Imanuel Simatupang

Politeknik Negeri Batam, Universitas Batam

Papers

Total Citations

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About

Imanuel Simatupang is a researcher specializing in human-computer interaction, biomedical signal processing, and machine learning, with a particular focus on electromyography (EMG)-based gesture recognition. His work centers on developing and comparing classification algorithms to interpret hand gestures from EMG signals captured by wearable devices like the Myo armband. In his most cited paper, "Comparison Gestures Recognition Using K-NN and Naïve Bayes" (2020, 6 citations), Simatupang systematically evaluates the performance of K-Nearest Neighbors and Naïve Bayes classifiers for recognizing five distinct hand gestures, providing foundational insights into algorithm selection for real-time prosthetic control and assistive technologies. His follow-up study, "Comparison EMG Pattern Recognition Using Bayes and NN Methods" (2020, 5 citations), extends this analysis by contrasting Naïve Bayes with Neural Networks, highlighting trade-offs between computational efficiency and accuracy. These comparative studies have been cited by researchers exploring low-latency, portable gesture interfaces. Simatupang’s contributions are particularly valuable for advancing accessible, non-invasive control systems in rehabilitation engineering and smart device interaction, where reliable EMG pattern recognition remains a critical challenge.