Estimating Muscle Fibre Conduction Velocity in the Presence of Array Misalignment

Gilliam, C and Jelfs, B 2019, 'Estimating Muscle Fibre Conduction Velocity in the Presence of Array Misalignment', in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, USA, 12th to 15th November 2018, pp. 853-860.


Document type: Conference Paper
Collection: Conference Papers

Title Estimating Muscle Fibre Conduction Velocity in the Presence of Array Misalignment
Author(s) Gilliam, C
Jelfs, B
Year 2019
Conference name 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Conference location Honolulu, USA
Conference dates 12th to 15th November 2018
Proceedings title 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Publisher IEEE
Place of publication USA
Start page 853
End page 860
Total pages 8
Abstract Surface electromyography (sEMG) has the potential to provide valuable information regarding the status and health of a muscle. In particular, recent developments in high density sEMG (HD-sEMG), which allow simultaneous recordings from a greater number of electrodes, enable the calculation of muscle attributes such as the conduction velocity of motor unit action potentials. However, as with standard recording montages, HDsEMG requires careful placement of the electrodes to align with the direction of the muscle fibres, thus limiting practical applications. In this paper we demonstrate an algorithm for calculating muscle fibre conduction velocity which is independent of the alignment of the array. The algorithm automatically corrects for the misalignment of the array whilst estimating the conduction velocity using common local all-pass (CLAP) filters. Specifically, the misalignment is modelled as a rotation of the array relative to the fibre and this rotation is estimated by iteratively fitting the model to the output of the CLAP filters. We validate the proposed algorithm on simulated HD-sEMG data generated from a realistic biological model, demonstrating that the algorithm obtains an accurate estimate of the conduction velocity even when the array is misaligned.
Subjects Signal Processing
Keyword(s) Signal processing
Signal Alignment
High Density sEMG
All-pass filters
DOI - identifier 10.23919/APSIPA.2018.8659741
Copyright notice © 2019 IEEE
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