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

Research output: Journal contributionsConference article in journalResearchpeer-review

Authors

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.

Original languageEnglish
JournalIFAC-PapersOnLine
Volume48
Issue number20
Pages (from-to)389-394
Number of pages6
ISSN2405-8971
DOIs
Publication statusPublished - 01.09.2015

    Research areas

  • Engineering - Least-squares identification, Output-error (OE) model, Parameter estimation, Recursive algorithms, Recursive least-squares method, Transcutaneous electrical nerve stimulation signal

Recently viewed

Publications

  1. A Lyapunov based PI controller with an anti-windup scheme for a purification process of potable water
  2. Age effects on controlling tools with sensorimotor transformations
  3. A Study on the Performance of Adaptive Neural Networks for Haze Reduction with a Focus on Precision
  4. Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
  5. A PHENOMENOGRAPHICAL STUDY OF CHILDRENS’ SPATIAL THOUGHT WHILE USING MAPS IN REAL SPACES
  6. Practice and carryover effects when using small interaction devices
  7. Machine Learning and Knowledge Discovery in Databases
  8. Evaluating structural and compositional canopy characteristics to predict the light-demand signature of the forest understorey in mixed, semi-natural temperate forests
  9. A cascade controller structure using an internal PID controller for a hybrid piezo-hydraulic actuator in camless internal combustion engines
  10. Continuous and Discrete Concepts for Detecting Transport Barriers in the Planar Circular Restricted Three Body Problem
  11. A Hermeneutic Interpretation of Concepts in a Cooperative Multicultural Working Project
  12. Neural relational inference for disaster multimedia retrieval
  13. The relationship between audit committees, external auditors, and internal control systems
  14. Automatic feature selection for anomaly detection
  15. Can measurement errors explain variance in the relationship between muscle- and tendon stiffness and range of motion?—a blinded reliability and objectivity study
  16. Performance incentives in activity-based management
  17. Detection of coherent oceanic structures via transfer operators
  18. Challenges and boundaries in implementing social return on investment
  19. Determination of 10 particle-associated multiclass polar and semi-polar pesticides from small streams using accelerated solvent extraction
  20. Cascade PID Controllers Applied on Level and Flow Systems in a SMAR Didactic Plant
  21. Use of Machine-Learning Algorithms Based on Text, Audio and Video Data in the Prediction of Anxiety and Post-Traumatic Stress in General and Clinical Populations