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Geological and Mining Engineering

Geological and Mining Engineering (GME), published by Scineer Publishing PTE. LTD, is an open-access journal. GME is devoted to publish geological and mining engineering related articles in the form of original papers, review papers and case studies. It also provides a platform for scholars to exchange the latest information in the related fields and aim to promote the research on geological and mining engineering throughout the world. GME welcomes all original

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Scineer Publishing is a publisher of open access journals. Covering various disciplines and promoting interdisciplinary development, Scineer Publishing adheres to a principle of respecting academics, and publishes insightful and rigorous academic research contents.

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E-mail: scineerpub@126.com

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Copyright © 2023 Scineer Publishing Pte. Ltd.

AI-Based Pendulum Motion Recognition and Period Prediction Using Smartphone Sensor Data

Yunhang Xia, Xu Zhang

Abstract


This study presents an AI-based framework for classifying pendulum motion and estimating an amplitude-corrected reference period from inertial signals. A controlled pilot dataset contained 1, 300 five-second sequences sampled at 50 Hz and represented stationary, smallangle periodic, large-angle periodic, damped, and disturbed motion. Forty-eight time-domain, frequency-domain, and cross-channel features
were extracted from three specific-force channels and one angular-velocity channel. Four lightweight model families were evaluated using
a single stratified 80/20 train-test split. On 260 classification test sequences, random forest achieved 99.62% accuracy and a macro-F1 score
of 99.62%. On 208 non-stationary sequences, support vector regression achieved an RMSE of 0.052 s and R² of 0.962, while random-forest
regression produced an MAE of 0.026 s and MAPE of 1.41%. The results establish a quantitative benchmark for the proposed framework under the defined study conditions. Repeated leakage-controlled validation and the release of code, data, split indices, and final hyperparameters
would further strengthen reproducibility and generalizability.

Keywords


Pendulum dynamics; Inertial sensors; Motion classification; Period regression; Physics-guided machine learning; Time-series analysis

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DOI: http://dx.doi.org/10.70711/frim.v4i8.10021

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Scineer Publishing is a publisher of open access journals. Covering various disciplines and promoting interdisciplinary development, Scineer Publishing adheres to a principle of respecting academics, and publishes insightful and rigorous academic research contents.

12 Eu Tong Sen Street, #07-168, Singapore 059819
E-mail: scineerpub@126.com

Scineer

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Copyright © 2023 Scineer Publishing Pte. Ltd.