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

Abstract

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

CLC Number:

TP391

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.

Figures/Tables 6

References 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.

Metrics

Recommended 0

No Suggested Reading articles found!

Gesture recognition based on flexible solar cells and ultrathin hydrogel film

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 26

Related Articles 15

Metrics

Recommended 0

[1]	Zehua GUO, Haowen ZHU, Tongwen XU. Network modal innovation for distributed machine learning [J]. Telecommunications Science, 2023, 39(6): 44-51.
[2]	Yaqiong LIU, Zhe LYU, Yafei ZHAO, Guochu SHOU. A survey on AI techniques applied in the satellite communication/satellite Internet field [J]. Telecommunications Science, 2023, 39(2): 10-24.
[3]	Jianwu ZHANG, Yanjun AN, Huangyan DENG. A survey on DNS attack detection and security protection [J]. Telecommunications Science, 2022, 38(9): 1-17.
[4]	Zhichao ZHOU, Yi FENG, Xiaohan XIA, Yuyao FENG, Chao CAI, Jiahui QIU, Lihui YANG, Yunxiao WU. Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network [J]. Telecommunications Science, 2021, 37(8): 85-95.
[5]	Ziyu XIAO. A review of key technologies for the future of the 2030 network——Beyond 5G [J]. Telecommunications Science, 2020, 36(9): 114-121.
[6]	Xiang LI,Yuan LI,Zifei ZHANG,Zhe YANG. A density clustering-based network performance failure big data analysis algorithm [J]. Telecommunications Science, 2020, 36(9): 51-58.
[7]	Yaxing QIU,Xidong WANG,Sen BIAN,Lei YUE. Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network [J]. Telecommunications Science, 2020, 36(7): 156-162.
[8]	Feng LIN,Liujing XU,Xiaohua CHEN,Weiqiang QI,Ke CHEN,Tiantian ZHU. Method of Webshell detection based on multi-view feature fusion [J]. Telecommunications Science, 2020, 36(6): 125-132.
[9]	Ye OUYANG,Aidong YANG,Fanyu MENG. A game theory-assisted machine learning methodology for subscriber churn behaviors detection [J]. Telecommunications Science, 2020, 36(6): 79-89.
[10]	Tao PANG,Haihua QIU,Biying PAN. Research on artificial intelligence key technologies of mobile terminal [J]. Telecommunications Science, 2020, 36(5): 145-151.
[11]	Jiantao SHENG,Maofei CHEN,Dongxin LIU,Laifu WANG,Guoshui SHI,Huamin JIN. Detection of malicious domain name based on a classifier combination [J]. Telecommunications Science, 2020, 36(5): 47-55.
[12]	Qizhu ZHONG. Research and application of VoLTE video call quality based on machine learning [J]. Telecommunications Science, 2020, 36(3): 156-165.
[13]	Zishan LIU,Qiang CHENG,Bo LV. A survey on key technologies of privacy protection for machine learning [J]. Telecommunications Science, 2020, 36(11): 18-27.
[14]	Xiaolian CHEN,Yi QIN,Jie ZHANG,Yajie LI,Haokun SONG,Huibin ZHANG. Optical fiber eavesdropping detection method based on machine learning [J]. Telecommunications Science, 2020, 36(11): 61-67.
[15]	Jia LI,Li CONG,Hua JIANG,Yang HU,Meng XU. Bandwidth prediction of power communication network based on DBN-Softmax [J]. Telecommunications Science, 2020, 36(10): 153-158.