A threshold-based approach for muscle contraction detection from surface EMG signals

Gaudi Morantes; Gerardo Fernández; Miguel Altuve

doi:10.1117/12.2035673

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19 November 2013 A threshold-based approach for muscle contraction detection from surface EMG signals

Gaudi Morantes, Gerardo Fernández, Miguel Altuve

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Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 89220C (2013) https://doi.org/10.1117/12.2035673
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico

Abstract

Surface electromyographic (SEMG) signals are commonly used as control signals in prosthetic and orthotic devices. Super cial electrodes are placed on the skin of the subject to acquire its muscular activity through this signal. The muscle contraction episode is then in charge of activating and deactivating these devices. Nevertheless, there is no gold standard" to detect muscle contraction, leading to delayed responses and false and missed detections. This fact motivated us to propose a new approach that compares a smoothed version of the SEMG signal with a xed threshold, in order to detect muscle contraction episodes. After preprocessing the SEMG signal, the smoothed version is obtained using a moving average lter, where three di erent window lengths has been evaluated. The detector was tuned by maximizing sensitivity and speci city and evaluated using SEMG signals obtained from the anterior tibial and gastrocnemius muscles, taken during the walking of ve subjects. Compared with traditional detection methods, we obtain a reduction of 3 ms in the detection delay, an increase of 8% in sensitivity but a decrease of 15% in speci city. Future work is directed to the inclusion of a temporal threshold (a double-threshold approach) to minimize false detections and reduce detection delays.

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Gaudi Morantes, Gerardo Fernández, and Miguel Altuve "A threshold-based approach for muscle contraction detection from surface EMG signals", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220C (19 November 2013); https://doi.org/10.1117/12.2035673

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