Embedded Electronic System Based on Dedicated Hardware DSPs for Electronic Skin Implementation. (2016)

• Record Type: Journal Article
• Authors: Ibrahim, Ali; Noli, Luca; Gastaldo, Paolo; Chible, Hussein; Valle, Maurizio
• Is Part Of: Procedia technology. Volume 26(2016)
• Page Start: 43
• Page End: 50
• Publication Date: 2016
• Subjects: Electronic Skin System -- Dedicated Embedded Hardware Implementation -- Digital Signal Processing -- Real Time Functionality ;; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.08.007
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: The effort to develop an electronic skin is highly motivated by many application domains namely robotics, biomedical instrumentations, and replacement prosthetic devices. Several e-skin systems have been proposed recently and have demonstrated the need of an embedded electronic system for tactile data processing either to mimic the human skin or to respond to the application demands. Processing tactile data requires efficient methods to extract meaningful information from raw sensors data. In this framework, our goal is the development of a dedicated embedded electronic system for electronic skin. The embedded electronic system has to acquire the tactile data, process and extract structured information. Machine Learning (ML) represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensors data. This paper presents an embedded electronic system based on dedicated hardware implementation for electronic skin systems. It provides a Tensorial kernel function implementation for machine learning based on Tensorial kernel approach. Results assess the time latency and the hardware complexity for real time functionality. The implementation results highlight the high amount of power consumption needed for the input touch modalities classification task. Conclusions and future perspectives are also presented.