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An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification.

, , , , , , , and . ECML/PKDD (6), volume 14174 of Lecture Notes in Computer Science, page 312-329. Springer, (2023)

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Rehabilitation Exercise Segmentation for Autonomous Biofeedback Systems with ConvFSM., , , , and . EMBC, page 574-579. IEEE, (2019)Combining Real-Time Segmentation and Classification of Rehabilitation Exercises with LSTM Networks and Pointwise Boosting., , , , and . AAAI, page 13229-13234. AAAI Press, (2020)Introducing ufo.js: A browser-oriented p2p network., , and . ICNC, page 353-357. IEEE, (2014)An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification., , , , , , , and . ECML/PKDD (6), volume 14174 of Lecture Notes in Computer Science, page 312-329. Springer, (2023)Human Activity Recognition with Convolutional Neural Networks., , , , , and . ECML/PKDD (3), volume 11053 of Lecture Notes in Computer Science, page 541-552. Springer, (2018)Automatic classification of knee rehabilitation exercises using a single inertial sensor: A case study., , , , and . BSN, page 21-24. IEEE, (2018)Fast and robust video-based exercise classification via body pose tracking and scalable multivariate time series classifiers., , , , , , , , and . Data Min. Knowl. Discov., 37 (2): 873-912 (March 2023)The Importance of Real-World Validation of Machine Learning Systems in Wearable Exercise Biofeedback Platforms: A Case Study., , , , and . Sensors, 21 (7): 2346 (2021)