{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T02:39:40Z","timestamp":1770691180425,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Activity recognition is the process of continuously monitoring a person\u2019s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to accurately learn and rehearse yoga postures without getting help from anyone else. With the use of deep learning algorithms, we propose an approach for the efficient detection and recognition of various yoga poses. The chosen dataset consists of 85 videos with 6 yoga postures performed by 15 participants, where the keypoints of users are extracted using the Mediapipe library. A combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has been employed for yoga pose recognition through real-time monitored videos as a deep learning model. Specifically, the CNN layer is used for the extraction of features from the keypoints and the following LSTM layer understands the occurrence of sequence of frames for predictions to be implemented. In following, the poses are classified as correct or incorrect; if a correct pose is identified, then the system will provide user the corresponding feedback through text\/speech. This paper combines machine learning foundations with data structures as the synergy between these two areas can be established in the sense that machine learning techniques and especially deep learning can efficiently recognize data schemas and make them interoperable.<\/jats:p>","DOI":"10.3390\/a15110403","type":"journal-article","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T23:26:32Z","timestamp":1667258792000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Deep Learning Models for Yoga Pose Monitoring"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7775-3244","authenticated-orcid":false,"given":"Debabrata","family":"Swain","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department, Pandit Deendayal Energy University, Gandhinagar 382007, India"}]},{"given":"Santosh","family":"Satapathy","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Pandit Deendayal Energy University, Gandhinagar 382007, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6506-9207","authenticated-orcid":false,"given":"Biswaranjan","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering-AI, Marwadi University, Rajkot 360003, India"}]},{"given":"Madhu","family":"Shukla","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering-AI and Big Data Analytics, Marwadi University, Rajkot 360003, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9895-7606","authenticated-orcid":false,"given":"Vassilis C.","family":"Gerogiannis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9964-4134","authenticated-orcid":false,"given":"Andreas","family":"Kanavos","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}]},{"given":"Dimitris","family":"Giakovis","sequence":"additional","affiliation":[{"name":"Experimental School of Larissa, Ministry of Education, 41334 Larissa, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/frobt.2015.00028","article-title":"A Review of Human Activity Recognition Methods","volume":"2","author":"Vrigkas","year":"2015","journal-title":"Front. Robot. AI"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3390\/computers2020088","article-title":"A Review on Video-Based Human Activity Recognition","volume":"2","author":"Ke","year":"2013","journal-title":"Computers"},{"key":"ref_3","unstructured":"Kothari, S. (2020). Yoga Pose Classification Using Deep Learning. [Ph.D. Thesis, San Jose State University]."},{"key":"ref_4","first-page":"18","article-title":"Recognition of Human Unusual Activity in Surveillance Videos","volume":"2","author":"Acharya","year":"2015","journal-title":"Int. J. Res. Sci. Innov. (IJRSI)"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Stephens, I. (2017). Medical Yoga Therapy. Children, 4.","DOI":"10.3390\/children4020012"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1111\/j.1749-8171.2009.00171.x","article-title":"The Development of Modern Yoga: A Survey of the Field","volume":"3","author":"Newcombe","year":"2009","journal-title":"Relig. Compass"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.4103\/0973-6131.85485","article-title":"Exploring the Therapeutic Effects of Yoga and its Ability to Increase Quality of Life","volume":"4","author":"Woodyard","year":"2011","journal-title":"Int. J. Yoga"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dash, S., Acharya, B.R., Mittal, M., Abraham, A., and Kelemen, A. (2020). Deep Learning Techniques for Biomedical and Health Informatics, Springer.","DOI":"10.1007\/978-3-030-33966-1"},{"key":"ref_9","unstructured":"Brownlee, J. (2022, September 10). Deep Learning Models for Human Activity Recognition. Available online: https:\/\/www.diva-portal.org\/smash\/record.jsf?pid=diva2%3A1480070&dswid=-5372."