care to people with low back pain in low- and middle-income countries: the case of Nepal. J Global Health 9:010304. https://doi.org/10.7189/jogh.09.010304CrossRef

Beinart N, Goodchild C, Weinman J, Ayis SA, Godfrey E (2013) Individual and intervention-related factors associated with adherence to home exercise in chronic low back pain: a systematic review. Spine J: Offl J North Am Spine Soc. https://doi.org/10.1016/j.spinee.2013.08.027CrossRef

Czejdo B, Baszun M (2010) Remote patient monitoring system and a medical social network. Int J Soc Human Comput 1:273–281. https://doi.org/10.1504/IJSHC.2010.032688CrossRef

Pallasena R, Sharma M, Krishnaswamy V (2019) Context-sensitive smart devices—definition and a functional taxonomy. Int J Soc Human Comput 3:108–134. https://doi.org/10.1504/IJSHC.2019.101593CrossRef

Gordon R, Bloxham S (2016) A systematic review of the effects of exercise and physical activity on non-specific chronic low back pain. Healthcare 4:22. https://doi.org/10.3390/healthcare4020022CrossRef

Yakub F, Md K, Ahmad Z, Mori Y (2014) Recent trends for practical rehabilitation robotics, current challenges and the future. Int J Rehabil Res 37:9–21. https://doi.org/10.1097/MRR.0000000000000035CrossRef

Díaz I, Gil JJ, Sánchez E (2011) Lower-limb robotic rehabilitation: literature review and challenges. J Robot. https://doi.org/10.1155/2011/759764CrossRef

Su CJ, Chiang CY, Huang JY (2014) Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Appl Soft Comput 22:652–666. https://doi.org/10.1016/j.asoc.2014.04.020CrossRef

10.

Cassola F, Morgado L, Carvalho F, Paredes H, Fonseca B, Martins P (2014) Online-gym: a 3D virtual gymnasium using kinect interaction. Procedia Technology 13:130–138. https://doi.org/10.1016/j.protcy.2014.02.017CrossRef

11.

Ryan N, Christopher K, Eleanor Q, Ami M, Bambang P, Brad ED (2019) Systematic review of mobile health applications in rehabilitation. Arch Phys Med Rehabil 100:115–127. https://doi.org/10.1016/j.apmr.2018.07.439CrossRef

12.

Kim Y, Jang S, Yoo Y (2014) Mobile assessment system for shoulder joint rehabilitation: system development and preliminary study. Int J Bio-Sci Bio-Technol 6:51–60. https://doi.org/10.14257/ijbsbt.2014.6.2.05CrossRef

13.

Wang K, Wang XL, Lin L, Wang M, Zuo W (2015) 3D Human activity recognition with reconfigurable convolutional neural networks. Proceedings of the 2014 ACM Conference on Multimedia. https://doi.org/https://doi.org/10.1145/2647868.2654912

14.

Ongvisatepaiboon K, Vanijja V, Chignell M, Mekhora K, Chan J (2016) Smartphone-based audio-biofeedback system for shoulder joint tele-rehabilitation. J Med Imag Health Informat 6:1127–1134. https://doi.org/10.1166/jmihi.2016.1810CrossRef

15.

Islam S, Bulbul F, Islam M (2018) A comparative study on human action recognition using multiple skeletal features and multiclass support vector machine. Mach Learn Appl: Int J 5:1–15. https://doi.org/10.5121/mlaij.2018.5201CrossRef

16.

Kong Y, Fu Y (2018) Human action recognition and prediction: a survey. journal of latex class files 13. ArXiv:1806.11230

17.

Zhang H, Zhang YX, Zhong B, Lei Q, Yang L, Du JX, Chen DS (2019) A comprehensive survey of vision-based human action recognition methods. Sensors 19:1005. https://doi.org/10.3390/s19051005CrossRef

18.

Jegham I, Khalif AB, Alouanic I, Mahjou MA (2020) Vision-based human action recognition: an overview and real world challenges. Foren Sci Int: Digit Investig. https://doi.org/10.1016/j.fsidi.2019.200901CrossRef

19.

