Please wait a minute...
浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
Automatic Technology, Telecommunication Technology     
Fingertip tapping force analysis for mobile devices HCI
YANG Wen zhen, ZHANG Hao, WU Xin li, SHAO Ming chao, JIN Zhong zheng
Virtual Reality Laboratory, Zhejiang Sci-Tech University, Hangzhou 310018, China
Download:   PDF(2193KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

Three type tapping forces (gentle tapping force, normal tapping force and heavy tapping force) and their corresponding tapping contact areas were measured and analyzed to explore the fingertip tapping forces interaction behaviors in order to realize the natural human-computer interaction (HCI) on the smart mobile devices by fingertip forces. Experimental results were as follows. 1) Thumb, index finger, middle finger, and ring finger have strong abilities to control their fingertip forces with gentle, normal, and heavy tapping actions, and more clearly. Every finger can act precise general tapping forces. 2) The gentle tapping force and the heavy tapping force can be definitely recognized by their tapping contact areas, although there is no exact full mapping correlations between the tapping forces and the tapping contact areas. 3) In some extents, three types tapping forces can be distinguished by their corresponding tapping contact areas.

Published: 28 October 2016
CLC:  TP 391  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Cite this article:

YANG Wen zhen, ZHANG Hao, WU Xin li, SHAO Ming chao, JIN Zhong zheng. Fingertip tapping force analysis for mobile devices HCI. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2016, 50(10): 1995-2001.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2016.10.021     OR     http://www.zjujournals.com/eng/Y2016/V50/I10/1995


面向移动终端人机交互的指尖点击力

为了实现智能移动终端基于指尖力的自然人机交互,以指尖点击交互为研究对象,按3种指尖点击力(轻点击力、正常点击力和重点击力)开展实验研究,探究指尖点击交互时点击力和点击面积的内在机理,试图依据指尖点击面积区别出这3种点击力的交互行为.实验结果如下：1) 大拇指、食指、中指和无名指都能够施加有明显区分度的轻点击力、正常点击力和重点击力,特别是对轻点击力均有很好的控制能力；2) 点击力和点击面积之间不存在满映射关系,但是存在强相关性；3) 依据点击面积可以很好地区分出指尖的轻点击力和重点击力.

