{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:19:34Z","timestamp":1772727574997,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Research Foundation of Korea through University Innovation Research Project","award":["INNO-2022-01"],"award-info":[{"award-number":["INNO-2022-01"]}]},{"DOI":"10.13039\/501100003725","name":"the Kyungbok University\u2019s Industry-University Cooperation Foundation","doi-asserted-by":"publisher","award":["INNO-2022-01"],"award-info":[{"award-number":["INNO-2022-01"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>The increasing prevalence of machine learning (ML) and automated machine learning (AutoML) applications across diverse industries necessitates rigorous comparative evaluations of their predictive accuracies under various computational environments. The purpose of this research was to compare and analyze the predictive accuracy of several machine learning algorithms, including RNNs, LSTMs, GRUs, XGBoost, and LightGBM, when implemented on different platforms such as Google Colab Pro, AWS SageMaker, GCP Vertex AI, and MS Azure. The predictive performance of each model within its respective environment was assessed using performance metrics such as accuracy, precision, recall, F1-score, and log loss. All algorithms were trained on the same dataset and implemented on their specified platforms to ensure consistent comparisons. The dataset used in this study comprised fitness images, encompassing 41 exercise types and totaling 6 million samples. These images were acquired from AI-hub, and joint coordinate values (x, y, z) were extracted utilizing the Mediapipe library. The extracted values were then stored in a CSV format. Among the ML algorithms, LSTM demonstrated the highest performance, achieving an accuracy of 73.75%, precision of 74.55%, recall of 73.68%, F1-score of 73.11%, and a log loss of 0.71. Conversely, among the AutoML algorithms, XGBoost performed exceptionally well on AWS SageMaker, boasting an accuracy of 99.6%, precision of 99.8%, recall of 99.2%, F1-score of 99.5%, and a log loss of 0.014. On the other hand, LightGBM exhibited the poorest performance on MS Azure, achieving an accuracy of 84.2%, precision of 82.2%, recall of 81.8%, F1-score of 81.5%, and a log loss of 1.176. The unnamed algorithm implemented on GCP Vertex AI showcased relatively favorable results, with an accuracy of 89.9%, precision of 94.2%, recall of 88.4%, F1-score of 91.2%, and a log loss of 0.268. Despite LightGBM\u2019s lackluster performance on MS Azure, the GRU implemented in Google Colab Pro displayed encouraging results, yielding an accuracy of 88.2%, precision of 88.5%, recall of 88.1%, F1-score of 88.4%, and a log loss of 0.44. Overall, this study revealed significant variations in performance across different algorithms and platforms. Particularly, AWS SageMaker\u2019s implementation of XGBoost outperformed other configurations, highlighting the importance of carefully considering the choice of algorithm and computational environment in predictive tasks. To gain a comprehensive understanding of the factors contributing to these performance discrepancies, further investigations are recommended.<\/jats:p>","DOI":"10.3390\/bioengineering10080891","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T02:10:45Z","timestamp":1690510245000},"page":"891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure"],"prefix":"10.3390","volume":"10","author":[{"given":"Wansuk","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Physical Therapy, International University of Korea, Jinju 17731, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0858-121X","authenticated-orcid":false,"given":"Taeseok","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Medical Performance Center (MPC), Sejong Sports Medicine and Performance Hospital, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0763-8245","authenticated-orcid":false,"given":"Seoyoon","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Occupational Therapy, College of Medical and Health Sciences, Kyungbok University, Namyangju 12051, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Diaz, S., Stephenson, J.B., and Labrador, M.A. (2019). Use of wearable sensor technology in gait, balance, and range of motion analysis. Appl. Sci., 10.","DOI":"10.3390\/app10010234"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Choi, W., and Heo, S. (2021). Deep learning approaches to automated video classification of upper limb tension test. Healthcare, 9.","DOI":"10.3390\/healthcare9111579"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"GochOO, M., Kim, T., Bae, J., Kim, D., Kim, Y., and Cho, J. (2021). Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network. Remote Sens., 13.","DOI":"10.3390\/rs13050912"},{"key":"ref_4","first-page":"102754","article-title":"Vision-Based Estimation of MDS-UPDRS Scores for Quantifying Parkinson\u2019s Disease Tremor Severity. Med","volume":"75","author":"Liu","year":"2022","journal-title":"Image Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s00530-022-00980-0","article-title":"A Comprehensive Survey on Human Pose Estimation Approaches","volume":"29","author":"Dubey","year":"2023","journal-title":"Multimed. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chung, J.-L., Ong, L.-Y., and Leow, M.-C. (2022). Comparative Analysis of Skeleton-Based Human Pose Estimation. Future Internet, 14.","DOI":"10.3390\/fi14120380"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Garg, S., Saxena, A., and Gupta, R. (2022). Yoga Pose Classification: A CNN and MediaPipe Inspired Deep Learning Approach for Real-World Application. J. Ambient Intell. Humaniz. Comput., 1\u201312.","DOI":"10.1007\/s12652-022-03910-0"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, A.