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In this paper, some ideas on the impact of specific input features on AI model performance for fitness exercise recognition is reported and discussed.
Aug 1, 2022 · In this paper, some ideas on the impact of specific input features on AI model performance for fitness exercise recognition is reported and ...
In this paper, some ideas on the impact of specific input features on AI model performance for fitness exercise recognition is reported and discussed, ...
On feature selection in automatic detection of fitness exercises using ...
ieeexplore.ieee.org › iel7
Since LSTM is a supervised learning technique, data must be labelled to indicate the results that the algorithm should learn to obtain. The input signal is the ...
On feature selection in automatic detection of fitness exercises using LSTM models. SISINNI E.;DEPARI A.;BELLAGENTE P.;FERRARI P.;FLAMMINI A.;PASETTI M.
On feature selection in automatic detection of fitness exercises using ...
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On feature selection in automatic detection of fitness exercises using LSTM models. E. Sisinni, A. Depari, P. Bellagente, P. Ferrari, A. Flammini, M ...
Mar 24, 2022 · The raw time series data are fed to the long short-term memory (LSTM) neural network based on sliding window approach to recognize exercises.
A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone ...
May 31, 2024 · Results of the LSTM regression analysis performed on the best selected features resulted from (1) F-test, (2) RReliefF, and (3) mRMR algorithms.
Missing: fitness | Show results with:fitness
Dec 29, 2023 · ded methods incorporate feature selection as part of the model training process, offering a balance between efficiency and feature interactions.