},{"key":"ref_10","unstructured":"Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C., and Grundmann, M. (2020). MediaPipe Hands: On-device Real-time Hand Tracking. arXiv."},{"key":"ref_11","first-page":"1111","article-title":"Human Activity Recognition: Challenges and Process Stages","volume":"5","author":"Alzahrani","year":"2016","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng. (IJIRCCE)"},{"key":"ref_12","first-page":"100046","article-title":"Deep Learning Based Human Activity Recognition (HAR) Using Wearable Sensor Data","volume":"1","author":"Gupta","year":"2021","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jogin, M., Madhulika, M.S., Divya, G.D., Meghana, R.K., and Apoorva, S. (2018, January 18\u201319). Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning. Proceedings of the 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT42901.2018.9012507"},{"key":"ref_14","unstructured":"Josyula, R., and Ostadabbas, S. (2021). A Review on Human Pose Estimation. arXiv, Available online: https:\/\/arxiv.org\/pdf\/2110.06877.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16329","DOI":"10.1007\/s00521-021-06232-y","article-title":"Deep Learning Models for Forecasting Aviation Demand Time Series","volume":"33","author":"Kanavos","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lyras, A., Vernikou, S., Kanavos, A., Sioutas, S., and Mylonas, P. (2021, January 26\u201328). Modeling Credibility in Social Big Data using LSTM Neural Networks. Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST), Online.","DOI":"10.5220\/0010726600003058"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Agrawal, Y., Shah, Y., and Sharma, A. (2020, January 10\u201312). Implementation of Machine Learning Technique for Identification of Yoga Poses. Proceedings of the 9th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India.","DOI":"10.1109\/CSNT48778.2020.9115758"},{"key":"ref_18","first-page":"186","article-title":"ExNET: Deep Neural Network for Exercise Pose Detection","volume":"Volume 1035","author":"Haque","year":"2018","journal-title":"Proceedings of the 2nd International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R)"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9349","DOI":"10.1007\/s00521-019-04232-7","article-title":"Real-time Yoga Recognition using Deep Learning","volume":"31","author":"Yadav","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_20","first-page":"143","article-title":"Yoga Posture Recognition","volume":"10","author":"Kadbhane","year":"2021","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng. (IJARCCE)"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L","article-title":"The Relationship between Recall and Precision","volume":"45","author":"Buckland","year":"1994","journal-title":"J. Am. Soc. Inf. Sci. (JASIS)"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Luvizon, D.C., Picard, D., and Tabia, H. (2018, January 18\u201322). 2D\/3D Pose Estimation and Action Recognition Using Multitask Deep Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00539"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Anilkumar, A., Athulya, K., Sajan, S., and Sreeja, K. (2021, January 28\u201329). Pose Estimated Yoga Monitoring System. Proceedings of the International Conference on IoT Based Control Networks & Intelligent Systems (ICICNIS), Kottayam, India.","DOI":"10.2139\/ssrn.3882498"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Savvopoulos, A., Kanavos, A., Mylonas, P., and Sioutas, S. (2018). LSTM Accelerator for Convolutional Object Identification. Algorithms, 11.","DOI":"10.3390\/a11100157"},{"key":"ref_25","unstructured":"Zou, J., Li, B., Wang, L., Li, Y., Li, X., Lei, R., and Sun, S. (December, January 29). Intelligent Fitness Trainer System Based on Human Pose Estimation. Proceedings of the International Conference On Signal And Information Processing, Networking And Computers (ICSINC), Yulin, China."},{"key":"ref_26","unstructured":"Chen, S., and Yang, R.R. (2020). Pose Trainer: Correcting Exercise Posture using Pose Estimation. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Thoutam, V.A., Srivastava, A., Badal, T., Mishra, V.K., Sinha, G.R., Sakalle, A., Bhardwaj, H., and Raj, M. (2022). Yoga Pose Estimation and Feedback Generation Using Deep Learning. Comput. Intell. Neurosci., Available online: https:\/\/www.hindawi.com\/journals\/cin\/2022\/4311350\/.","DOI":"10.1155\/2022\/4311350"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Al-Saffar, A.A.M., Tao, H., and Talab, M.A. (2017, January 23\u201324). Review of Deep Convolution Neural Network in Image Classification. Proceedings of the IEEE International Conference on Radar Antenna, Microwave, Electronics and Telecommunications (ICRAMET), Jakarta, Indonesia.","DOI":"10.1109\/ICRAMET.2017.8253139"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shiranthika, C., Premakumara, N., Chiu, H.L., Samani, H., Shyalika, C., and Yang, C.Y. (2020, January 2\u20134). Human Activity Recognition Using CNN & LSTM. Proceedings of the 5th IEEE International Conference on Information Technology Research (ICITR), Moratuwa, Sri Lanka.","DOI":"10.1109\/ICITR51448.2020.9310792"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","article-title":"Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features","volume":"6","author":"Ullah","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","unstructured":"Agarap, A.F. (2018). Deep Learning using Rectified Linear Units (ReLU). arXiv."