基于柔性太阳能电池和超薄水凝胶薄膜的手势识别

doi:10.11959/j.issn.1000-0801.2023143

电信科学 ›› 2023, Vol. 39 ›› Issue (7): 109-115.doi: 10.11959/j.issn.1000-0801.2023143

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¹^,², 丁文伯³, 徐晓敏³, 宋林琦¹^,², 徐伟涛¹^,²

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

修回日期:2023-07-05 出版日期:2023-07-20 发布日期:2023-07-01
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
基金资助:
国家自然科学基金资助项目(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(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(CityU11201422)

Gesture recognition based on flexible solar cells and ultrathin hydrogel film

Rucheng WU¹^,², Wenbo DING³, Xiaomin XU³, Linqi SONG¹^,², Weitao XU¹^,²

¹ Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China
² Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China
³ Tsinghua Shenzhen International Graduate School, Shenzhen 518071, China

Revised:2023-07-05 Online:2023-07-20 Published:2023-07-01
Supported by:
The National Natural Science Foundation of China(62101471);The Shenzhen Research Institute, City University of Hong Kong, and the Research Grants Council of the Hong Kong Special Administrative Region, China(CityU21201420);The Shenzhen Research Institute, City University of Hong Kong, and the Research Grants Council of the Hong Kong Special Administrative Region, China(CityU11201422)

摘要/Abstract

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。

关键词: 柔性太阳能电池, 超薄水凝胶薄膜, 手势识别, 机器学习, 智能可穿戴设备

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.

Key words: flexible solar cells, ultrathin hydrogel film, gesture recognition, machine learning, smart wearable device

中图分类号:

TP391

吴如成, 丁文伯, 徐晓敏, 宋林琦, 徐伟涛. 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, Linqi SONG, Weitao XU. Gesture recognition based on flexible solar cells and ultrathin hydrogel film[J]. Telecommunications Science, 2023, 39(7): 109-115.

图/表 6

图1

图2

图3

图4

图5

图6

参考文献 26

[1]	CHAUDHARY A , RAHEJA J L , DAS K ,et al. Intelligent approaches to interact with machines using hand gesture recognition in natural way:asurvey[J]. International Journal of Computer Science ＆ Engineering Survey, 2011,2(1): 122-133.
[2]	RUIZ J , LI Y , LANK E . User-defined motion gestures for mobile interaction[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2011: 197-206.
[3]	STURMAN D J , ZELTZER D . A survey of glove-based input[J]. IEEE Computer Graphics and Applications, 1994,14(1): 30-39.
[4]	PAVLOVIC V I , SHARMA R , HUANG T S . Visual interpretation of hand gestures for human-computer interaction:a review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7): 677-695.
[5]	QUEK F K H . Toward a vision-based hand gesture interface[C]// Proceedings of Virtual Reality Software and Technology. WORLD SCIENTIFIC, 1994: 17-31.
[6]	JARVIS R A . A perspective on range finding techniques for computer vision[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983,PAMI-5(2): 122-139.
[7]	WURFEL P . Physics of solar cells:from basic principles to advanced concepts[M]. John Wiley ＆ Sons, 2016.
[8]	CHIOU R , NGUYEN H , CIOBANESCU H I ,et al. Developing VR-based solar cell lab module in green manufacturing education[C]// Proceedings of 2021 ASEE Virtual Annual Conference Content Access Proceedings. ASEE Conferences, 2021.
[9]	CHENG S M , LOU Z R , ZHANG L ,et al. Ultrathin hydrogel films toward breathable skin-integrated electronics[J]. Advanced Materials (Deerfield Beach,Fla), 2023,35(1): e2206793.
[10]	MITRA S , ACHARYA T . Gesture recognition:asurvey[J]. IEEE Transactions on Systems,Man,and Cybernetics,Part C (Applications and Reviews), 2007,37(3): 311-324.
[11]	ABDELNASSER H , HARRAS K , YOUSSEF M . Wigest:aubiquitous WiFi-based gesture recognition system[C]// Proceedings of Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1. Hamad bin Khalifa University Press (HBKU Press), 2014:ITPP1127.
[12]	Human 3D reconstruction and motion capture using a single flying camera - rare ＆special e-zone[EB]. 2018.
[13]	JACKO J , SEARS A . Proceedings of the SIGCHI conference on human factors in computing systems[EB]. 2001.
[14]	XU Z , BAI K , ZHU S C . TapLogger:inferring user inputs on smartphone touchscreens using on-board motion sensors[C]// Proceedings of the Fifth ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York:ACM Press, 2012: 113-124.
[15]	LI T X , XIONG X , XIE Y F ,et al. Reconstructing hand poses using visible light[J]. Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies, 2017,1(3): 1-20.
[16]	VARSHNEY A , SOLEIMAN A , MOTTOLA L ,et al. Battery-free visible light sensing[C]// Proceedings of the 4th ACM Workshop on Visible Light Communication Systems. New York:ACM Press, 2017: 3-8.
[17]	MA D , LAN G H , HASSAN M ,et al. SolarGest:ubiquitous and battery-free gesture recognition using solar cells[C]// Proceedings of MobiCom’19:The 25th Annual International Conference on Mobile Computing and Networking. New York:ACM Press, 2019: 1-15.
[18]	CHEADLE C , VAWTER M P , FREED W J ,et al. Analysis of microarray data using Z score transformation[J]. The Journal of Molecular Diagnostics:JMD, 2003,5(2): 73-81.
[19]	HUANG D Y , HU W C , CHANG S H . Vision-based hand gesture recognition using PCA Gabor filters and SVM[C]// Proceedings of 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Piscataway:IEEE Press, 2009: 1-4.
[20]	CAMG?Z N C , KINDIROGLU A A , AKARUN L . Gesture recognition using template based random forest classifiers[C]// European Conference on Computer Vision. Cham:Springer, 2015: 579-594.
[21]	WU Y H , WU Z , FU C L . Continuous arm gesture recognition based on natural features and logistic regression[J]. IEEE Sensors Journal, 2018,18(19): 8143-8153.
[22]	LIU Y Q , WANG X H , YAN K . Hand gesture recognition based on concentric circular scan lines and weighted K-nearest neighbor algorithm[J]. Multimedia Tools and Applications, 2018,77(1): 209-223.
[23]	CHUNG H Y , CHUNG Y L , TSAI W F . An efficient hand gesture recognition system based on deep CNN[C]// Proceedings of 2019 IEEE International Conference on Industrial Technology (ICIT). Piscataway:IEEE Press, 2019: 853-858.
[24]	ZHU G M , ZHANG L , SHEN P Y ,et al. Multimodal gesture recognition using 3-D convolution and convolutional LSTM[J]. IEEE Access, 2017(5): 4517-4524.
[25]	SENGUPTA A , YE Y T , WANG R ,et al. Going deeper in spiking neural networks:VGG and residual architectures[J]. Frontiers in Neuroscience, 2019(13): 95.
[26]	PAGLIARO M , CIRIMINNA R , PALMISANO G . Flexible solarcells[J]. ChemSusChem, 2008,1(11): 880-891.

