## 6 Designed Architecture of the Smart Sensory Glove Based on AlN-Based Sensors  
This section presents a novel smart glove supported by an onboard ML algorithm to classify gestures by processing the signals acquired by the integrated sensors. The proposed solution relies on five ultra-thin and flexible AlN-based piezoelectric sensors placed at proper points on the back of the hand. Then, the design and testing of the electronic conditioning section for AlN-based flexible sensors are presented. Finally, the architecture of a wearable hybrid recognition system is shown, which combines images captured by a micro-camera with haptic data collected by the smart glove to identify the touched object. The piezoelectric sensors integrated into the smart glove are based on an aluminum nitride (AlN) thin layer, a biocompatible piezoelectric material with high resistivity and electromechanical response, well suited for applications on the human body. The transducer was made by superimposing several layers: an initial protective 25 \(\upmu\)m thick Kapton film, a flexible material resistant to high temperatures, and then a 120 nm thick AlN layer and a 200 nm thick molybdenum (Mo) electrode are placed by photolithography, and then two further patterned layers of 1\(\upmu\)m thick AlN and 200 nm thick Mo. A Parylene layer was applied both above and below to isolate the sensor's surface, leaving a part of the upper \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & \begin{tabular}{c} **N\({}^{\circ}\) of Sensors and** \\ **Type** \\ \end{tabular} & **Processing Unit** & \begin{tabular}{c} **Resulting Data/** \\ **Feedback** \\ \end{tabular} & \begin{tabular}{c} **Future Applications and** \\ **Improvements** \\ \end{tabular} \\ \hline B. [PERSON] et al. [110] & \begin{tabular}{c} (15) MPU9250 9-axis \\ inertial and magnetic \\ sensors \\ \end{tabular} & STM32F4 & \begin{tabular}{c} ELM-based \\ gestures \\ recognition \\ \end{tabular} & Robotic teleoperation based on the gesture recognition \\ \hline [PERSON]. [PERSON] et al. [117] & \begin{tabular}{c} (3) Pressure sensors \\ (3) Bending sensors \\ \end{tabular} & Atmega 328P & \begin{tabular}{c} Finger movements \\ detection \\ \end{tabular} & Clinical study testing \\ \hline [PERSON] et al. [119] & \begin{tabular}{c} (1) Pressure pads made \\ with silicone elastomer \\ (1) Coiled SMA wires \\ and Kapton tape for the \\ armband \\ \end{tabular} & Atmega 328P & \begin{tabular}{c} Mechanotactile \\ stimulation \\ \end{tabular} & \begin{tabular}{c} Testing on upper limb amputees \\ to assess the device in a real \\ application \\ \end{tabular} \\ \hline [PERSON] et al. [125] & \begin{tabular}{c} 8-layer jamming \\ mechanism, (1) Inertial \\ Measurement Unit (IMU) \\ \end{tabular} & Arduino Mega 2560 & \
\begin{tabular}{c} Stiffening of \\ jamming elements \\ \end{tabular} &
\begin{tabular}{c} Improve wearability; stiffness \\ control; optimize tremor \\ suppression using force \\ assessment \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summarizing table with the smart gloves previously analyzed and compared in terms of sensors’ typologies and number, processing unit, and future applications and improvements. surface free on which the electrical connections are to be applied. In this non-isolated area, a connection package made with 3D printing was applied, depositing conductive (CI) and dielectric (DI) ink. Figure 18 shows an exploded diagram of the obtained multilayer transducer. Such sensors have previously been integrated into other wearable systems to monitor swallowing in patients suffering from dysphagia [20], collect electrical energy from human body movements [21], or in combination with a triboelectric sensor, collect human joint movements [22]. The sensor was characterized using a specially calibrated setup to establish the piezoelectric response as the applied pressure varies.