Human Posture Recognition by Distribution-Aware Coordinate Representation and Machine Learning
Keywords:
Human Posture, Supervised Machine Learning, Classification, feature extractionAbstract
There has been a lot of research put into the statistical study of human behavior and movement. The ability to infer behavior from a single picture or a series of photos is a hot research area right now. Human Posture Recognition is a significant breakthrough in the direction of behavior comprehension since it may be used to identify actions taking place in a picture. Human posture estimate from the video is crucial for a wide range of uses, including the measurement of workouts, the identification of signs, and the manipulation of whole bodies via gestures. It may serve as the foundation for many dance, fitness, and yoga practices. It may also make augmented reality possible, where digital data is superimposed on the actual environment.The purpose of this study is to investigate and evaluate the viability of using Machine Learning to categorize human body position alongside a wide variety of complicated physical activities. Different basic, boosting, and ensemble machine learning methods are used in this study to categorize human posture based on the positions of individual body components (Distribution-aware coordinate representation). This study's dataset has 10 distinct physical positions that may be used to categorize 5 distinct workouts. These routines include variations on the push-up, pull-up, sit-up, jumping Jack, and squat. The final states of each exercise (the "up" and "down" postures for push-ups, for example) have been represented by two distinct classes. The strong predictions offered by the ensemble techniques were the result of the aggregation of the efforts of many different learners, making them more flexible.
Downloads
References
N. K. Trivedi, V. Gautam, H. Sharma, A. Anand, and S. Agarwal, “Diabetes Prediction using Different Machine Learning Techniques,” 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 2173–2177, 2022, doi: 10.1109/ICACITE53722.2022.9823640.
N. Ujjwal, A. Singh, A. K. Jain, and R. G. Tiwari, “Exploiting Machine Learning for Lumpy Skin Disease Occurrence Detection,” 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 1–6, Oct. 2022, doi: 10.1109/ICRITO56286.2022.9964656.
I. Al-muwaffaq and Z. Bozkus, “MLTDD: use of machine learning techniques for diagnosis of thyroid gland disorder,” Comput Sci Inf Technol, pp. 67–73, 2016, doi: 10.5121/csit.2016.60507.
A. K. Agarwal, V. Kiran, R. K. Jindal, D. Chaudhary, and R. G. Tiwari, “Optimized Transfer Learning for Dog Breed Classification,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 1s, pp. 18–22, Oct. 2022, Accessed: Jan. 08, 2023. [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/2233
R. G. Tiwari, D. S. Yadav, and A. Misra, “Performance Evaluation of Optimizers in the Classification of Marble Surface Quality Using CNN,” pp. 181–191, 2023, doi: 10.1007/978-981-19-3148-2_15/COVER.
R. G. Tiwari, A. K. Agarwal, R. K. Kaushal, and N. Kumar, “Prophetic Analysis of Bitcoin price using Machine Learning Approaches,” in Proceedings of IEEE International Conference on Signal Processing,Computing and Control, 2021, vol. 2021-Octob. doi: 10.1109/ISPCC53510.2021.9609419.
M. M. Panda, S. N. Panda, and P. K. Pattnaik, “Exchange Rate Prediction using ANN and Deep Learning Methodologies: A Systematic Review,” Indo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020 - Proceedings, pp. 86–90, Feb. 2020, doi: 10.1109/INDO-TAIWANICAN48429.2020.9181351.
W. Ren, O. Ma, H. Ji, and X. Liu, “Human Posture Recognition Using a Hybrid of Fuzzy Logic and Machine Learning Approaches,” IEEE Access, vol. 8, pp. 135628–135639, 2020, doi: 10.1109/ACCESS.2020.3011697.
X. Zhou, W. Liang, K. I. K. Wang, H. Wang, L. T. Yang, and Q. Jin, “Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things,” IEEE Internet Things J, vol. 7, no. 7, pp. 6429–6438, Jul. 2020, doi: 10.1109/JIOT.2020.2985082.
