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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃寶儀 | zh_TW |
| dc.contributor.advisor | Polly Huang | en |
| dc.contributor.author | 黃珮欣 | zh_TW |
| dc.contributor.author | Pei-Shin Hwang | en |
| dc.date.accessioned | 2024-06-13T16:06:59Z | - |
| dc.date.available | 2024-06-14 | - |
| dc.date.copyright | 2024-06-13 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2024-04-08 | - |
| dc.identifier.citation | [1] H. Karasawa, R. Fukui, M. Watanabe and S. Warisawa, “Simultaneous Recognition of Hand Shape and Two-Axis Wrist Bending Using Wearable Wrist Contour Measuring Device,” 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, China, 2019, pp. 1550-1555.
[2] T. Mitani, S. Okishiba, N. Tateyama, K. Yamanojo, S. Warisawa and R. Fukui, “A Wearable Multi-Joint Wrist Contour Measuring Device for Hand Shape Recognition,” in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8331-8338, July 2022. [3] “HC-05 Datasheet,” https://components101.com/sites/default/files/component_datasheet/HC-05%20Datasheet.pdf [4] A. Kato, Y. Matsumoto, Y. Kobayashi, S. Sugano and M. G. Fujie, “Wrist joint angle estimation by means of muscle bulge based on deformation of the forearm skin surface,” 2016 World Automation Congress (WAC), Rio Grande, PR, USA, 2016, pp. 1-6. [5] Y. Chen, X. Liang, M. Assaad and H. Heidari, “Wearable Resistive-based Gesture-Sensing Interface Bracelet,” 2019 UK/ China Emerging Technologies (UCET), Glasgow, UK, 2019, pp. 1-4. [6] H. Kaur and J. Rani, “A review: Study of various techniques of Hand gesture recognition,” 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, 2016, pp. 1-5. [7] M. Oudah, A. Al-Naji, and J. Chahl, “Hand Gesture Recognition Based on Computer Vision: A Review of Techniques,” Journal of Imaging, vol. 6, no. 8, p. 73, Jul. 2020. [8] W. Mao, Mei Legam Wang, W. Sun, L. Qiu, S. Pradhan, and Y.-C. Chen, “RNN-Based Room Scale Hand Motion Tracking,” Oct. 2019. [9] D. Li, J. Liu, S. I. Lee, and J. Xiong, “Room-Scale Hand Gesture Recognition Using Smart Speakers,” Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, 2023, pp. 462–475. [10] “ADS1015 Datasheet,” https://www.ti.com/lit/ds/symlink/ads1015.pdf [11] “ADS1X15,” https://github.com/RobTillaart/ADS1X15 [12] Z. Chi et al., “EAR: Exploiting Uncontrollable Ambient RF Signals in Heterogeneous Networks for Gesture Recognition,” Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018, pp. 237–249. [13] N. Yu, W. Wang, A. X. Liu, and L. Kong, “QGesture: Quantifying Gesture Distance and Direction with WiFi Signals,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 2, no. 1, Mar. 2018. [14] Y. Bai, Z. Wang, K. Zheng, X. Wang and J. Wang, “WiDrive: Adaptive WiFi-Based Recognition of Driver Activity for Real-Time and Safe Takeover,” 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Dallas, TX, USA, 2019, pp. 901-911. [15] D. Jiang, M. Li, and C. Xu, “WiGAN: A WiFi Based Gesture Recognition System with GANs,” Sensors, vol. 20, no. 17, p. 4757, Aug. 2020. [16] H. Truong