## 2 System Design and Method
### Gesture Recognition Procedure  
One of the key functions of smart sensory gesture systems is their ability to instantaneously respond to external stimuli, as well as accurately identify force and position information during somatosensory stimuli [35]. Three tactile sensors fabricated and characterized as described above were used for training a machine learning model. Based on using piezoresistive outputs, the system was first used for touch force recognition, and later deployed within a robotic system for kinesthesis analysis. In both cases, the sensors were assembled and fixed with an adhesive tape on the thumb, index, and middle fingers of the right hand. Then, open-setting experiments were conducted in which five volunteers were directed to establish typical hand motions used during intravascular catheterization. The outputs from the tactile sensors were simultaneously collected to monitor the original gesture signals by converting hand force motions to resistance variation values. During the process, Arduino uno 3r (Atmega 328P-PU) was used as the data recording platform. The corresponding resistance changes were trans  
Figure 3: Rapid response of the output voltage of the piezoresistive tactile sensor under different loading pressures at frequencies of (**a**) 1 kHz and (**b**) 10 kHz. ferred to the host computer via USB and used as the input data. To ensure high recognition accuracy of hand movements, a huge dataset comprising different sensors was acquired from multiple sessions, whereby the five volunteers were guided to repeatedly establish only a single hand motion. In each trial, a minimum of 11,500 data points were acquired for each movement, resulting in a total of 45,000 data points from all experiments. We further argue that the new sensor structure has stable characteristics in terms of data acquisition; that is, each sensor unit produces statistical data that are learnable by both traditional machine learning and deep learning approaches. For this purpose, we developed a long short-term memory (LSTM)-dense classification model and used it to classify the different hand gestures obtained in the open-setting experiment. The LSTM-dense network was implemented within Python based on the Keras(r) Tensorflow framework on a desktop with a 2.2 GHz Intel i7 processor and an NVIDIA graphic processing unit. Recurrent networks have relatively limited performance capacities on some time-series datasets due to the vanishing gradient problem. Thus, LSTM serves as an alternative approach since it requires switching the input, forget, and output gates without hidden cells. Furthermore, dense layers were supplemented into the LSTM-based learning-based network to classify four different types of hand gestures. The flow of the gesture recognition process is illustrated in Figure 4.  
The LSTM layers integrated into the network were separated with an attention unit, while two consecutive dense layers were appended for the actual motion classification based on signals received on the tactile sensors. The deep network consisted of three main parts with different functional layers. The first mainly consisted of the input and preprocessing layers, the hidden module included a variety of LSTM, attention, and dense layers, and the last was a single-layered module for reporting the classification output. During training, network parameters were configured arbitrarily, and the feature learning stratagem of the network was based on unique training (60%), validation (20%), and test (20%) datasets. The training was achieved with a dynamic learning rate initialized at 10\({}^{4}\), an adaptive moment optimizer with a decay value of 10\({}^{6}\), and a recurrent dropout of 10% added for regularization. The performance of the final model was obtained under 1000 training epochs and a batch size of 5. Differences in network performance were achieved, but only the best  
Figure 4: Learning-based network for hand motion classification.