# A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics
## 2 Materials and Methods
### Lower-limb EMG Assessment  
Electrode placement for the left-leg lower-limb EMG monitoring is depicted in Figure 10 for the Tibialis anterior with the reference electrode placed above the lateral malleolus, and in Figure 10 for the gastrocnemius with the reference electrode placed above the medial malleolus. Acquisition of an EMG channel was performed with a \(f_{\_}{s\_EMG}\) = 1 kHz sampling frequency using Ag/AgCl wet electrodes and the Mikroelektronika EMG Click board. As specified in the AFE datasheet, signal acquisition follows two on-board gain stages, i.e., instrumentation amplifier and variable gain amplifier, implemented with a MPC609 operational amplifier and MAX6106 micropower voltage reference, and three analog filter stages, i.e., high-pass filter with 1.6 Hz, and 0.16 Hz cutoff frequencies respectively and lowpass filter with 60 Hz cutoff frequency [40]. The EMG signal is nominally in the 1 \(\mu\)V-10 mV amplitude range [41; 42], with signal amplitudes accounting as follows. The motor unit action potential (MUAP) of a healthy  
Figure 10: Electrode placement for lower-limb EMG acquisition of the (a) tibialis anterior, (b) gastrocnemius. muscle ranges around 2 mV in amplitude, primary muscular disease accounts for MUAPs around 0.5 mV, whereas re-innervation and intramuscular sprouting of chronic partial denervation results in MUAPs larger than 10 mV [43]. As such, the AFE gain of the EMG acquisition board was set to 10x such as to have the EMG signal in the \(+/-50\) mV amplitude range prior to ADC. After acquisition, EMG feature extraction was aimed towards the determination of muscular activity and muscle activation patterns. Information regarding muscular activity is retained in the lower frequency range below 50 Hz. Time-domain features regarding the muscle activation pattern on the other hand are available in the 500 Hz frequency band [44; 45; 46]. For this purpose, the EMG signal is applied to a bank of two parallel filter as illustrated in Figure 11. However, it should be noticed that 150 Hz lowpass filtering was considered for the muscle activation pattern as we were interested in the identification of the muscle activation pattern rather than its assessment. After filtering, segmentation of the ECG signal is performed with 128-sample non-overlapping adjacent rectangular windows [47]. Further on, the features of interest in this work are presented. Features of muscular activation are extracted from the ECG signal limited to the 50 Hz bandwidth in terms of the zero crossing (ZC) and slope sign changes (SSC) count defined as:  
\[ZC=\sum_{i=2}^{N}(sgn(x_{i-1}\cdot x_{i})=-1), \tag{2}\]  
and  
\[SSC=\sum_{i=3}^{N}(sgn((x_{i-1}-x_{i-2})\cdot(x_{i}-x_{i-1}))=-1), \tag{3}\]  
where \(N\) is the segment length. The ZC expressed by (2) is a measure of the signal sign changes, namely the number of times the signal crosses the zero axis. As such, a large ZC count accounts for small signal swing, i.e., low muscular activity, whereas a small ZC count accounts for large signal swing, i.e., high muscular activity. Similarly, the SSC expressed by (3) is a measure of the local extrema, namely the number of changes from a positive slope to a negative slope (local maxima), or from a negative slope to a positive slope (local minima). As such, a large SSC count accounts for small signal swing, i.e., low muscular activity, whereas a small SSC count accounts for large signal swing, i.e., high muscular activity. Both ZC and SSC can be accounted for as an indicator for the onset of movement [48; 49]. Features of the muscle activation pattern are extracted from the ECG signal limited to the 150 Hz bandwidth in terms of the mean absolute value (MAV) and root mean square (RMS) defined as:  
Figure 11: Block diagram of the EMG feature extraction procedure assuming two parallel lowpass filters: 50 Hz lowpass filter for muscular activity and 150 Hz for the muscle activation pattern.