et al., “CapBand: Battery-Free Successive Capacitance Sensing Wristband for Hand Gesture Recognition,” in Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018, pp. 54–67. [17] T. Grosse-Puppendahl et al., “Finding Common Ground: A Survey of Capacitive Sensing in Human-Computer Interaction,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017, pp. 3293–3315. [18] W. K. Wong, F. H. Juwono and B. T. T. Khoo, “Multi-Features Capacitive Hand Gesture Recognition Sensor: A Machine Learning Approach,” in IEEE Sensors Journal, vol. 21, no. 6, pp. 8441-8450, 15 March15, 2021. [19] “Arduino UNO R3 Datasheet,” https://docs.arduino.cc/resources/datasheets/A000066-datasheet.pdf [20] K. S. Abhishek, L. C. F. Qubeley and D. Ho, “Glove-based hand gesture recognition sign language translator using capacitive touch sensor,” 2016 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), Hong Kong, China, 2016, pp. 334-337. [21] H. Yang, X. Yao, L. Yuan, L. Gong, and Y. Liu, “Strain-sensitive electrical conductivity of carbon nanotube-graphene-filled rubber composites under cyclic loading.,” Nanoscale, vol. 11 2, pp. 578–586, 2019. [22] Z. Yang et al., “Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network,” Frontiers in Bioengineering and Biotechnology, vol. 9, p. 779353, Oct. 2021. [23] S. -O. Shin, D. Kim and Y. -H. Seo, “Controlling Mobile Robot Using IMU and EMG Sensor-Based Gesture Recognition,” 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications, Guangdong, China, 2014, pp. 554-557. [24] W. Geng, Y. Du, W. Jin, W. Wei, Y. Hu, and J. Li, “Gesture recognition by instantaneous surface EMG images,” Scientific Reports, vol. 6, p. 36571, Nov. 2016. [25] W.-C. Chuang, W.-J. Hwang, T.-M. Tai, D.-R. Huang, and Y.-J. Jhang, “Continuous Finger Gesture Recognition Based on Flex Sensors,” Sensors, vol. 19, no. 18, p. 3986, Sep. 2019. [26] C. Tan et al., “A high performance wearable strain sensor with advanced thermal management for motion monitoring,” Nature Communications, vol. 11, Jul. 2020. [27] H. Yang, L. H. Gong, Z. Zheng, and X. F. Yao, “Highly stretchable and sensitive conductive rubber composites with tunable piezoresistivity for motion detection and flexible electrodes,” Carbon, vol. 158, pp. 893–903, 2020. [28] X. Chu, J. Liu, and S. Shimamoto, “A sensor-based hand gesture recognition system for Japanese sign language,” in LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies, Mar. 2021, pp. 311–312. [29] S. Jiang et al., “Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing,” in IEEE Transactions on Industrial Informatics, vol. 14, no. 8, pp. 3376-3385, Aug. 2018. [30] M. Kim, J. Cho, S. Lee, and Y. Jung, “IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces,” Sensors, vol. 19, no. 18, p. 3827, Sep. 2019. [31] N. Mohamed, M. B. Mustafa and N. Jomhari, “A Review of the Hand Gesture Recognition System: Current Progress and Future Directions,” in IEEE Access, vol. 9, pp. 157422-157436, 2021. [32] B. Oldfrey, R. Jackson, P. Smitham, and