**4. Discussion**

The present work analyzed sEMG in a population of 20 PD patients for the assessment of GM an TA activation patterns during gait using a statistical approach for the processing of a large number of strides.

Previous studies [25–29,40] evaluating muscle activation in PD patients reported non-concordant results probably due to the limited (from few units to a couple of tens) number of gait cycles per subject, indicating a single reference activation pattern for each muscle.

The relatively recent application of a statistical approach for the analysis of sEMG from long gait sequences (i.e., multiple decades of gait cycles) highlighted that different activation patterns can occur even in healthy gait [4,12–16] and in hemiplegic children [32], as preliminary application to pathological conditions. In the present study, the application of a statistical approach was applied in PD patients to long gait sequences from a 6-min walking test (i.e., in the order of multiple decades of gait cycles) to identify the different possible gait patterns.

Approaching the analysis of gait in pathology, specific conditions have to be addressed. Previous studies that applied statistical EMG analysis to the gait of healthy subjects did not tackle the issue of gait symmetry, as in healthy subjects no difference between left and right sides is reasonably considered [15,16]. Considering the pathological population under investigation in the present study, this hypothesis must be verified exploiting an approach coherent with the further statistical analysis on the on/off timing activation pattern. In the present study, overall symmetry was investigated using stride time as a discrete temporal indicator in accordance with previous studies [20].

The application of the statistical approach [38] to sEMG in the analyzed PD population after gait symmetry assessment allowed us to identify different activation patterns for both GM and TA, supporting the existence of an intrinsic variability in EMG activation schemes even in a paradigmatic motor task as gait, as already observed in healthy subjects [10,12,17] and hemiplegic children [32]. In more detail, 5 activation patterns were identified by the computational procedure for both muscles, although, based on the quantification of occurrence frequency, only four can be considered significant for GM (i.e., the 5-activation pattern excluded having an occurrence frequency below 3%), and four for TA (i.e., the 1-activation pattern excluded due to occurrence frequency below 4% and in a minority of subjects), although the TA 5-activation pattern presented a low but significant occurrence frequency (in the order of 10%), resulted too variable in timing to allow a systematic characterization.

As already observed in healthy subjects, GM activity was centered between flat foot contact and push o ff, and in the final swing: even if shorter and delayed, the first activation is also found in PD, highlighting the active participation in stabilizing ankle dorsiflexion during the forward progression [3,11]; the second region of activity mainly occurred during the early- and mid-swing, anticipating the activation timing observed in healthy adults [12]. When no statistical approach was applied and only the triceps surae mean single timing pattern was estimated, this second region of activity was not observed [31]. For TA, the muscular activity of healthy subjects resulted to be centered from the pre-swing to the following loading response and during the mid-stance [12]. In the analyzed PD subjects, the activity at the beginning of the gait cycle was absent or delayed, showing alterations that normally contribute to foot positioning at the touch down [27]. Moreover, the activations during the swing phase were generally shorter and anticipated, showing a compensation of the anticipation in the activation of GM during the swing phase [27]. These di fferences in the TA activation pattern were not observed previously with the mean single pattern approach [31].

The coactivation analysis of the two antagonist muscles, GM vs TA, was applied to understand the mechanisms of regulation of joint sti ffness and stability [41,42]. To the authors' knowledge, only one study assessed the coactivation of antagonist muscles using statistical approaches, investigating GA and quadriceps femoris during gait of healthy adults [15]. Following the same methodological approach [15], the co-contraction between GM and TA was quantified as the overlapping of activation intervals. In the analyzed PD subjects, results showed two or three GM-TA co-activation intervals within the single gait cycle (Figure 5), confirming the maintained reciprocity of antagonist nature of considered muscles [27]: the first coactivation lasted from heel strike to mid-stance and can be associated to ankle stabilization during the early stance [43]; the second activation occurred from the pre—to initial swing, when TA contributes to rapid dorsi-flexion of the foot during swing phase [44], while GM activity is commonly related to the plantar-flexion needed for the heel raising [45]; the third and last coactivation occurred during late swing, when TA acts as ankle dorsi-flexor and GM as foot-invertor [12], to properly positioning and stabilizing the foot for the following foot contact [11,46]. First and second coactivations occurred for every GM and TA n-activation pattern, while the third coactivation did not occur for the GM 1-activation pattern, independently from the associated TA n-activation pattern.

Due to the number of analyzed PD subjects and their specific characteristics (e.g., severity and ON state of Levodopa therapy), the results of the present study cannot be generalized to PD population; nevertheless, the exploited statistical approach, applied to a large number of strides acquired in ecological conditions, demonstrated the capability to also identify di fferent activation patterns in the target pathologic population, allowing to quantify the occurrence frequency of each, analyze activation timing distribution per activation pattern, and coactivation as related to specific activation patterns, supporting the methodological aim of the present study.

The use of a statistical approach to identify activation patterns from sEMG in the selected pathologic population supported a better insight in the analysis of muscle activation and control, and can be used for monitoring and understanding how muscle activity changes with the development of the pathology. Moreover, it provided quantitative parameters to support further analysis (e.g., occurrence frequency of each n-activation pattern, presence/absence of a certain muscle activity in a specific gait phase) that can also be exploited to size sample and gait duration or can be included, together with spatio-temporal parameters, in the statistical methods/models for the general analysis of gait in PD patients [47–49].

In addition, the approach allows for the estimate of SNR on the signal, and the constraint on SNR allows us to guarantee the quality of the analyzed data and of the resulting outcome, although the constraint can lead to the discarding of part of the acquired data, as not all gait cycles can be included in the analysis (Table 1). Therefore, for the application of statistical approach, not only do long sequences of gait have to be acquired, but also grea<sup>t</sup> care must be paid during the experimental procedure (e.g., skin preparation, electrode placement etc.) to minimize the loss of acquired data. The verification of symmetry, as in the present study, allows us to increment the statistical power of the data-set, computing data from the right and left side together, nevertheless, this verification is not guaranteed in pathologic subjects and specific inspection must be previously performed.
