A Muscle Fibre Conduction Velocity Tracking ASIC for Local Fatigue Monitoring

Abstract

Abstract-Electromyography analysis can provide information about a muscle's fatigue state by estimating Muscle Fibre Conduction Velocity (MFCV), a measure of the travelling speed of Motor Unit Action Potentials (MUAPs) in muscle tissue. MFCV better represents the physical manifestations of muscle fatigue, compared to the progressive compression of the myoelectic Power Spectral Density, hence it is more suitable for a muscle fatigue tracking system. This paper presents a novel algorithm for the estimation of MFCV using single threshold bit-stream conversion and a dedicated ASIC for its implementation, suitable for a compact, wearable and easy to use muscle fatigue monitor. The presented ASIC is implemented in a commercially available AMS 0.35µm CMOS technology and utilizes a bit-stream crosscorrelator that estimates the conduction velocity of the myoelectric signal in real time. A test group of 20 subjects was used to evaluate the performance of the developed ASIC, achieving good accuracy with an error of only 3.2% compared to Matlab.

Field Programmable Gate Array-Embedded Platform for Dynamic Muscle Fiber Conduction Velocity Monitoring

“…In this work, we refer to the diabetic peripheral neuropathy (DPN), a pathology associated with vascular injuries typical of diabetic subjects, which leads to changes in a parameter known as muscle fiber conduction velocity (MFCV) [5]. The MFCV has an important clinical role in the progress assessment of several pathologies [5,6,7,8] and in muscle fatigue monitoring [8,9].…”

“…Despite its clinical applicability, there are currently no solutions which are able to evaluate in real time MFCV during a dynamic task (i.e., ordinary life movements such as walking) and fatigue [8,11,12,13]. In most cases, this kind of solution requires cumbersome structure such as ergometer cycles [12,13], limiting their use to ambulatory applications.…”

“…In most cases, this kind of solution requires cumbersome structure such as ergometer cycles [12,13], limiting their use to ambulatory applications. Other diffused solutions force the subject to keep long isotonic contraction to reliably extract MFCV [8,12,13]. Nevertheless, the measurement protocol on which the systems are based limits the comfort and freedom of movement for the subject.…”

FPGA Implementation of a Single Step MFCV Estimator Based on EMG in Diabetic Neuropathy

“…Starting from its first definition in 1943 by Denslow and Hasset, the muscle fibre conduction velocity (MFCV) has gained more and more importance in the neurophysiology of healthy muscles [5]. It got a relevant role in gait analysis, rehabilitation, and prosthetic scenarios [6,7], as well as in the clinical investigation of polyneuropathies, such as DPN [1]. Recently, it has been proved that MFCV can be used to detect muscle fibre denervation atrophy, as early sign of motor axonal loss [2].…”

“…In this case, the MFCV is derived by the ratio between the interelectrode distance (Δd) and the measure of the propagation time θ between two EMG electrodes placed along the same muscle fibre [6]. Despite its clinical applicability, the literature does not propose solutions that allow a real-time assessment of the muscle fibre conduction velocity (MFCV) and fatigue, in ordinary life movements [7][8][9][10]. In the most cases, these solutions require cumbersome static structure, such as an ergometer cycle [9,10], or force the subject to keep the contraction all along the measurements [7][8][9][10], limiting the subject's movement freedom.…”