Title: Prediction of sagittal lower limb joints moments under dynamic condition: feasibility of using EMG and ARMA model identification techniques

Authors: Amjed S. Al-Fahoum; Khaled H. Gharaibeh

Addresses: Biomedical Systems and Informatics Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan ' Procurement Department, Dulb Trading & Contracting, Sultan Bin Salman Bin Abdulaziz St. 1, Riyadh 11663, KSA

Abstract: A novel alternative method reducing the need for direct inverse dynamics to solve the muscle redundancy problem at human lower limbs is proposed. It aims at computing lower limb joints moments under dynamic conditions using only electromyographic (EMG) signals in combination with an auto regressive moving average (ARMA) model. The experimental protocol is conducted by practicing full gait cycle trials in an effort to calculate joint moments. Quantitative comparisons with the output of a biological-based model showed that the proposed method is able to: 1) produce accurate estimates of the resultant moment; 2) maintain the obtained accuracy regardless of the information about status of the angle or its derivatives. The joint moment prediction by the ARMA model attained an average of R² = 1.73. The model is characterised by stability, accuracy and minimum number of input variables. These characteristics represent an added value to be utilised in lower limbs rehabilitation.

Keywords: electromyography; EMG signals; inverse dynamics; gait cycle; auto regressive moving average; ARMA; non-stationary signals; lower limbs; joint moments; model identification; lower limb rehabilitation.

DOI: 10.1504/IJECB.2014.060402

International Journal of Experimental and Computational Biomechanics, 2014 Vol.2 No.3, pp.245 - 264

Received: 27 Apr 2013
Accepted: 21 Oct 2013
Published online: 02 Jul 2014 *

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