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

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 26

相关文章 15

Metrics

推荐阅读 0

[1]	郭泽华, 朱昊文, 徐同文. 面向分布式机器学习的网络模态创新[J]. 电信科学, 2023, 39(6): 44-51.
[2]	刘雅琼, 吕哲, 赵亚飞, 寿国础. AI技术在卫星通信/互联网领域的应用综述[J]. 电信科学, 2023, 39(2): 10-24.
[3]	章坚武, 安彦军, 邓黄燕. DNS攻击检测与安全防护研究综述[J]. 电信科学, 2022, 38(9): 1-17.
[4]	周志超, 冯毅, 夏小涵, 冯瑜瑶, 蔡超, 邱佳慧, 杨立辉, 乌云霄. 基于移动蜂窝网的机器学习室外指纹定位方案[J]. 电信科学, 2021, 37(8): 85-95.
[5]	肖子玉. 面向2030的未来网络关键技术综述——Beyond 5G[J]. 电信科学, 2020, 36(9): 114-121.
[6]	李想,李原,张子飞,杨哲. 基于密度聚类的网络性能故障大数据分析方法[J]. 电信科学, 2020, 36(9): 51-58.
[7]	邱亚星,王希栋,边森,岳磊. 基于聚类分析和深度学习的多频多模网络负载均衡优化[J]. 电信科学, 2020, 36(7): 156-162.
[8]	林锋,徐柳婧,陈晓华,戚伟强,陈可,朱添田. 一种基于多视角特征融合的Webshell检测方法[J]. 电信科学, 2020, 36(6): 125-132.
[9]	欧阳晔,杨爱东,孟凡语. 一种博弈论辅助的机器学习算法检测用户流失行为[J]. 电信科学, 2020, 36(6): 79-89.
[10]	庞涛,丘海华,潘碧莹. 手机终端人工智能关键技术研究[J]. 电信科学, 2020, 36(5): 145-151.
[11]	盛剑涛,陈茂飞,刘东鑫,汪来富,史国水,金华敏. 基于组合分类器的恶意域名检测技术[J]. 电信科学, 2020, 36(5): 47-55.
[12]	钟其柱. 基于机器学习分析VoLTE视频通话质量的研究及应用[J]. 电信科学, 2020, 36(3): 156-165.
[13]	刘姿杉,程强,吕博. 面向机器学习的隐私保护关键技术研究综述[J]. 电信科学, 2020, 36(11): 18-27.
[14]	陈孝莲,秦奕,张杰,李亚杰,宋浩鲲,张会彬. 基于机器学习的光纤窃听检测方法[J]. 电信科学, 2020, 36(11): 61-67.
[15]	李佳,丛犁,姜华,胡杨,徐梦. 基于DBN-Softmax的电力通信网络带宽预测[J]. 电信科学, 2020, 36(10): 153-158.