M. Miodownik, “A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors,” Frontiers in Robotics and AI, vol. 6, 2019. [33] A. Kiaghadi, M. Baima, J. Gummeson, T. Andrew, and D. Ganesan, “Fabric as a Sensor: Towards Unobtrusive Sensing of Human Behavior with Triboelectric Textiles,” in Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018, pp. 199–210. [34] L. Nikiel, W. Wampler, J. Neilsen and N. Hershberger, “How carbon black affects electrical properties,” Rubber & Plastics News, 2009, pp. 12–18. [35] M. Weigel, T. Lu, G. Bailly, A. Oulasvirta, C. Majidi, and J. Steimle, “ISkin: Flexible, Stretchable and Visually Customizable On-Body Touch Sensors for Mobile Computing,” in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015, pp. 2991–3000. [36] M. Amjadi, K.-U. Kyung, I. Park, and M. Sitti, “Stretchable, Skin-Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review,” Advanced Functional Materials, vol. 26, no. 11, pp. 1678–1698, 2016. [37] H. Souri et al., “Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications,” Advanced Intelligent Systems, vol. 2, no. 8, p. 2000039, 2020. [38] P. Bifulco et al., “A stretchable, conductive rubber sensor to detect muscle contraction for prosthetic hand control,” 2017 E-Health and Bioengineering Conference (EHB), Sinaia, Romania, 2017, pp. 173-176. [39] T. Tamai, “Electrical Properties of Conductive Elastomer as Electrical Contact Material,” in IEEE Transactions on Components, Hybrids, and Manufacturing Technology, vol. 5, no. 1, pp. 56-61, March 1982. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92703 | - |
| dc.description.abstract | 手勢辨識技術是個非常熱門的研究主題，尤其在人機互動 (Human-Computer Interaction, HCI) 的領域中，手勢辨識的技術可以幫助使用者用更符合直覺的方式操控電腦。本研究著重於開發人機互動中的手勢辨識裝置，功能可以是以手勢向智慧助手下指令、或是比較娛樂取向，做為 VR/AR 系統的輸入端。對於我們的目標而言，希望裝置能有幾項特性：穿戴舒適、隨時隨地都能使用、保有隱私不被窺探、製做成本低。
然而目前存在的研究中，還沒有完全符合我們需求的項目。基於電腦視覺和無線訊號偵測的手勢辨識技術，可移動性 (mobility) 較低，而目前有的穿戴式的手勢辨識手環，可穿戴性 (wearability) 還不夠高。舉例來說，基於肌電 (Electromyography, EMG) 感測的裝置，需要黏貼電極片在手臂上、基於彎曲感測器 (flex sensor) 的裝置，需要配戴有感測器的手套在手上，這些裝置的穿戴方法在日常生活中，對使用者都會造成不便。現有的研究中，最接近我們需求的作品，是一條基於電容感測的手環，它可以偵測手腕輪廓的變化，以此辨識手勢。不過這項作品還是有一項缺點，就是手環使用硬質的矽膠製成，在穿戴上還是不夠舒適，而且其手環結構複雜，製做成本較高。 因此，本研究旨在改良此款手環，沿用手腕輪廓辨識的靈感，但探索使用其它種感測器做辨識的可能性。在嘗試了多種材料後，我們發現了導電橡膠這項材料。導電橡膠為混合碳黑 (carbon black) 粉末的橡膠，具有導電性，且電阻值會因為受力而發生改變，因此我們可以測量橡膠的電阻值並回推裝置受力或是變形情形。導電橡膠柔軟、有彈性，在穿戴時不會造成使用者不適，看似非常適合作為HCI裝置使用。但是實際上少有作品使用此材料，理由是此類應力感測器的電阻變化和伸長比例間並非線性關係，據我們所知目前也還沒有人建立出導電橡膠的電阻變化模型。因此在實驗前，我們也不太確定導電橡膠是否真的適合使用。 在本研究中，我們首先對導電橡膠條進行大量的測量，收集導電橡膠條在不同原長和不同伸展長度下的電阻值並建立模型，發現導電橡膠在伸展量很小的時候，會有非常顯著的電阻變化，也因此認定能使用導電橡膠做為手環上的感測器，決定改以電阻感測的方式做辨識。接著，我們以導電橡膠條和丹寧布製做了手環，並利用前述的電阻模型設計校正數據的演算法，再將校正後的數據輸入Random Forest Classifier 辨識使用者做出的手勢。 | zh_TW |
| dc.description.abstract | Hand gesture recognition is a popular research topic, especially in the field of Human-Computer Interaction (HCI). This study focuses on developing a wearable hand gesture recognition device for human-computer interaction, which can be used to give commands to smart assistants through gestures or be utilized as an input interface for VR/AR systems with entertainment purposes.
