Author

Alwi Harliansyah Hrp

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Evaluation of Random Forest and LSTM for IMU-Based Wrist Movement Classification with sEMG Monitoring

This study evaluates Random Forest and Long Short-Term Memory and Long Short-Term Memory for five-class wrist-movement classification using six-channel inertial measurement unit (IMU) data, while surface electromyography (sEMG) is used for RMS-based muscle-activity monitoring.

M. Restu, Alviandiky Firmansyah, Alwi Harliansyah Hrp · 0 citations

Open access Sep 2026

Evaluation of Random Forest and LSTM for sEMG-Based Estimation of Two DOF Wrist Kinematics

Robotic wrist rehabilitation after stroke requires control signals that reflect the user’s volitional intent, while clinically deployable systems are constrained by the number of surface electromyography (sEMG) channels that can be practically placed on a hemiparetic forearm. This study evaluates how far a single sEMG...

Muhammad Restu Alvian Firmansyah, Alwi Harliansyah Hrp · Journal of Applied Science,... · 0 citations