# A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics
## 2 Materials and Methods  
This work proposes a wearable monitoring system for the assessment of gait biomechanics. Objective evaluation if performed in terms of correlation of the plantar pressure distribution along the footprint, recorded along the COP progression line, and lower-limb EMG. The block diagram of the proposed solution is illustrated in Figure 6 and is explained as follows. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Muscles** & n.a. & n.a. & n.a. \\ \hline
**No of Plantar Sensors** & 19 & 29 passive reflective markers & Seventeen 14 mm diameter retro-reflective markers were attached to the skin on each leg \\ \hline
**Plantar Data Regions** & 3 segment foot model (hindfoot, forefoot and hallux) + tibial segment & 4: shank, hindfoot, forefoot and hallux segments & n.a. \\ \hline
**Hardware Used for Data Collection** & n.a. & New Balance neutral (cushioning shoe (model 840) with circular holes cut out with a diameter of 2.5–2.7 cm & n.a. \\ \hline
**No. of Participants** & 2 & 12 & 10 (4 males, and 6 females) \\ \hline
**Reference** & Ref. [30] presents the evaluation of foot kinematics during barefoot walking based on a multi-segment foot model (12 foot parameters are evaluated) & Ref. [31] investigates the between-day reliability and within-session variability of the Oxford Foot Model (OFM) while walking in a neutral cushioning shoe & Ref. [34] a protocol for measuring the kinematics of the foot and ankle (12 foot parameters are evaluated) \\ \hline
**Results and Accuracy** & No numerical values available, only graphs & \\ \hline
**Muscles** & n.a. & \\ \hline
**No of Plantar Sensors** & 21 six mm retroreflective markers & \\ \hline
**Plantar Data Regions** & number of foot segments that comprise the foot for each model varies (all 5 models, however, contain equivalent hindfoot, forefoot, and hallux segments) & \\ \hline
**Hardware Used for Data Collection** & n.a. & \\ \hline
**No. of Participants** & 10 (6 males, and 4 females) & \\ \hline
**Reference** & Ref. [35] compares the repeatability and kinematics of five different three-dimensional multi-segmented foot models & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 6: Block diagram of the proposed wearable foot biomechanics monitoring system. An ATmega328P microcontroller (\(\upmu\)C), deployed on an Arduino Nano board, is employed to read six Aidong IMS C20B thin-film resistive sensors deployed onto a shoe insole. Next, the \(\upmu\)C performs the acquisition of two EMG channels via off the shelf Mikroelektronika EMG Click analog front ends (AFE). Signal acquisition is performed with a \(f_{s\_FSR}\) = 100 Hz sampling frequency for the plantar pressures and a \(f_{s\_EMG}\) = 1 kHz sampling frequency for the EMG channels respectively. A synchronized sampling method was adopted, having one analog input channel for each targeted signal. Resolution of the acquired signals is given by the 10-bit analog to digital converter (ADC). The wearable foot biomechanics monitoring system laboratory proof of concept is illustrated in Figure 7. The electronics, i.e., Arduino Nano board with ATmega328P \(\upmu\)C, pressure sensor circuit breadboard and two EMG acquisition AFEs, were attached onto a Velcro strip, as illustrated in Figure 7a, to enable wearing on the lower limb, as illustrated in Figure 7b. The pressure sensor and EMG signal acquisition and processing follows the logical diagram illustrated in Figure 8, aiming for the identification of physiological gait. As illustrated, the first stage is the acquisition of plantar pressures and lower-limb EMG signals. At this stage, it is possible to visualize the recorded signals in time-domain using the Arduino Studio Plotter or Monitor. From this point on, processing is twofold. On one hand, EMG signal processing accounts for feature extraction, activity discrimination and extraction of muscular activity.