Minimalist and multi-functional human machine interface (HMI) using a flexible wearable triboelectric patch

By Qiongfeng Shi, Zixuan Zhang, Tao Chen, Chengkuo Lee. Nano Energy, 2019

173 citations recorded by the source corpus.

Abstract Human machine interfaces are of increasing importance in our daily life to connect human intentions with machine actions. Recently, triboelectric interfaces are being extensively investigated in order to achieve flexible wearable and self-powered capability simultaneously. However, drawbacks exist in current designs for the detection of common human interactions, such as large number of sensing elements and electrodes, poor output performance, and interacting experience, etc. This work presents a flexible triboelectric interacting patch with only four sensing electrodes to detect various human machine interactions. The four electrodes are configured in the layout of a splitting ring. Initially, by leveraging the individual areas and common jointing areas of the four electrodes, eight functional electrodes points are defined, which can achieve position sensing with clear differentiations even under different types of operations including both tapping and sliding interactions. Moreover, nine additional points out of the electrode areas can be defined as well for more advanced sensing of operation positions and manners, through distinguishing the unique patterns of the generated voltage. With these predefined points, the interacting patch can be applied as general interface for various human machine interactions. Based on the fabricated device, functional interfaces for writing trace recognition, identification code system and remote control are successfully realized, showing the high applicability of the device in diversified human machine interactions. Indicating by these affluent demonstrations, the developed interacting patch exhibits great potential in various interacting applications, e.g., writing pad, security, smart control, entertainment, virtual reality, augmented reality, and robotics, etc.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/1f54f577ce23d72450708151d3aa30226a18a2c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chengkuo Lee
2. Xinge Guo
3. Long Liu
4. Tianyiyi He
5. Feng Wen
6. Zhongda Sun
7. Qiongfeng Shi
8. Bowei Dong

In Cognitive Neuroscience

1. Chengkuo Lee
2. Xinge Guo
3. Long Liu
4. Tianyiyi He
5. Feng Wen
6. Qiongfeng Shi
7. Jianxiong Zhu
8. Bowei Dong

In Human-Computer Interaction

1. Xinge Guo
2. Long Liu
3. Chengkuo Lee

Citations in the corpus

• Progress in TENG technology—A journey from energy harvesting to nanoenergy and nanosystem (2020, methodology): “Copyright 2019, Elsevier.233 G, in vivo powering of the pacemaker by respiration-driven implanted TENG.”
• Promoting smart cities into the 5G era with multi-field Internet of Things (IoT) applications powered with advanced mechanical energy harvesters (2021, methodology): “Similar control patches or pads with triboelectric electrodes` designs also have been developed and utilized to achieve controlling a drone in a virtual game [341,342], a small vehicle in reality [342,343], and build an identification code system [341,343].”
• Development Trends and Perspectives of Future Sensors and MEMS/NEMS (2019, background): “Figure 11e depicts a minimalist triboelectric patch with only four sensing electrodes for multi-functional applications [177].”