AirType: 基於機器學習之空中輸入識別系統｜國立清華大學博碩士論文庫

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研究生：	羅世宏 Lo, Shih-Hong
論文名稱：	AirType: 基於機器學習之空中輸入識別系統 AirType: In-Air Typing-Recognition System Based on Machine Learning
指導教授：	周百祥 Chou, Pai H.
口試委員:	周志遠 Chou, Jerry 蔡明哲 Tsai, Ming-Jer
學位類別：	碩士 Master
系所名稱：
論文出版年：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	37
中文關鍵詞：	嵌入式系統、機器學習、手勢辨識
外文關鍵詞：	Embedded System, Machine Learning, Gesture Recognition
相關次數：	點閱：3 下載：0
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本文提出一系列的演算法用於空中輸入辨識的資料切割處理及分類。我們亦設計一個以動作感測為主的手指穿戴式裝置，該裝置上配備微小的慣性感測器以及附有藍芽功能的微控制器，用於蒐集手指動作的資料和將資料透過藍芽傳送至電腦端進行資料處理。我們提出的演算法主要是進行資料切割、特徵提取以及利用基於機器學習的k-近鄰演算法來進行手勢分類，最後透過將分類結果對應至預先定義好的想像的鍵盤，即可辨認使用者所按下之按鍵。我們實作提出的演算法於我們設計的手指穿戴式裝置上，實驗結果顯示，無論使用是個人化訓練的模型，或者是使用大眾化訓練的模型，我們的演算法能夠達到相當高的準確性。於我們的實驗當中，個人化訓練的模型與大眾化訓練的模型分別可達到90.8%和90.2%。

We propose a series of algorithms for air-typing recognition from data collected by a finger- wearable motion-sensing ring. This finger-worn unit consists of a miniature inertial measurement unit (IMU) and a microcontroller unit (MCU) with an on-chip Bluetooth Low Energy (BLE) transceiver. Our proposed algorithms perform data segmentation, feature extraction, and classification based on k-Nearest Neighbors (kNN) to recognize the gestures and map them into the imaginary keyboard. Experimental results show that our air-typing system can achieve 90.8% and 90.2% on user-dependent and user-independent cases, respectively.

Contents i
Acknowledgments vi
Introduction 1
1 Motivation........................................ 1
2 Contributions ...................................... 2
3 ThesisOrganization................................... 2
Related Work 3
1 Projection-BasedApproach............................... 3
2 Ring-BasedApproach.................................. 4
Background Theory 5
1 K-NearestNeighbors .................................. 5
2 SupportVectorMachine ................................ 6
3 DecisionTree...................................... 7
4 NaiveBayes....................................... 9
5 LogisticRegression................................... 9
Technical Approach 11
1 GestureRepresentation................................. 11
2 MotionSensingSystem................................. 12
3 SegmentationAlgorithm ................................ 13
4 RecognitionAlgorithm ................................. 15
4.1 Normalization ................................. 15
4.2 FeatureExtraction ............................... 15
4.3 PrincipalComponentsAnalysis ........................ 18
4.4 Classification.................................. 19
4.5 KeystrokeIdentification ............................ 20
System Architecture and Implementation 21
1 SystemArchitecture................................... 21
2 NodeSubsystem .................................... 22
2.1 MicrocontrollerUnit .............................. 22
2.2 InertialMeasurementUnit ........................... 22
2.3 BluetoothLowEnergy ............................. 23
3 HostSubsystem..................................... 24
Evaluation 25
1 ExperimentalSetup................................... 25
2 ExperimentalResults .................................. 26
2.1 DimensionalityComparison .......................... 26
2.2 RecognitionAccuracy ............................. 27
2.3 Performance .................................. 30
3 Discussion........................................ 30
Conclusions and Future Work 34
1 Conclusions....................................... 34
2 FutureWork....................................... 34
2.1 Typingonanyplane .............................. 34
2.2 Full-sizedkeyboard............................... 35
2.3 MouseMode .................................. 35
                                

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