基于柔性太阳能电池和超薄水凝胶薄膜的手势识别

吴如成; 丁文伯; 徐晓敏; 宋林琦; 徐伟涛

doi:10.11959/j.issn.1000-0801.2023143

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基于柔性太阳能电池和超薄水凝胶薄膜的手势识别

研究与开发 | 更新时间：2024-06-05

- 基于柔性太阳能电池和超薄水凝胶薄膜的手势识别
- Gesture recognition based on flexible solar cells and ultrathin hydrogel film
- 电信科学 2023年39卷第7期页码：109-115
- 作者机构：
  
  1. 香港城市大学深圳研究院，广东深圳 518057
  2. 香港城市大学计算机学院，香港 999077
  3. 清华大学深圳国际研究生院，广东深圳 518071
- 作者简介：
  
  [ "吴如成（1998- ），男，香港城市大学计算机学院博士生，香港城市大学深圳研究院博士生，主要研究方向为人工智能、可穿戴设备和物联网等" ]
  [ "丁文伯（1990- ），男，博士，清华大学深圳国际研究生院助理教授、博士生导师，主要研究方向为信号处理、机器人、人机界面和机器学习" ]
  [ "徐晓敏（1989- ），女，博士，清华大学深圳国际研究生院副教授、博士生导师，主要从事柔性材料和生物电子器件领域工作" ]
  [ "宋林琦（1985- ），男，博士，香港城市大学计算机学院助理教授，香港城市大学深圳研究院副研究员，主要研究方向为数据科学、机器学习、推荐系统、信息论和算法" ]
  [ "徐伟涛（1987- ），男，博士，香港城市大学计算机学院助理教授，香港城市大学深圳研究院副研究员，主要研究方向为物联网、移动计算、物联网安全、可穿戴设备和低功耗广域网" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62101471);香港城市大学深圳研究院和中国香港特别行政区研究资助局资助项目;The Shenzhen Research Institute, City University of Hong Kong, and the Research Grants Council of the Hong Kong Special Administrative Region, China(CityU21201420);香港城市大学深圳研究院和中国香港特别行政区研究资助局资助项目;The Shenzhen Research Institute, City University of Hong Kong, and the Research Grants Council of the Hong Kong Special Administrative Region, China(CityU11201422)
- DOI：10.11959/j.issn.1000-0801.2023143
  中图分类号： TP391
- 网络出版日期：2023-07，
  
  纸质出版日期：2023-07-20
- 稿件说明：
移动端阅览
吴如成, 丁文伯, 徐晓敏, 等. 基于柔性太阳能电池和超薄水凝胶薄膜的手势识别[J]. 电信科学, 2023,39(7):109-115.

Rucheng WU, Wenbo DING, Xiaomin XU, et al. Gesture recognition based on flexible solar cells and ultrathin hydrogel film[J]. Telecommunications science, 2023, 39(7): 109-115.
吴如成, 丁文伯, 徐晓敏, 等. 基于柔性太阳能电池和超薄水凝胶薄膜的手势识别[J]. 电信科学, 2023,39(7):109-115. DOI： 10.11959/j.issn.1000-0801.2023143.

Rucheng WU, Wenbo DING, Xiaomin XU, et al. Gesture recognition based on flexible solar cells and ultrathin hydrogel film[J]. Telecommunications science, 2023, 39(7): 109-115. DOI： 10.11959/j.issn.1000-0801.2023143.

摘要

人们在日常生活中会用到各种各样的手势，如何将不同的手势与现有的智能可穿戴设备等结合起来对生活质量的提升有不可或缺的作用，利用太阳能相关设备的光电转换特性可以很好地解决手势识别和设备能耗问题。在柔性太阳能电池与手势识别结合的研究中，采集了5种常用手势数据，进行了Z-Score、低通滤波、滑动窗口等信号处理方法，并利用随机森林、支持向量机和神经网络等对其进行分类，成功地在小样本的基础上实现了100%的预测精度，可以证明该方法在手势识别的应用中有显著优势。

Abstract

Daily life involves various gestures

and combining these with smart wearable devices is crucial for improving quality of life.One effective solution to the challenges of gesture recognition and device energy consumption isutilizing the photoelectric conversion characteristics of solar energy-related devices.The data of five commonly used gestures were collected in the research of the combination of flexible solar cells and gesture recognition.Z-Score

low-pass filter

sliding window techniques for signal processing were applied

and successfully achieved 100% predicted accuracy using random forest

support vector machine and neural network algorithms even with small samples which showed that this method had significant advantages in the application of gesture recognition.

关键词

Keywords

references

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