# Activity Recognition Using Different Sensor Modalities and Deep Learning
## 4 Discussion and Conclusions  
In this paper, we have designed a wireless smart insole. It is a low-cost, lightweight, and compact solution. Our design has no visible wires as in many other studies [36; 37; 38]. The advantage of this design is to protect both the devices and users from damage while wearing and removing from the human body. Moreover, it provides walking comfort with memory foam soles for users. The number of commercial smart insole systems is increasing. Some of the most well-known are F-Scan [39] and the Pedar Sensole System [40]. These systems have high resolution, but they are expensive and used mainly for clinical diagnostics and research purposes. Motion [41] and Orpyx [42] have low resolution due to a low number of sensors. Although these insoles are relatively cheaper than those with high resolution, they are not affordable enough to appeal to the masses. This study focused on the low-cost development of a designed smart insole system for research requiring mass participation. There are many studies on activity monitoring. The most popular one is IMU sensor-based monitoring due to its low cost and easy placement in any desired part of the human body. However, the user needs to carry the device on the body. This may cause discomfort \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Foot Contact States** & **Accelerometer and Gyroscope** & **All Data** \\ \hline \(F_{score}\) & 0.947 & 0.939 & 0.958 \\ \hline \hline \end{tabular}
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Table 4: \(F_{score}\) Comparison of the network performances for each input category \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Target Class**} \\  & & WG & **WU** & **WD** & **ASC** & **DES** & **RN** & **JP** & **SS** & **Precision** \\ \hline \multirow{8}{*}{Actual} & **WG** & 86.40\% & 4.50\% & 2.90\% & 2.80\% & 3.30\% & 1.30\% & 0.90\% & 0.60\% & 84.10\% \\  & **WD** & 4.60\% & 85.40\% & 4.60\% & 2.90\% & 2.80\% & 1.80\% & 0.60\% & 0.40\% & 82.80\% \\  & **WD** & 3.70\% & 2.90\% & 87.40\% & 4.10\% & 3.20\% & 1.70\% & 1.20\% & 1.30\% & 82.80\% \\  & **ASC** & 2.90\% & 3.60\% & 2.10\% & 85.00\% & 4.20\% & 2.00\% & 1.00\% & 1.30\% & 83.10\% \\ Class & **DES** & 2.20\% & 3.20\% & 2.50\% & 4.70\% & 85.80\% & 2.10\% & 0.20\% & 1.20\% & 84.20\% \\  & **RN** & 2.00\% & 3.00\% & 0.20\% & 0.10\% & 0.50\% & 1.90\% & 0.50\% & 1.90\% & 0.50\% & 96.10\% \\  & **IF** & 0.00\% & 0.10\% & 0.20\% & 0.00\% & 0.00\% & 0.30\% & 93.00\% & 1.20\% & 98.10\% \\  & **S** & 0.00\% & 0.00\% & 0.10\% & 0.40\% & 0.20\% & 0.10\% & 1.20\% & 93.50\% & 97.90\% \\  & **Recall** & 86.40\% & 85.40\% & 87.40\% & 85.00\% & 85.80\% & 90.50\% & 93.00\% & 93.50\% & \\ \hline \hline \end{tabular}
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Table 2: The classification performance of ConvLSTM NN when the input set is the accelerometer and gyroscope outputs. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Target Class**} \\  & & **WG** & **WU** & **WD** & **ASC** & **DES** & **RN** & **JP** & **SS** & **Precision** \\ \hline \multirow{8}{*}{Actual} & **WG** & 93.00\% & 0.90\% & 2.30\% & 1.00\% & 2.30\% & 0.20\% & 0.90\% & 0.10\% & 92.40\% \\  & **WD** & 1.30\% & 94.20\% & 1.90\% & 3.10\% & 1.70\% & 0.90\% & 1.20\% & 0.40\% & 90.00\% \\  & **WD** & 1.40\% & 0.70\% & 92.30\% & 2.70\% & 2.40\% & 1.00\% & 1.10\% & 1.10\% & 89.90\% \\  & **ASC** & 1.70\% & 2.80\% & 2.70\% & 91.40\% & 2.60\% & 1.40\% & 1.30\% & 0.20\% & 87.80\% \\  & **DES** & 1.80\% & 0.70\% & 0.30\% & 0.80\% & 89.10\% & 1.10\% & 0.40\% & 0.50\% & 94.10\% \\  & **RN** & 0.50\% & 0.30\% & 0.10\% & 0.20\% & 0.50\% & 95.00\% & 0.50\% & 0.50\% & 97.30\% \\  & **IP** & 3.00\% & 0.10\% & 0.30\% & 0.60\% & 0.70\% & 0.20\% & 94.00\% & 0.20\% & 97.50\% \\  & **SS** & 0.00\% & 0.30\% & 0.10\% & 0.20\% & 0.70\% & 0.20\% & 0.60\% & 97.00\% & 97.90\% \\  & **Recall** & 93.00\% & 94.20\% & 92.30\% & 91.40\% & 89.10\% & 95.00\% & 94.00\% & 97.00\% & \\ \hline \hline \end{tabular}
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