We aim to achieve several characteristics: comfort while wearing, usability anytime and anywhere, privacy preservation, and low cost. However, existing projects do not fully meet our requirements. Technologies based on computer vision and wireless signal detection lack mobility, while current wearable gesture recognition wristbands are not wearable enough. The closest existing work to our requirements is a wristband based on capacitive sensing, capable of detecting changes in wrist contours to recognize gestures. Nonetheless, this work has a drawback as the wristband is made from rigid silicone, leading to discomfort during wear, and it has a complex structure, resulting in higher production costs. Therefore, this study aims to improve this wristband design by retaining the idea of wrist contour recognition but using different types of sensors for recognition. After trying various materials, we discovered a material called conductive rubber. Conductive rubber is soft, elastic, and comfortable to wear, making it seemingly suitable for HCI devices. However, few works have utilized this material due to the nonlinearity between resistance change and elongation proportion. In this study, we first conducted extensive measurements on conductive rubber cord to build a model for the resistance change of it. We found that resistance value exhibits significant changes when subjected to small extensions. Based on this discovery, we decided to use conductive rubber cord as a sensor on the wristband and employ a resistance sensing approach for gesture recognition. We then created the wristband using conductive rubber cords and denim fabric. By employing the previously developed resistance model and a calibration algorithm for the collected data, we fed the calibrated data into a Random Forest Classifier to recognize the gestures made by the user. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-06-13T16:06:59Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-06-13T16:06:59Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iv 目次 vi 圖次 x 表次 xii 第一章 簡介 1 1.1 研究動機與方法 1 1.2 系統設計 1 1.3 背景 2 1.3.1 手勢辨識 2 1.3.2 透過手腕外型偵測手勢 4 1.3.3 導電橡膠介紹 5 第二章 相關研究 6 2.1 手腕肌肉 6 2.2 手勢辨識 7 2.3 可伸展感測器 10 2.4 導電橡膠 12 第三章 系統設計與實作 13 3.1 系統概覽 13 3.2 硬體架構 13 3.2.1 Arduino UNO 14 3.2.2 藍芽模組 15 3.2.3 類比數位轉換器 (ADC) 15 3.2.4 導電橡膠 16 3.2.5電路設計 16 3.3 軟體架構 17 3.3.1 Arduino 端 – 收集數據 17 3.3.2 電腦端 python server -- 資料收集 18 3.3.3 資料預處理 18 3.3.4 random forest 手勢辨識 21 3.4 原型製作過程 21 3.4.1 導電布 22 3.4.2 導電橡膠片 23 3.4.3 v1：潛水布製成的手套型手環 24 3.4.4 v2：潛水布製成的手套型手環，且導電橡膠間有做絕緣處理 26 3.4.5 v3：潛水布製成的錶帶型手環 26 3.4.6 v4：丹寧布製成的錶帶型手環 27 第四章 實驗 29 4.1 實驗設計 29 4.1.1 Arduino 程式設計 29 4.1.2 橡膠電阻與長度關係建模 29 4.1.3 收集手勢資料 32 4.1.4 校正數據 33 4.2 實驗數據呈現 35 4.2.1 橡膠電阻與長度建模 35 4.2.2手勢測量出的原始數據 37 4.2.3 以電阻校正後的數據 38 4.2.4 以長度校正後的數據 39 4.2.5 其他版本手環的測量數據 40 4.2.6 Randomized Search CV 選擇參數 43 第五章 實驗結果分析 44 5.1 材料選擇 44 5.1.1 布料 44 5.1.2 感測器 46 5.2 校正 51 5.2.1 未校正辨識結果 51 5.2.2 以電阻校正的辨識結果 53 5.2.3 以長度校正的辨識結果 56 5.3 Random Forest參數選擇 59 第六章 討論 60 6.1 限制 60 6.1.1 持續下降的電阻 60 6.1.2 基準值浮動 61 6.1.3 “Stretch” 做為校正手勢 62 6.2 實務上的可行性 62 6.2.1 本研究裝置與相關研究的比較 62 6.2.2 接續的實驗 63 6.3未來展望 64 6.3.1電源 64 6.3.2在線學習 65 6.3.3 遷移式學習 65 第七章 結論 67 第八章 參考資料 69 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 人機互動 | zh_TW |
| dc.subject | 手勢辨識 | zh_TW |
| dc.subject | 穿戴式裝置 | zh_TW |
| dc.subject | 軟式應力感測器 | zh_TW |
| dc.subject | 導電橡膠 | zh_TW |
| dc.subject | conductive rubber | en |
| dc.subject | human-computer interaction | en |
| dc.subject | hand gesture recognition | en |
| dc.subject | strain sensor | en |
| dc.title | 基於電阻感測的手勢辨識手環 | zh_TW |
| dc.title | Hand Gesture Recognition Wristband Based on Resistance Sensing | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳伶志;林靖茹 | zh_TW |
| dc.contributor.oralexamcommittee | Ling-Jyh Chen;Kate Ching-Ju Lin | en |
| dc.subject.keyword | 人機互動,手勢辨識,穿戴式裝置,軟式應力感測器,導電橡膠, | zh_TW |
| dc.subject.keyword | human-computer interaction,hand gesture recognition,strain sensor,conductive rubber, | en |
| dc.relation.page | 73 | - |
| dc.identifier.doi | 10.6342/NTU202302388 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-04-09 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| 顯示於系所單位： | 電機工程學系 | |
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