},{"key":"ref_32","unstructured":"Swain, D., Pani, S.K., and Swain, D. (2022, September 10). Diagnosis of Coronary Artery Disease Using 1-D Convolutional Neural Network. Available online: https:\/\/www.ijrte.org\/wp-content\/uploads\/papers\/v8i2\/B2693078219.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Thakkar, V., Tewary, S., and Chakraborty, C. (2018, January 12\u201313). Batch Normalization in Convolutional Neural Networks\u2014A Comparative Study with CIFAR-10 Data. Proceedings of the 5th IEEE International Conference on Emerging Applications of Information Technology (EAIT), Kolkata, India.","DOI":"10.1109\/EAIT.2018.8470438"},{"key":"ref_34","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Szandala, T. (2021). Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks. Bio-Inspired Neurocomputing, Springer.","DOI":"10.1007\/978-981-15-5495-7_11"},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1049\/el.2018.7980","article-title":"Trimmed Categorical Cross-entropy for Deep Learning with Label Noise","volume":"55","author":"Rusiecki","year":"2019","journal-title":"Electron. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fatourechi, M., Ward, R.K., Mason, S.G., Huggins, J.E., Schl\u00f6gl, A., and Birch, G.E. (2008, January 11\u201315). Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets. Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA), San Diego, CA, USA.","DOI":"10.1109\/ICMLA.2008.34"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1147\/JRD.2017.2709578","article-title":"An Effective Algorithm for Hyperparameter Optimization of Neural Networks","volume":"61","author":"Diaz","year":"2017","journal-title":"IBM J. Res. Dev."},{"key":"ref_40","first-page":"689","article-title":"An Efficient System for the Prediction of Coronary Artery Disease using Dense Neural Network with Hyper Parameter Tuning","volume":"8","author":"Swain","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng. (IJITEE)"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"49","DOI":"10.4018\/IJOCI.2021070104","article-title":"Developing a Graphical User Interface for an Artificial Intelligence-Based Voice Assistant","volume":"11","author":"Subhash","year":"2021","journal-title":"Int. J. Organ. Collect. Intell. (IJOCI)"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lahitani, A.R., Permanasari, A.E., and Setiawan, N.A. (2016, January 26\u201327). Cosine Similarity to Determine Similarity Measure: Study Case in Online Essay Assessment. Proceedings of the 4th IEEE International Conference on Cyber and IT Service Management, Bandung, Indonesia.","DOI":"10.1109\/CITSM.2016.7577578"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"90847","DOI":"10.1109\/ACCESS.2020.2994222","article-title":"Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking","volume":"8","author":"Hasnain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","first-page":"39","article-title":"An Efficient Heart Disease Prediction System Using Machine Learning","volume":"1101","author":"Swain","year":"2020","journal-title":"Mach. Learn. Inf. Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Swain, D., Pani, S.K., and Swain, D. (2018, January 28\u201329). A Metaphoric Investigation on Prediction of Heart Disease using Machine Learning. Proceedings of the IEEE International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India.","DOI":"10.1109\/ICACAT.2018.8933603"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"19615","DOI":"10.1007\/s00521-022-07650-2","article-title":"Multiclass Sentiment Analysis on COVID-19 related Tweets using Deep Learning Models","volume":"34","author":"Vernikou","year":"2022","journal-title":"Neural Comput. Appl."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/11\/403\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:06:34Z","timestamp":1760144794000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/11\/403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,31]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["a15110403"],"URL":"https:\/\/doi.org\/10.3390\/a15110403","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,31]]}}}