## 6 Designed Architecture of the Smart Sensory Glove Based on AlN-Based Sensors
### Architecture of a Hybrid Recognition System Based on the Developed Smart Glove  
Furthermore, the designed smart glove could be combined with a wearable vision system to obtain a hybrid recognition system useful to assist visually impaired patients [136]. Specifically, the system integrates a micro-camera (model HD 500 W OTG, manufactured by GesiousSpy Inc., Shenzhen, China) mounted on the user's glasses (Figure 32). The camera is characterized by a 2592 \(\times\) 1944 pixel resolution, 30 FPS frame rate, and small dimensions (15 mm \(\times\) 15 mm \(\times\) 13 mm), providing the pictures by a common USB 2.0 (Universal Serial Bus) interface. The system combines the visual data acquired by the micro-camera with those acquired by the smart glove to improve the performance of the systems in recognizing objects. Notably, the system core is a Raspberry Pi4 (8 GB RAM version), which collects pictures from the micro-camera through a USB interface, and from the data from the developed smart glove, transmitted through the BLE interface.  
Figure 30: Application of the sensor on the index finger (**a**) and corresponding output signal Vout (yellow trace) and the filtered output signal Vout, filtered (cyan trace) related to finger bending (**b**).  
Figure 31: Application of the sensor on the wrist (**a**) and corresponding output signal Vout (yellow trace) and the filtered output signal Vout, filtered (cyan trace) related to wrist bending (**b**).  
In particular, the smart glove locally calculates the tactile features by spectral analysis over 2 s windows and transmits them to the Raspberry Pi board through Bluetooth. In parallel, the latter acquires the camera's image and synchronizes them with the haptic information transmitted by the smart glove. Furthermore, the Raspberry board processes the pictures to extract the features; specifically, the processing consists of normalizing image data to a float value between 0 and 1 and reducing the color depth. Then, the processing board implements a neural network based on MobileNetV2 architecture [137].  
The MobileNetV2 relies on bottleneck that depth-separable with residuals, In particular, the architecture includes, at first, a full convolution layer constituted by 32 filters; then, 19 residual bottleneck layers are placed. Batch normalization and dropout are employed during training along with a 3 \(\times\) 3 kernel size, typical for contemporary networks [137]. Relu functions are used as non-linearity due to their higher reliability when implemented in low-precision computing. The neural network has an output layer constituted by \(n\) nodes, corresponding to as many class objects to be identified. In this way, the neural network allows objects to be classified based on both tactile and visual data.  
Similarly, the Edge Impulse platform, based on the TensorFlow ecosystem, can be used for developing the described model. As discussed above, it enables handling all the involved phases, in other words, gathering the dataset, including pictures and signals from piezoelectric sensors, the creation of processing and inferring chain, and model testing to evaluate its performances. The model can be employed in C++ and continuously run on the Raspberry board. Furthermore, the recognized object class can be communicated to the patient by means of a vocal indication synthesized by the Festival speech synthesis package, reproduced through earphones [138].  
Figure 32: Graphical representation of the proposed combined tactile and image recognition system.