TY - JOUR

T1 - Identification of conductive fiber parameters with transcutaneous electrical nerve stimulation signal using RLS algorithm

AU - Schimmack, Manuel

AU - Mercorelli, Paolo

N1 - 9th IFAC Symposium on Biological and Medical Systems BMS 2015 — Berlin, Germany, 31 August-2 September 2015

PY - 2015/9/1

Y1 - 2015/9/1

N2 - This paper presents a scaled system identification method for medical application in the fields of noninvasive nerve stimulation and neurological disease treatment. The Recursive Least Squares (RLS) method with an implemented forgetting factor was used to estimate parameters, including inductance, within the nano range of a linear model, using input-output scaling factors. To estimate the parameters in the nano range, the input signal must have a very high frequency, which subsequently requires a very high sampling rate, and therefore expensive hardware and precise programming. In contrast, this technique allows for a lower sampling rate and an input signal with low frequency to identify the parameters characterizing the linear model, without requiring additional hardware for the estimation process. This method was used to provide a scaled identification bandwidth together with a reduced sampling rate to use a Transcutaneous Electrical Nerve Stimulation signal (TENS) by themself for the identification process. In addition, the proposed method identified the inductance of the conductive textile system, the most critical parameter to be estimated. The measured results indicated that the proposed RLS method along with a forgetting factor was an effective and robust method for estimating the parameters.

AB - This paper presents a scaled system identification method for medical application in the fields of noninvasive nerve stimulation and neurological disease treatment. The Recursive Least Squares (RLS) method with an implemented forgetting factor was used to estimate parameters, including inductance, within the nano range of a linear model, using input-output scaling factors. To estimate the parameters in the nano range, the input signal must have a very high frequency, which subsequently requires a very high sampling rate, and therefore expensive hardware and precise programming. In contrast, this technique allows for a lower sampling rate and an input signal with low frequency to identify the parameters characterizing the linear model, without requiring additional hardware for the estimation process. This method was used to provide a scaled identification bandwidth together with a reduced sampling rate to use a Transcutaneous Electrical Nerve Stimulation signal (TENS) by themself for the identification process. In addition, the proposed method identified the inductance of the conductive textile system, the most critical parameter to be estimated. The measured results indicated that the proposed RLS method along with a forgetting factor was an effective and robust method for estimating the parameters.

KW - Least-squares identification

KW - Output-error (OE) model

KW - Parameter estimation

KW - Recursive algorithms

KW - Recursive least-squares method

KW - Transcutaneous electrical nerve stimulation signal

KW - Engineering

KW - Least-squares identification

KW - Output-error (OE) model

KW - Parameter estimation

KW - Recursive algorithms

KW - Recursive least-squares method

KW - Transcutaneous electrical nerve stimulation signal

UR - http://www.scopus.com/inward/record.url?scp=84992507389&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2015.10.171

DO - 10.1016/j.ifacol.2015.10.171

M3 - Conference article in journal

VL - 48

SP - 389

EP - 394

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

IS - 20

ER -