Non-linearity in motor unit velocity twitch dynamics: implications for ultrafast ultrasound source separation

Lubel, Emma ; Sgambato, Bruno Grandi ; Rohlén, Robin ; Ibánez, Jaime (Universidad de Zaragoza) ; Barsakcioglu, Deren Y. ; Tang, Meng-Xing ; Farina, Dario

Resumen: Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is partly limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.

Idioma: Inglés

DOI: 10.1109/TNSRE.2023.3315146

Año: 2023

Publicado en: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 31 (2023), 3699-3710
ISSN: 1534-4320

Factor impacto JCR: 4.8 (2023)

Categ. JCR: REHABILITATION rank: 5 / 170 = 0.029 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 32 / 123 = 0.26 (2023) - Q2 - T1

Factor impacto CITESCORE: 8.6 - Internal Medicine (Q1) - Neuroscience (all) (Q1) - Biomedical Engineering (Q1) - Rehabilitation (Q1)

Factor impacto SCIMAGO: 1.315 - Biomedical Engineering (Q1) - Computer Science Applications (Q1) - Rehabilitation (Q1) - Medicine (miscellaneous) (Q1) - Neuroscience (miscellaneous) (Q1) - Internal Medicine (Q1)

Financiación: info:eu-repo/grantAgreement/EC/H2020/899822/EU/Ultrasound peripheral interface and in-vitro model of human somatosensory system and muscles for motor decoding and restoration of somatic sensations in amputees/SOMA
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031905-I

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)