Multi-step prediction and filtering of pathological tremor for FES applications

Sivanagaraja Tatinati; Kalyana C. Veluvolu; Wei Tech Ang

Multi-step prediction and filtering of pathological tremor for FES applications

Sivanagaraja Tatinati, Kalyana C. Veluvolu, Wei Tech Ang

School of Electronics Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, a machine learning technique (LS-SVM) was employed for filtering and multistep prediction of pathological tremor to overcome the delay associated with the prefiltering and electromechanical delay in FES applications. To validate the proposed approach, a study was conducted on the pathological tremor data collected form eight patients. Results show that LS-SVM provides better prediction accuracy compared to the earlier methods.

Original language	English
State	Published - 2013
Event	7th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2013, in Conjunction with SENDEX 2013 - Gyeonggi-do, Korea, Republic of Duration: 29 Aug 2013 → 31 Aug 2013

Conference

Conference	7th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2013, in Conjunction with SENDEX 2013
Country/Territory	Korea, Republic of
City	Gyeonggi-do
Period	29/08/13 → 31/08/13

Cite this

@conference{fd495285b9a14c26888acd647d540532,

title = "Multi-step prediction and filtering of pathological tremor for FES applications",

abstract = "In this paper, a machine learning technique (LS-SVM) was employed for filtering and multistep prediction of pathological tremor to overcome the delay associated with the prefiltering and electromechanical delay in FES applications. To validate the proposed approach, a study was conducted on the pathological tremor data collected form eight patients. Results show that LS-SVM provides better prediction accuracy compared to the earlier methods.",

author = "Sivanagaraja Tatinati and Veluvolu, \{Kalyana C.\} and Ang, \{Wei Tech\}",

year = "2013",

language = "English",

note = "7th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2013, in Conjunction with SENDEX 2013 ; Conference date: 29-08-2013 Through 31-08-2013",

}

Multi-step prediction and filtering of pathological tremor for FES applications. / Tatinati, Sivanagaraja; Veluvolu, Kalyana C.; Ang, Wei Tech.
2013. Paper presented at 7th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2013, in Conjunction with SENDEX 2013, Gyeonggi-do, Korea, Republic of.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Multi-step prediction and filtering of pathological tremor for FES applications

AU - Tatinati, Sivanagaraja

AU - Veluvolu, Kalyana C.

AU - Ang, Wei Tech

PY - 2013

Y1 - 2013

N2 - In this paper, a machine learning technique (LS-SVM) was employed for filtering and multistep prediction of pathological tremor to overcome the delay associated with the prefiltering and electromechanical delay in FES applications. To validate the proposed approach, a study was conducted on the pathological tremor data collected form eight patients. Results show that LS-SVM provides better prediction accuracy compared to the earlier methods.

AB - In this paper, a machine learning technique (LS-SVM) was employed for filtering and multistep prediction of pathological tremor to overcome the delay associated with the prefiltering and electromechanical delay in FES applications. To validate the proposed approach, a study was conducted on the pathological tremor data collected form eight patients. Results show that LS-SVM provides better prediction accuracy compared to the earlier methods.

UR - https://www.scopus.com/pages/publications/84894559489

M3 - Paper

AN - SCOPUS:84894559489

T2 - 7th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2013, in Conjunction with SENDEX 2013

Y2 - 29 August 2013 through 31 August 2013

ER -

Multi-step prediction and filtering of pathological tremor for FES applications

Abstract

Conference

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