**5. Extraction of muscle synergies**

Linear decomposition of multi-source EMG signal is another method to diagnose the alteration in EMG patterns of patients with gait disorders [5, 42]. The muscle synergy hypothesis can be employed to understand better the physiological aspects of gait disorders using a number of linear decomposition algorithms such

**25**

**Figure 3.**

*filter for 9-channel EMG data.*

10 than 20 Hz.

*A Review of EMG Techniques for Detection of Gait Disorders*

as principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), and non-negative matrix factorization algorithm (NNMF). Each algorithm is unique and extracts the synergy structure based on the assumption made on the synergy (e.g. orthogonality, non-negativity, statistical independence, etc.). After applying the factorization algorithm, the multi-electrode EMG signal is decomposed into the activation coefficients and synergies. The synergy vectors from the healthy group can be compared with a group suffering from the neurological or non-neurological disorder [43]. Statistical tests including cosine correlation, Pearson correlation or cluster analysis are generally used to compare the similarity and alterations in synergy structures [44, 45]. The application of a clustering algorithm for diagnosing gait disorder is discussed in a later section. Patients with thoracic spinal cord injury revealed lesser modules, higher cocontraction and, less directional tuning in relation to healthy individuals [46]. It is likely that the number of dimensional space was affected due to the choice of preprocessing [47]. A review cum research by Kieliba et al. [47] supported that increase in the cut off frequency of the filter decreases the variance, accounts for a particular component and increases dimensional space of synergies to be extracted. EMG acquired from children with cerebral palsy and from individual's post-stroke has shown that the choice of preprocessing (filtering, normalization) had an effect on the number of synergies and differentiation of physiological traits [48, 49]. **Figure 3** displays how the choice of low pass filter (10 and 20 Hz), a second-order Butterworth filter, effects the dimensional space. Filters are generally used to remove movement artifact. The principal component variance is higher for

From a neurophysiological perspective, the recruitment of fewer spinal modules

during movement is due to the loss of supraspinal inflow that results in simple muscle coordination (neuroadaptation). In upper extremities, the neuroadaptation was similarly perceived in the form of changes in the dimensional space of muscle synergy structures. Alteration of synergy structures was also present in patients with chronic stroke (upper extremity), and cerebral palsy [42, 43, 45, 50]. The linear envelopes extracted from the EMG data are subjected to MS extraction. The synergy hypothesis is well suited for capturing the physiological aspects of motor

*A variance threshold* ≥*0.9 reveals five synergies for 10 Hz low pass filter and four synergies for 20 Hz low pass* 

*DOI: http://dx.doi.org/10.5772/intechopen.84403*
