# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 3 Experimental Evaluation
### Overall Assessment
#### 3.2.2 EMG Data Analysis  
Data were processed using MATLAB software (MATLAB 9.7.0, MathWorks, Natick, MA, USA). The raw EMG signals were band-pass filtered using a zero-lag third order Butterworth filter (20-450 Hz), rectified, and low-pass filtered with a zero-lag fourth order Butterworth filter (10 Hz). The time scale was normalized by interpolating individual gait cycles over 1200 points. Then, the EMG signal from each muscle was normalized to the MVC peak value across all trials. We considered 234 gait cycles for each session (with/without the exosuit).  
To investigate the effect of the exosuit on muscle fatigue, the co-activation, the root mean square (RMS) and the mean frequency (MF) of the power spectrum of the EMG signals were calculated. For each stride, the co-activation has been evaluated through the time varying multi muscle co-activation function (TMCf), which was assessed between the knee antagonist muscles using the following formula proposed by [100]:  
\[TMCf(d(i),i)=\left(1-\frac{1}{1+\exp(-12(d(i)-0.5))}\right)\frac{(\sum\limits_ {m=1}^{M}EMG_{m}(i)/M)^{2}}{max_{m=1\dots M[EMG_{m}(i)]}} \tag{7}\]  
where \(M\) is the number of considered muscles, \(EMG_{m}(i)\) is the \(i\)-th sample of the EMG value of the \(m\)th muscle, \(d(i)\) is the mean of the differences between muscles signals at sample \(i\):  
\[d(i)=\frac{\sum\limits_{m=1}^{M}\sum\limits_{n=m+1}^{M}|EMG_{m}(i)-EMG_{n}(i) |}{M!/(2!(M-2)!)} \tag{8}\]  
This method provides a sample-by-sample estimate of the relative activation of the pair of muscles. With the use of this equation, high co-contraction values represent a high level of activation of both muscles across a large time interval, while low co-contraction values indicate either low level activation of one muscle along with low-level activation of the other muscle in the pair [100]. To have a global measure of the coactivity level as a co-activation index (CI), the TMCf was then averaged over the entire stride cycle (\(i\) = 1:N, where N is the number of samples per stride, 1200 samples). For each stride, the RMS was computed over the interval ranging from 5% to 70% of the gait cycle for the VL muscle and from 60% to 100% of the stride cycle for the BF and over the entire gait cycle, according to the following formula:  
\[RMS=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}EMG_{i}^{2}} \tag{9}\]where \(EMG_{i}\) is the value of the i-th sample of the envelope of each muscle and N is the number of samples of each interval. For each muscle and each stride, the MF was computed as the ratio between the spectral moments of order 1 and 0 [101; 102]:  
\[MF=\frac{\int_{t_{1}}^{t_{2}}fPSD(f)\delta f}{\int_{t_{1}}^{t_{2}}PSD(f)\delta f} \tag{10}\]  
where \(t_{1}\) and \(t_{2}\) are the initial and final instants of each stride, \(PSD(f)\) is the power spectrum density of the EMG signal, and \(f\) is the frequency.  
The statistical analysis was performed using SPSS 20.0 software (IBM). \(p\)-values \(<\) 0.05 were considered statistically significant. We used the Shapiro-Wilk test [103] to verify that the data was from a normal distribution, then we applied a parametric paired \(t\)-test to detect any significant differences in the CI and in the RMS and MF for each muscle between an average of 230 strides for each test repetitions in the condition in which the subject was not wearing the exosuit and same number of strides was considered when the subject was wearing it. A priori power analysis using the G*Power computer program [104] indicated that a total sample of 54 strides would be enough to detect medium effects (\(d=0.5\)) with 95% power using a paired t-test between means with \(\alpha=0.05\).