-L., and Chu, W.-T. (2020, January 28\u201330). A Posture Evaluation System for Fitness Videos Based on Recurrent Neural Network. Proceedings of the 2020 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan.","DOI":"10.1109\/IS3C50286.2020.00055"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2059031","DOI":"10.1142\/S0218001420590314","article-title":"Estimate of Head Posture Based on Coordinate Transformation with MP-MTM-LSTM Network","volume":"34","author":"Yang","year":"2020","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lin, C.-B., Lin, Y.-H., Huang, P.-J., Wu, Y.-C., and Liu, K.-H. (2020). A Framework for Fall Detection Based on OpenPose Skeleton and LSTM\/GRU Models. Appl. Sci., 11.","DOI":"10.3390\/app11010329"},{"key":"ref_11","unstructured":"Sorokina, V., and Ablameyko, S. (2021, January 21\u201324). Extraction of Human Body Parts in Image Using Convolutional Neural Network and Attention Model. Proceedings of the 15th International Conference, Minsk, Belarus."},{"key":"ref_12","first-page":"36","article-title":"Dual Ultra-Wideband (UWB) Radar-Based Sleep Posture Recognition System: Towards Ubiquitous Sleep Monitoring","volume":"4","author":"Lai","year":"2023","journal-title":"Eng. Regener."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101822","DOI":"10.1016\/j.artmed.2020.101822","article-title":"Automated machine learning: Review of the state-of-the-art and opportunities for healthcare","volume":"104","author":"Waring","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.21037\/qims-20-922","article-title":"Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: A comparative study","volume":"11","author":"Wan","year":"2021","journal-title":"Quant Imaging Med. Surg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1186\/s40537-022-00646-8","article-title":"Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML","volume":"9","author":"Siriborvornratanakul","year":"2022","journal-title":"J. Big Data"},{"key":"ref_16","unstructured":"AIHub by the Korea Intelligence Society Agency (2023, January 03). Available online: https:\/\/aihub.or.kr\/aihubdata\/data\/list.do?pageIndex=1&currMenu=115&topMenu=100&da-ta-SetSn=&srchOrder=&SrchdataClCode=DATACL001&searchKeyword=&srchDataRealmCode=REALM006."},{"key":"ref_17","unstructured":"Amazon Web Services (2023, January 15). Build, Tune, and Deploy an End-to-End Churn Prediction Model Using Amazon SageMaker Pipelines. Available online: https:\/\/aws.amazon.com\/ko\/blogs\/machine-learning\/build-tune-and-deploy-an-end-to-end-churn-prediction-model-using-amazon-sagemaker-pipelines\/."},{"key":"ref_18","unstructured":"(2023, January 15). Google Cloud. End-to-End AutoML Workflow. Available online: https:\/\/cloud.google.com\/vertex-ai\/docs\/tabular-data\/tabular-workflows\/e2e-AutoML?hl=ko#end-to-end_on."},{"key":"ref_19","unstructured":"Microsoft (2023, January 15). Azure Machine Learning Solution Architecture. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/architecture\/solution-ideas\/articles\/azure-machine-learning-solution-architecture."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhu, F., Hua, W., and Zhang, Y. (2023, January 24\u201326). GRU Deep Residual Network for Time Series Classification. Proceedings of the 2023 IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.","DOI":"10.1109\/ITNEC56291.2023.10082454"},{"key":"ref_21","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014, January 8\u201313). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Proceedings of the Neural Information Processing Systems, NIPS, Montreal, QC, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, S., and Mingyu, S. (2022, January 15\u201317). A comparison between linear regression, lasso regression, decision tree, XGBoost, and RNN for asset price strategies. Proceedings of the International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), Huzhou, China.","DOI":"10.1117\/12.2646634"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s11571-022-09819-w","article-title":"The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system","volume":"17","author":"Pan","year":"2023","journal-title":"Cogn. Neurodyn."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Garcia, S., Luengo, J., and Herrera, F. (2015). Data Preprocessing in Data Mining, Springer.","DOI":"10.1007\/978-3-319-10247-4"},{"key":"ref_25","unstructured":"Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_26","unstructured":"He, X., Zhao, K., and Chu, X. (2019). Automl: A Survey of the State of-the-Art. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zaharia, S., Rebedea, T., and Trausan-Matu, S. (2022). Machine Learning-Based Security Pattern Recognition Techniques for Code Developers. Appl. Sci., 12.","DOI":"10.3390\/app122312463"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","article-title":"A survey on ensemble learning","volume":"14","author":"Dong","year":"2020","journal-title":"Front. Comput. Sci."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/8\/891\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:20:46Z","timestamp":1760127646000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/8\/891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,27]]},"references-count":28,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["bioengineering10080891"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering10080891","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,27]]}}}