A. Moin et al., “A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition,” Nature Electronics 2020 4:1, vol. 4, no. 1, pp. 54–63, Dec. 2020, doi: 10.1038/s41928-020-00510-8.
K. Saho, S. Hayashi, M. Tsuyama, L. Meng, and M. Masugi, “Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements,” Sensors 2022, Vol. 22, Page 1721, vol. 22, no. 5, p. 1721, Feb. 2022, doi: 10.3390/S22051721.
“Physical Exercise Recognition Dataset | Kaggle.” https://www.kaggle.com/datasets/muhannadtuameh/exercise-recognition (accessed Jan. 08, 2023).
V. Rattan, R. Mittal, J. Singh, and V. Malik, “Analyzing the application of SMOTE on machine learning classifiers,” 2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021, pp. 692–695, Mar. 2021, doi: 10.1109/ESCI50559.2021.9396962.
A. Fernández, S. García, F. Herrera, and N. v. Chawla, “SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary,” Journal of Artificial Intelligence Research, vol. 61, pp. 863–905, Apr. 2018, doi: 10.1613/JAIR.1.11192.
F. Kherif and A. Latypova, “Principal component analysis,” Machine Learning: Methods and Applications to Brain Disorders, pp. 209–225, Jan. 2020, doi: 10.1016/B978-0-12-815739-8.00012-2.
R. Vidal, Y. Ma, and S. S. Sastry, “Principal Component Analysis,” pp. 25–62, 2016, doi: 10.1007/978-0-387-87811-9_2.
W. Xing and Y. Bei, “Medical Health Big Data Classification Based on KNN Classification Algorithm,” IEEE Access, vol. 8, pp. 28808–28819, 2020, doi: 10.1109/ACCESS.2019.2955754.
M. Goswami and N. J. Sebastian, “Performance Analysis of Logistic Regression, KNN, SVM, Naïve Bayes Classifier for Healthcare Application During COVID-19,” Lecture Notes on Data Engineering and Communications Technologies, vol. 96, pp. 645–658, 2022, doi: 10.1007/978-981-16-7167-8_47/COVER.
S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowl Based Syst, vol. 192, p. 105361, Mar. 2020, doi: 10.1016/J.KNOSYS.2019.105361.
F. J. Yang, “An implementation of naive bayes classifier,” Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, pp. 301–306, Dec. 2018, doi: 10.1109/CSCI46756.2018.00065.
J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Syst Appl, vol. 134, pp. 93–101, Nov. 2019, doi: 10.1016/J.ESWA.2019.05.028.
Priyanka and D. Kumar, “Decision tree classifier: A detailed survey,” International Journal of Information and Decision Sciences, vol. 12, no. 3, pp. 246–269, 2020, doi: 10.1504/IJIDS.2020.108141.
C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artificial Intelligence Review 2020 54:3, vol. 54, no. 3, pp. 1937–1967, Aug. 2020, doi: 10.1007/S10462-020-09896-5.
P. Bahad and P. Saxena, “Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics,” pp. 235–244, 2020, doi: 10.1007/978-981-15-0633-8_22.
Ambuj Kumar Agarwal, Gulista Khan, Shamimul Qamar, Niranjan Lal,Localization and correction of location information for nodes in UWSN-LCLI,Advances in Engineering Software,Volume 173,2022,103265,ISSN 0965-9978,https://doi.org/10.1016/j.advengsoft.2022.103265
R. Sharma, H. Pandey, and A. K. Agarwal, “Exploiting artificial intelligence for combating COVID-19 : a review and appraisal,” vol. 12, no. 1, pp. 514–520, 2023, doi: 10.11591/eei.v12i1.4366.
D. Srivastava, H. Pandey, and A. K. Agarwal, “Complex predictive analysis for health care : a comprehensive review,” vol. 12, no. 1, pp. 521–531, 2023, doi: 10.11591/eei.v12i1.4373.
A. K. . Agarwal, V. . Kiran, R. K. . Jindal, D. . Chaudhary, and R. G. . Tiwari, “Optimized Transfer Learning for Dog Breed Classification”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 18–22, Oct. 2022.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
