A new fractal-based kinetic index to characterize gait deficits with application in stroke survivor functional mobility assessment

Highlights

• A new kinetic index (K.I.) is proposed to characterize gait deficits.

• K.I. has strong correlation with the Time Up and Go (TUG) test score.

• K.I. could classify stroke survivors into different homogeneous subgroups.

• Implications of the proposed K.I. on clinical assessment are discussed.

Abstract

This paper proposes a new Kinetic Index (K.I.) to characterize the gait deficits in stroke survivors. The index is derived from the fractal properties of surface electromyography (sEMG) signals. The objectives of proposing this K.I. are (i) to find the correlation between sEMG fractal properties with TUG test; (ii) to classify stroke survivors into different homogeneous subgroups based on K.I., and (iii) to compare the classification results based on published methods. To achieve these objectives, 30 stroke survivors with different levels of gait impairments were recruited to perform TUG. sEMG signals from Tibialis Anterior (TA) and Gastrocnemius Lateral (GL) were acquired in a 5-meter walk test. Sliding window Higuchi fractal dimension algorithm was applied to sEMG of these TA and GL muscles to determine the fractal properties. Hierarchical cluster analysis was used to classify stroke survivors into different subgroups with (i) conventional multiple category of gait parameters (Approach 1), and (ii) single input by using the proposed K.I. value (Approach 2). Besides that, classification based on stroke survivors TUG score was also applied. Results showed that K.I. has strong correlation with the TUG score. A higher value in K.I. associates with higher TUG score. This suggests K.I. could quantify gait deficits and detect risk of fall in this population. The classification results from the Approach 1 were similar to previous published studies. The gait parameters from Approach 2 showed similar gait patterns to Approach 1. Meanwhile, gait results from classification based on TUG score yielded heterogeneous subgroups. These results suggested that K.I. was able to assess gait severity among stroke survivors and was more efficient (it requires a single input parameter only) to classify stroke survivors into homogeneous subgroups.

Introduction

Surface electromyography (sEMG) signal records the electrical activities of skeletal muscles. It infers muscle function that generates body movement [1]. sEMG has been widely used by researchers and clinicians to perform gait analysis [[2], [3], [4]]. It could be characterized by techniques involving time and frequency domain analysis. For example, root mean square value (RMS), zero-crossing (ZC) rate, median frequency (MDF), mean frequency (MNF), determine muscle fatigue and muscle energy expenditure. These methods reveal specific properties in the linear system context. However, sEMG signal is nonlinear in nature [5]. Rodrick and Karwowski observed positive Lyapunov exponents existed in sEMG of the biceps muscle in some work postures. These suggested chaotic-liked behaviors [6]. Ouyang et al. [7] revealed the characteristics of sEMG during different hand movements using recurrence plots. Besides that, fractal analysis is another common approach to identify nonlinear characteristics of sEMG signals.

Fractal dimension (FD) measures self-affine and dominant complexity of a signal [8]. Detrended fluctuation analysis (DFA) [9], correlation dimension, Katz method [10], box counting method, Higuchi fractal dimension (HFD) [11] and bi-phase power spectrum [12] are common methods to estimate FD of a time series. These techniques have been widely applied to correlate the sEMG FD and its interference patterns [12,13]. Besides that, fractal analysis is also commonly applied in sEMG signal classification [[14], [15], [16]]. In a recent study, FD of rectus femoris muscle sEMG was strongly correlated to the height of vertical jump [17]. Besides that, FD was used to estimate the contraction force from different muscles [18]. In gait analysis, Beretta-Piccoli et al. [19] extracted FD from the quadriceps femoris muscle sEMG to scrutinize fatigue. Boccia et al. [20] correlated the rate of change of FD from vastus lateralis and medialis muscles sEMG to fatigue contraction.

While there are extensive reports on sEMG fractal analysis, its applications in patients with neurological disorders are relatively limited. This could be attributed to the requirement of analyzing long time-series [21,22]. Nevertheless, this technique shows great potential as a quantitative gait assessment tool for neurological pathologies [22]. FD of a time series could serve as a normalized indicator since its value varies between 1 (line) and 2 (plane). Comparatively, raw sEMG signal would have large variation in amplitude across the stroke survivors. Therefore, it requires normalization with maximum voluntary contraction (MVC) which cannot be reliably established in individuals with neurological disorders.

Timed Up and Go test (TUG) is a simple test to evaluate the basic mobility, balance and locomotor skills of elderly and patients with neurological disorders such as stroke. The procedure of TUG test can be studied in [23]. TUG test is often used to monitor the recovery progress of disabled patients after certain intervention [[24], [25], [26]]. Besides that, TUG test can also be applied to classify stroke from healthy [27], type of amputation of lower limbs [28], type of walking aid [29] etc. This forms the motivation of current research work. We aim to investigate the feasibility of applying fractal analysis on sEMG signals from stroke survivors to characterize their gait deficits and to classify the gait deficits based on their TUG score.

Gait classification among stroke is to identify homogeneous subgroups of stroke survivors, which could enable physiotherapist to deliver treatment that is more effective during rehabilitation. This is particularly important to those researchers who do not have full access to collect necessary gait data; such a method would also facilitate communication between clinicians [30]. Besides that, proper classification can help to organize and manage large amounts of complex gait data. These gait data were generated by instrumented gait analysis such as kinematic and EMG data [31]. Many authors have attempted to identify homogeneous subgroup of gait pattern among stroke survivors using methods such as cluster analysis [32,33] and artificial neural network [30,34]. However, most of these methods require multiple inputs, which are very subjective and generally based on observation by visual inspection from researchers or clinicians [33]. Meanwhile, single category of parameter often yielded functionally heterogeneous results [33]. Therefore, it is worth applying classification methods to classify the fractal features mentioned earlier and compared it to conventional classification results.

The objectives of this study are: (i) to formulate a new kinetic index using the fractal features from sEMG of different muscles and to correlate to TUG score; (ii) to classify the gait pattern of stroke subjects into homogenous subgroup using different approaches; and (iii) to compare the classification results. To achieve these objectives, 30 strokes survivors with different walking functionalities were recruited in this experimental study. The subject’s sEMG from different muscles were analyzed.