［1］ HEO S, GU J, LEE G. Expanding touch input vocabulary by using consecutive distant taps ［C］∥ ACM Conference on Human Factors in Computing Systems. Toronto: ACM, 2014: 2597-2606.
［2］ ARIF A S, MAZALEKA, STUERZLINGER W. The use of pseudo pressure in authenticating smartphone users ［C］ ∥ MOBIQUITOUS’14. London: IEEE, 2014: 151-160.
［3］ HEO S, LEE G. Force gestures: augmented touch screen gestures using normal and tangential force［C］ ∥ UIST’11. New York: ACM, 2011: 621-626.
［4］ LEE B, LEE H, LIM S, et al. Evaluation of human tangential force input performance ［C］∥ ACM Conference on Human Factors in Computing Systems. New York: ACM, 2012: 3121-3130.
［5］ STEWART C, ROHS M, KRATZ S, et al. Characteristics of pressurebased input for mobile devices ［C］∥ ACM Conference on Human Factors in Computing Systems. Atlanta: ACM, 2010: 801-810.
［6］ STEWART C, HOGGAN E, HAVERINEE L, et al. An exploration of inadvertent variations in mobile pressure input ［C］ ∥ MobileHCI’12. San Francisco: ACM, 2012: 15.
［7］ HEO S, HAN J, LEE G. Designing rich touch interaction through proximity and 2.5D force sensing touchpad ［C］∥ Australian ComputerHuman Interaction Conference: Augmentation, Application, Innovation, Collaboration. New York: ACM, 2013: 401-404.
［8］ JOHN W, REN D, CHRISTOPHER W, et al. Analysis of the effects of surface stiffness on the contact interaction between a finger and a cylindrical handle using a threedimensional hybrid model ［J］. Medical Engineering and Physics, 2014, 36 (7): 831-841.
［9］ HEO P, KIM J. Finger flexion force sensor based on volar displacement of flexor tendon ［C］∥ ICRA’12. Saint Paul: IEEE, 2012: 1392-1397.
［10］ ODAGAKI M, TAURA T, HARAKAWA T. Touch interface for sensing fingertip forcein mobile device using electromyogram ［C］∥ EMBS’13. Osaka: IEEE, 2013: 3443-3446.
［11］ LIU P, MARTEL F, RANCOURT D, et al. Fingertip force estimation from forearm muscle electrical activity ［C］∥ ICASSP’14. Florence: IEEE, 2014: 2069-2073.
［12］ MASCARO S A, ASADA H. Measurement of finger posture and threeaxis fingertip touch force using fingernail sensors ［J］. IEEE Transactions on Robotics and Automation, 2004, 2(1): 26-35.
［13］ CHEN N, URBAN S, BAYER, et al. Measuring fingertip forces from camera images for random fingerposes ［C］∥ IROS’15. Hamburg: IEEE, 2015: 16.
［14］ HWANG S, BIANCHI A, WOHN K Y. Vibpressestimation pressure input using vibration absorption on mobile devices ［C］∥ MobileHCI′13. Munich: ACM, 2013: 27-30.
［15］ KURITA Y, IKEDA A, UEDA J, et al. A fingerprint pointing device utilizing the deformation of the fingertip during the incipient slip ［J］. IEEE Transaction onRobotics, 2005, 21 (5): 801-811.
［16］ SONEDA T, NAKANO K. Investigation of vibrotactile sensation of human fingerpads by observation of contact zones ［J］. International Tribology, 2010,43(1): 210-217.
［17］ AKIHITO K, TSUJI T, KURITA Y. Fingertip force estimation based on the deformation of the fingertip ［M］∥ HIROYUKI K, HIDEYUKI A, KIUK K. Haptic interaction. Japan: Springer, 2015: 201-205.
［18］ 杨文珍, 高曙明, 万华根, 等. 基于人手指力学特性的虚拟手接触力生成［J］. 浙江大学学报：工学版, 2008, 42(12): 2145-2150.
YANG Wenzhen, GAO Shuming, WAN Huagen, et al. Contact force rendering of virtual hand interaction based on human fingers’ force characteristics ［J］. Journal of Zhejiang University: Engineering Science, 2008, 42(12): 2145-2150.
[1] HE Xue-jun, WANG Jin, LU Guo-dong, LIU Zhen-yu, CHEN Li, JIN Jing. 3D head portrait sculpture by industrial robot based on triangular mesh slicing and collision detection[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1104-1110.
[2] WANG Hua, HAN Tong-yang, ZHOU Ke. KeyGraph-based community detection algorithm for public security intelligence[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1173-1180.
[3] YOU Hai-hui, MA Zeng-yi, TANG Yi-jun, WANG Yue-lan, ZHENG Lin, YU Zhong, JI Cheng-jun. Soft measurement of heating value of burning municipal solid waste for circulating fluidized bed[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1163-1172.
[4] BI Xiao-jun, WANG Jia-hui. Teaching-learning-based optimization algorithm with hybrid learning strategy[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(5): 1024-1031.
[5] WANG Liang, YU Zhi-wen, GUO Bin. Moving trajectory prediction model based on double layer multi-granularity knowledge discovery[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 669-674.
[6] LIAO Miao, ZHAO Yu-qian, ZENG Ye-zhan, HUANG Zhong-chao, ZHANG Bing-kui, ZOU Bei-ji. Automatic segmentation for cell images based on support vector machine and ellipse fitting[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 722-728.
[7] HUANG Zheng-yu, JIANG Xin-long, LIU Jun-fa, CHEN Yi-qiang, GU Yang. Fusion feature based semi-supervised manifold localization method[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 655-662.
[8] JIANG Xin-long, CHEN Yi-qiang, LIU Jun-fa, HU Li-sha, SHEN Jian-fei. Wearable system to support proximity awareness for people with autism[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 637-647.
[9] MU Jing-jing, ZHAO Xin-yue, HE Zai-xing, ZHANG Shu-you. Contour reconstruction of overlapped bubbles based on concave-convex transformation and circle fitting[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 714-721.
[10] DAI Cai-yan, CHEN Ling, LI Bin, CHEN Bo-lun. Sampling-based link prediction in complex networks[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 554-561.
[11] LIU Lei, YANG Peng, LIU Zuo-jun. Locomotion-Mode recognition using multiple kernel relevance vector machine[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 562-571.
[12] GUO Meng-li, DA Fei-peng, DENG Xing, GAI Shao-yan. 3D face recognition based on keypoints and local feature[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 584-589.
[13] WANG Hai jun, GE Hong juan, ZHANG Sheng yan. Fast object tracking algorithm via kernel collaborative presentation[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(2): 399-407.
[14] ZHANG Ya nan, CHEN De yun, WANG Ying jie, LIU Yu peng. Incremental graph pattern matching based dynamic recommendation method for cold-start user[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(2): 408-415.
[15] LIU Yu peng, QIAO Xiu ming, ZHAO Shi lei, MA Chun guang. Deep combination of large-scale features in statistical machine translation[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(1): 46-56.