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All Journal Journal of Applied Science, Technology & Humanities
Muhammad Restu Alvian Firmansyah
Institut Pertanian Bogor
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Social Sciences

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Evaluation of Random Forest and LSTM for sEMG-Based Estimation of Two DOF Wrist Kinematics Muhammad Restu Alvian Firmansyah; Alwi Harliansyah HRP
Journal of Applied Science, Technology & Humanities Vol. 3 No. 4 (2026): September 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/b03ak471

Abstract

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 channel can support discrete motion-intent classification and continuous two-degree-of-freedom (2-DOF) wrist kinematic estimation for exoskeleton control. Two anonymised datasets, recorded at 200 Hz using one forearm sEMG channel and one inertial measurement unit (IMU), were processed through detrending, 50 Hz notch filtering, fourth-order zero-phase 20–90 Hz band-pass filtering, and 250 ms windowing with 50% overlap. Seven time- and frequency-domain features were extracted and provided to Random Forest (RF), long short-term memory (LSTM), and hybrid RF–LSTM models. Under a strict held-out-trial protocol, LSTM achieved higher five-class classification accuracy than RF (95.56% versus 87.28%). Regression performance was weaker: LSTM produced the best flexion–extension result (R² = 0.290, RMSE = 12.49°), while hybrid RF–LSTM performed best for radial–ulnar estimation (R² = 0.204, RMSE = 12.35°). Closed-loop Dynamixel MX-28AT tracking showed zero overshoot. Overall, single-channel sEMG supports reliable intent classification, but proportional angle estimation remains limited, indicating that further sensing or regression-specific feature optimization is required for precise control.
Extended Telemetry Reliability Analysis of a Solar-Powered IoT Smart Irrigation System Alwi Harliansyah HRP; Muhammad Restu Alvian Firmansyah
Journal of Applied Science, Technology & Humanities Vol. 3 No. 4 (2026): September 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/ak67hc54

Abstract

This study presents a differentiated reanalysis of telemetry from a stand-alone solar-powered smart irrigation prototype. Instead of repeating the final-project summary, the analysis uses an updated August export containing 602 additional observations and applies continuity, completeness, temporal harmonization, ceiling-aware soil-moisture, and rule-consistency audits. The combined dataset contained 12,025 unique timestamps from 1 July to 9 August 2026. No missing value was interpolated. Sampling gaps greater than 30 minutes formed 42 sessions, while ten core telemetry fields were simultaneously valid in 63.29% of records. The two soil-moisture channels produced 3,819 paired observations and 3,682 paired five-minute bins. The automatic and manual channels reached the 100% ceiling in 71.25% and 46.35% of paired records, respectively; daily median differences were therefore zero percentage points with an interquartile range of 0-6. Across 3,789 valid battery-voltage and Energy Management System code pairs, threshold agreement was 99.60%. After 9 July, panel and battery voltage ranks were strongly associated (rho=0.995), with a median offset of 0.06 V. The extended audit confirms reliable rule classification and closely tracking voltage channels, but it also shows that sensor ceilings, incomplete environmental readings, and absent pump-ON history prevent valid claims about irrigation, water, or energy efficiency.
Co-Authors Alwi Harliansyah HRP