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Girar una perilla: predicción basada en aprendizaje profundo del par y los ángulos del brazo utilizando miografía de

Ramandeep Singh¹, Parikshith Chavakula¹, Joy Chatterjee¹

¹Neuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, 110029, India.

Physical and engineering sciences in medicine

|December 15, 2025

Abstract:

Accurate prediction of human motor actions is essential for developing intuitive, responsive, and adaptive human-machine interaction systems. This study investigates the use of force myography (FMG) to predict knob-turning activity with varying torque values and arm angles. Participants performed knob-turning activities on three spiral springs with different torque values and at four arm angles. A convolution neural network, long short-term memory hybrid classification approach was employed to classify the FMG data and predict torque and arm angle with an overall accuracy of 95.87 ± 2.59% and 94.06 ± 2.44%, respectively. The study also shows that the presence of subcutaneous fat did not significantly affect the classification of torque and arm angle ([Formula: see text], Mann-Whitney U test). These findings demonstrate the potential of FMG as an effective method for accurately predicting activities of daily life involving tasks with varying torque and arm angles.

Palabras clave:

Actividades de la vida diaria Ángulos del brazo Aprendizaje profundo Miografía de fuerza Giro de perilla Par

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