@article{10.11648/j.jeee.20261401.14,
  author = {Rojo Nofidiantsoa Randriamanalina and Fetraharijaona Ramahandrisoa and Faly Herizo Andriambololoniaina and Rivo Mahandrisoa Randriamaroson},
  title = {Algorithmic Approach to an Embedded Electronic Medical Device for EMG-to-Joint Angle Regression},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {14},
  number = {1},
  pages = {34-45},
  doi = {10.11648/j.jeee.20261401.14},
  url = {https://doi.org/10.11648/j.jeee.20261401.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20261401.14},
  abstract = {This study proposes an algorithmic approach for the development of a bio-inspired prosthetic hand system controlled by surface electromyographic (EMG) signals, aiming to achieve natural, adaptive, and continuous motion in upper-limb prostheses. The proposed framework integrates biomedical signal processing, machine learning–based motor intention decoding, and embedded mechatronic control within a unified system. Multi-channel surface EMG signals were acquired from the forearm and processed through a dedicated pipeline including amplification, physiologically relevant filtering, feature extraction, and normalization. To infer motor intention, two learning paradigms were investigated and compared: a classical Support Vector Machine (SVM) using handcrafted EMG features, and a Long Short-Term Memory (LSTM) neural network designed to perform continuous regression of finger joint angles corresponding to the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints. While the SVM provided a baseline for gesture-related decoding, the LSTM demonstrated a clear advantage by explicitly modeling temporal dependencies and non-linear relationships in sequential EMG data, resulting in more accurate and temporally coherent kinematic predictions. Experimental validation was carried out on a custom bio-inspired prosthetic prototype equipped with potentiometric joint feedback, showing that the LSTM-based controller achieved higher prediction accuracy and smoother real-time control during representative gestures such as flexion, extension, and grasping. Furthermore, deployment using TensorFlow Lite confirmed the feasibility of embedding deep sequential models on low-power hardware platforms. Overall, this work highlights the importance of temporal modeling for EMG-driven control and establishes a robust foundation for neural-controlled prosthetic systems that combine signal intelligence, physiological relevance, and embedded optimization, contributing to the advancement of human–machine interfaces aimed at restoring dexterity and autonomy in amputee patients.},
 year = {2026}
}

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TY  - JOUR
T1  - Algorithmic Approach to an Embedded Electronic Medical Device for EMG-to-Joint Angle Regression
AU  - Rojo Nofidiantsoa Randriamanalina
AU  - Fetraharijaona Ramahandrisoa
AU  - Faly Herizo Andriambololoniaina
AU  - Rivo Mahandrisoa Randriamaroson
Y1  - 2026/01/30
PY  - 2026
N1  - https://doi.org/10.11648/j.jeee.20261401.14
DO  - 10.11648/j.jeee.20261401.14
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 34
EP  - 45
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20261401.14
AB  - This study proposes an algorithmic approach for the development of a bio-inspired prosthetic hand system controlled by surface electromyographic (EMG) signals, aiming to achieve natural, adaptive, and continuous motion in upper-limb prostheses. The proposed framework integrates biomedical signal processing, machine learning–based motor intention decoding, and embedded mechatronic control within a unified system. Multi-channel surface EMG signals were acquired from the forearm and processed through a dedicated pipeline including amplification, physiologically relevant filtering, feature extraction, and normalization. To infer motor intention, two learning paradigms were investigated and compared: a classical Support Vector Machine (SVM) using handcrafted EMG features, and a Long Short-Term Memory (LSTM) neural network designed to perform continuous regression of finger joint angles corresponding to the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints. While the SVM provided a baseline for gesture-related decoding, the LSTM demonstrated a clear advantage by explicitly modeling temporal dependencies and non-linear relationships in sequential EMG data, resulting in more accurate and temporally coherent kinematic predictions. Experimental validation was carried out on a custom bio-inspired prosthetic prototype equipped with potentiometric joint feedback, showing that the LSTM-based controller achieved higher prediction accuracy and smoother real-time control during representative gestures such as flexion, extension, and grasping. Furthermore, deployment using TensorFlow Lite confirmed the feasibility of embedding deep sequential models on low-power hardware platforms. Overall, this work highlights the importance of temporal modeling for EMG-driven control and establishes a robust foundation for neural-controlled prosthetic systems that combine signal intelligence, physiological relevance, and embedded optimization, contributing to the advancement of human–machine interfaces aimed at restoring dexterity and autonomy in amputee patients.
VL  - 14
IS  - 1
ER  - 

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