## 6 Designed Architecture of the Smart Sensory Glove Based on AlN-Based Sensors
Specifically, the conditioning section comprises a preamplification stage and a filtering stage; the first aims to convert the input signal supplied by the piezoelectric sensor into a low-impedance signal source and limit the noise as possible (Figure 20). The latter carries out the same function and eliminates the undesired signal components. These operations are performed in parallel by a conditioning board including five identical conditioning channels, one for each piezoelectric sensor. The design and testing of this conditioning section are presented in Section 6.1. The conditioned signals from the five sensors are acquired by the 12-bit analog-to-digital-converter (ADC) integrated into the nRF52840 System on Chip (SoC) (Figure 20). It is a very low-power system-on-chip (SoC) that integrates a multi-protocol 2.4 GHz transceiver and a powerful CPU with Arm(r) Cortex(r)-M4F architecture, equipped with a 1 MB flash program memory. The chip is the ideal SoC for developing IPv6-compatible automation applications or short-range wireless personal area networks. Furthermore, the device comprises a 12-bit ADC, multiplexed on eight configurable channels, operating with a sampling frequency of up to 200k sps (samples per second). Afterward, the acquired data were employed to infer the gestures carried out by the user. For this aim, the _Edge Impulse(r)_ platform was used, an integrated development environment for developing machine-learning models. It manages the acquisition and processing of data easily and quickly; in fact, it conveniently imports sensor, audio, and camera data and seamlessly distributes them to embedded systems, GPUs, and custom accelerators. The platform allows using data from any sensor to extract meaningful information for recognizing sounds or keywords, as well as testing the created model with real data to detect bugs early in the life cycle of the newly trained model [128]. The device software, including SDK, client, and generated code, is provided in open-source mode with an Apache 2.0 license. The collaboration with the TensorFlow Lite Micro project enables supporting a wide range of architectures, operators, and targets. The feature extraction is performed for the designed model with a 2 s time window size and a 0.2 s overlap. In particular, a spectral analysis is chosen for the considered application, which extracts the frequency and power features of the signal [128] (Figure 21). Low-pass and high-pass filters can also be applied to eliminate unwanted frequencies. It is suitable for analyzing repetitive patterns such as those in motion or vibration signals provided by inertial sensors [128]. The resulting spectral features are sent in input to a neural network comprising an input layer with 55 nodes (55 features), two hidden layers constituted by 20 and 10 neurons, and 6 output nodes, representing six classes related to six gestures associated with gestures indicating the numbers from 0 to 5 (Figure 19c). The graphic representation of the designed inferring chain is reported in Figure 21.  
Thanks to the communication capability of the selected microcontroller unit, the developed smart glove can wirelessly send indications related to the system status and recognized classes to a remote host device (Figure 20). Finally, the 3D model of the designed smart glove is reported in Figure 22. As shown, the five AlN-based piezoelectric sensors are placed in correspondence to the junction between the first and second phalanx. Additionally, electronic conditioning, acquisition and processing sections are installed on the hand's upper face, along with the 500 mAh LiPo battery to supply the smart glove. Figure 21: Graphical representation of the designed inferring chain for gesture recognition. Figure 22: Three-dimensional model of the developed piezoelectric glove with highlighted the main components: AlN flexible sensors, conditioning board, acquisition and processing board, and Lipo battery.