###### Abstract  
Developments in flexible electronics have adopted various approaches which have enhanced the applicability of human-machine interface fields. Recently, microstructural integration and hybrid functional materials were designed for realizing human somatosensory. Nonetheless, designing tactile sensors with smart structures using facile and low-cost fabrication processes remains challenging. Furthermore, using the sensors for recognizing stimuli and feedback applications remains poorly validated. In this study, a highly flexible piezoresistive tactile sensor was developed by homogeneously dispersing carbon black (CB) in a microstructure porous sugar/PDMS-based sponge. Owning to its high flexibility and softness, the sensor can be mounted on human or robotic systems for different clinical applications. We validated the applicability of the proposed sensor by applying it to recognizing grasp and release forces in an open setting and to classifying hand motions that surgeons apply on the master interface of a robotic system during intravascular catheterization. For this purpose, we implemented the long short-term memory (LSTM)-dense classification model and five traditional machine learning methods, namely, support vector machine, multilayer perceptron, decision tree, and k-nearest neighbor. The models were used to classify the different hand gestures obtained in an open-setting experiment. Amongst all, the LSTM-dense method yielded the highest overall recognition accuracy (87.38%). Nevertheless, the performance of the other models was in a similar range, showing that our sensor structure can be applied in intelligence sensing or tactile feedback systems. Secondly, the sensor prototype was applied to analyze the motions made while manipulating an interventional robot. We analyzed the displacement and velocity of the master interface during typical axial (push/pull) and radial operations with the robot. The results obtained show that the sensor is capable of recording unique patterns during different operations. Thus, a combination of the flexible wearable sensors and machine learning could yield a future generation of flexible materials and artificial intelligence of things (AIoT) devices. tactile sensors; low-cost electronics; sensor applications; machine learning; human-machine interface; surgical robotics +
Footnote †: journal: Applied Sciences  
Accepted: 2 August 2021  
Footnote †: journal: Applied Sciences
achieving intelligent sensing systems; this can be attributed to the integration of higher capabilities for processing information [5]. In recent years, great efforts have been made to enhance the flexibility and sensitivity of wearable tactile sensors to meet the essential requirements needed for intelligent devices [6]. In terms of functionality, piezoresistivity, piezoelectricity, capacitance, and triboelectricity are the four main sensing mechanisms that have been utilized [7; 8; 9; 10]. Mainly, these sensing mechanisms involve converting external forces and mechanical stimuli into electrical signals. Among them, piezoresistive-type sensors are more favored owning to their cost-effectiveness, simple design and structure, facile fabrication, and simple read-out signals; thus, they are widely adopted. In addition, the flexibility, stability, and sensitivity of piezoresistive sensors are highly desired when compared with sensors using other sensing mechanisms. Usually, the conventional designs of high-performance flexible piezoresistive sensors are based on polymer matrices (such as elastomers--Ecoflex rubber and polydimethylsiloxane (PDMS) [11; 12]) and conductive filler networks including graphene, nanowires, nanofibers, carbon black, and carbon nanomaterials [13; 14; 15; 16; 17]. However, a variety of microstructure materials, such as pyramids [18; 19], nanofibers, and two-dimensional electrodes, are widely used for developing flexible piezoresistive sensors which have shown high sensitivity and reliable linear response.