Pan JI, Chung HW, Huang JJ (2013) Intelligent Shoulder Joint Home-Based Self-Rehabilitation Monitoring System. Int J Smart Home 7(5):395–404. https://doi.org/10.14257/ijsh.2013.7.5.38CrossRef

20.

Huang MC, Liu J, Xu W, Alshurafa N, Zhang X, Sarrafzadeh M (2014) Using pressure map sequences for recognition of on bed rehabilitation exercises. IEEE J Biomed Health Inform 18:411–418. https://doi.org/10.1109/JBHI.2013.2296891CrossRef

21.

Ilktan A, Akgul Y (2014) A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera. IEEE Trans Neural Syst Rehabil Eng 22:1160–1171. https://doi.org/10.1109/TNSRE.2014.2326254CrossRef

22.

Antón D, Goni A, Illarramendi A (2015) Exercise recognition for kinect-based telerehabilitation. Methods Inf Med 54:145–155. https://doi.org/10.3414/ME13-01-0109CrossRef

23.

Ghazal S, Khan U (2018) Human posture classification using skeleton information. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies, pp 1–4. https://doi.org/10.1109/ICOMET.2018.8346407

24.

Toshev A, Szegedy C (2014) DeepPose: Human pose estimation via deep neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 1653–1660.https://doi.org/10.1109/CVPR.2014.214

25.

Cao Z, Martinez G, Simon T, Wei SE, Sheikh Y (2019) OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2929257CrossRef

26.

Wang R, Cao Z, Wang X, Liu Z, Zhu X (2019) Human pose estimation with deeply learned multi-scale compositional models. IEEE Access 7:71158–71166. https://doi.org/10.1109/ACCESS.2019.2919154CrossRef

27.

Tsai JK, Hsu CC, Wang WY, Huang SK (2020) Deep learning-based real-time multiple-person action recognition system. Sensors 20:4758. https://doi.org/10.3390/s20174758CrossRef

28.

Wan S, Qi L, Xu X, Tong C, Gu Z (2020) Deep learning models for real-time human activity recognition with smartphones. Mob Netw Appl 25:743–755. https://doi.org/10.1007/s11036-019-01445-xCrossRef

29.

Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Future Gener Comput Syst 81:307–313. https://doi.org/10.1016/j.jksuci.2019.09.004CrossRef

30.

Jaouedia N, Boujnah N, Bouhlelc MS (2020) A new hybrid deep learning model for human action recognition. J King Saud Univ–Comput Inf Sci 32:447–453. https://doi.org/10.1016/j.jksuci.2019.09.004CrossRef

31.

Chena Y, Tianb Y, He M (2020) Monocular human pose estimation: a survey of deep learning-based methods. Comput Vis Image Underst 192:102897. https://doi.org/10.1016/j.cviu.2019.102897CrossRef

32.

Arunnehru J., Nandhana AK, Sharan RR, Nambiar PG (2020) Human pose estimation and activity classification using machine learning approach. International Conference on Soft Computing and Signal Processing (ICSCSP), pp 113–123.https://doi.org/10.1007/978-981-15-2475-2_11

33.

Lipton ZC (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.

34.

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735CrossRef

35.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539CrossRef

36.

Du Y, Wang W, Wang L (2015) Hierarchical recurrent neural network for skeleton based action recognition. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1110–1118. https://doi.org/10.1109/CVPR.2015.7298714

37.

Zhu W, Lan C, Xing J, Zeng W, Li Y, Shen L, Xie X (2016) Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: Proceedings of the 13th AAAI Conference on Artificial Intelligence, pp 3697–3703

38.

Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal LSTM with trust gates for 3D human action recognition. 14th European Conference on Computer Vision. Springer, Cham, pp 816–833

39.

Stearns CC, Kannappan K (1995) Method for 2-D affine transformation of images. US Patent No.5475803A

Title: Novel side pose classification model of stretching gestures using three-layer LSTM
Authors: Boldmaa Solongontuya
Kyung Joo Cheoi
Mi-Hye Kim
Publication date: 05-03-2021
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 9/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-03684-w

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