Dataset Information

Stretchable Filler/Solid Rubber Piezoresistive Thread Sensor for Gesture Recognition.

ABSTRACT: Recently, the stretchable piezoresistive composites have become a focus in the fields of the biomechanical sensing and human posture recognition because they can be directly and conformally attached to bodies and clothes. Here, we present a stretchable piezoresistive thread sensor (SPTS) based on Ag plated glass microspheres (Ag@GMs)/solid rubber (SR) composite, which was prepared using new shear dispersion and extrusion vulcanization technology. The SPTS has the high gauge factors (7.8~11.1) over a large stretching range (0-50%) and approximate linear curves about the relative change of resistance versus the applied strain. Meanwhile, the SPTS demonstrates that the hysteresis is as low as 2.6% and has great stability during 1000 stretching/releasing cycles at 50% strain. Considering the excellent mechanical strain-driven characteristic, the SPTS was carried out to monitor posture recognitions and facial movements. Moreover, the novel SPTS can be successfully integrated with software and hardware information modules to realize an intelligent gesture recognition system, which can promptly and accurately reflect the produced electrical signals about digital gestures, and successfully be translated into text and voice. This work demonstrates great progress in stretchable piezoresistive sensors and provides a new strategy for achieving a real-time and effective-communication intelligent gesture recognition system.

SUBMITTER: Zhu P

PROVIDER: S-EPMC8780386 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and to respond accordingly. The ability to discriminate between human gestures can help in several applications, such as assisted living, healthcare, neuro-rehabilitation, and sports. Recently, multi-sensor data fusion mechanisms have been investigated to improve discrimination accuracy. In this paper, we present a sensor fusion framework that integrates complementary systems: the electromyography (EMG) signal from muscles and visual information. This multi-sensor approach, while improving accuracy and robustness, introduces the disadvantage of high computational cost, which grows exponentially with the number of sensors and the number of measurements. Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios, such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of five gestures from sign language where visual and electromyography signals are synchronized. We compared a fully neuromorphic approach to a baseline implemented using traditional machine learning approaches on a portable GPU system. According to the chip's constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline. These neuromorphic alternatives have increased inference time, between 20 and 40%, with respect to the GPU system but have a significantly smaller energy-delay product (EDP) which makes them between 30× and 600× more efficient. The proposed work represents a new benchmark that moves neuromorphic computing toward a real-world scenario.

Dataset Information

Stretchable Filler/Solid Rubber Piezoresistive Thread Sensor for Gesture Recognition.

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