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系統識別號 U0002-1509201414122200
中文論文名稱 適用於智慧型手機之步伐計算演算法之設計與實做
英文論文名稱 A Step Counting Algorithm for Smartphone Users: Design and Implementation
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 102
學期 2
出版年 103
研究生中文姓名 林學偉
研究生英文姓名 Huseh-Wei Lin
學號 601410052
學位類別 碩士
語文別 英文
口試日期 2014-06-16
論文頁數 40頁
口試委員 指導教授-潘孟鉉
委員-黃啟富
委員-鄭建富
委員-潘孟鉉
中文關鍵字 加速度計  步態分析  智慧型手機  步數計算 
英文關鍵字 accelerometer  gaint analysis  smartphone  step counting 
學科別分類 學科別應用科學資訊工程
中文摘要 近幾年來,智慧型手機已成為我們日常生活中的一環,其中,使用者的行走步數對於室內定位和健康照護等是一重要資訊,然而目前所提出的步伐計算大多需要限制使用者以固定放式持有手機,或是於行走中無法自然地使用手機,上述這些限制與一般使用者使用手機行為模式不符且於使用上非常不便。因此在本論文中,我們設計一新穎的步伐計算演算法,可以減輕上述之使用手機的限制且能準確地計算使用者行走步數。我們所提出的演算法可分成兩個部分,第一個部分透過手機中之加速度計蒐集線性加速度和重力加速度,接下來利用重力加速度及座標軸旋轉之概念來取得手機相對於水平面之傾斜角度,得到與水平面之角度後,藉由所計算之角度來推估所感測到之線性加速度數據映射於水平面之線性加速度數值。經由此模組之轉換後,所感測到之線性加速度感測數值將皆以水平方向為基準,接下來偵測使用者可能開始移動的起始點。第二個階段透過相關係數的概念去辨識前述所得之線性加速度資料中是否有相似的趨勢,藉此計算腳步。我們的實做結果顯示,我們所設計之方法可以區分不同的步態且精確地判斷出使用者行走之步數。
英文摘要 In recent years, smartphones have become the most popular devices in our daily lives. The step count is an important information for developing services for smartphone users, e.g., indoor localization and health management. Most step counting solutions restrict that (i) the phone has to be fixed to the user and (ii) the user cannot use the phone naturally while walking. We can see that these restrictions are inconvenient for users. In this paper, we propose an adaptive step calculation algorithm, which can relieve the above restrictions and can count users' steps precisely. The proposed algorithm is composed by two phases. The first phase collects linear acceleration and gravity values from the smartphone's accelerometer. Then, this phase transforms the perceived linear acceleration values to parallel with horizontal plane and identifies possible start points of periodical regular fluctuations (of linear acceleration measurements). The second phase adopts the concept of correlation coefficients to identify whether the collected sensing measurements exhibit similar tendencies, and then calculates step counts. In this work, we implement the proposed method on the Android platform. The experiment results indicate that the proposed scheme can accurately divide gait changes and effectively identify steps.
論文目次 Contents
Chapter 1 Introduction 1
Chapter 2 Related Works 5
Chapter 3 Data Collection Phase 8
3.1 Data Transformation Module 8
3.2 Start Point Detection Module 12
Chapter 4 Data Analysis Phase 14
Chapter 5 Prototyping Results 22
5.1 Some Segmenting Results 22
5.2 Experiment Results 25
Chapter 6 Conclusions 28
Bibliography 29
附錄 31

List of Figures
Figure 1.1: The perceived linear acceleration values when the user (a) holds the smartphone in the right hand and swings her hands naturally with gaits and (b) holds the smartphone in the right hand and watches the screen while walking. 2
Figure 3.1: The linear accelerate values of Z axis before and after transformation. 9
Figure 3.2: The rotations of the point p around (a) the Y axis and (b) the X axis. 11
Figure 3.3: The sets of TPp points derived from Tzusp and Tzlsp. 13
Figure 4.1: An example when a user changes carrying ways. 20
Figure 5.1: Data analysis results of the user (a) holds the phone in the right hand and watches the screen, (b) holds the phone in the right hand and swings naturally with gaits, (c) puts the phone in the right pocket of her pant, (d) holds the phone in the right hand and talks, (e) puts the phone in a back bag, and (f) puts the phone in a handbag while walking. 24

List of Tables
Table 5.1: The experiment results of averaged error on step counts. 26
Table 5.2: The experiment results of averaged error on when the user dynamically changes carrying ways. 26
參考文獻 [1] Fitbit.
https://play.google.com/store/apps/details?id=com.fitbit.FitbitMobile.
[2] Noom walk.
https://play.google.com/store/apps/details?id=com.noom.walk.
[3] M. Alzantot and M. Youssef. UPTIME: Ubiquitous pedestrian tracking using mobile phones. In Proc. of IEEE Wireless Communications and Networking Conference (WCNC), 2012.
[4] P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In Proc. of IEEE INFOCOM, 2000.
[5] H. Bao and W.-C. Wong. An indoor dead-reckoning algorithm with map matching. In Proc. of IEEE Int’l Wireless Communications and Mobile Computing Conference (IWCMC), 2013.
[6] A. Brajdic and R. Harle. Walk detection and step counting on unconstrained smartphones. In Proc. of ACM int’l joint conference on Pervasive and ubiquitous computing (UbiComp), 2013.
[7] J. Chon and H. Cha. Lifemap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing, 10(2):58–67, 2011.
[8] L. Hu and D. Evans. Localization for mobile sensor networks. In Proc. of ACM Int’l Conference on Mobile Computing and Networking(MobiCom), 2004.
[9] W.-Y. Hu, J.-L. Lu, S. Jiang, W. Shu, and M.-Y. Wu. WiBEST: A hybrid personal indoor positioning system. In Proc. of IEEE Wireless Communications and Networking Conference (WCNC), 2013.
[10] A. Jimenez, F. Seco, C. Prieto, and J. Guevara. A comparison of pedestrian dead-reckoning algorithms using a low-cost mems imu. In Proc. of IEEE Int’l Symposium on Intelligent Signal Processing (WISP), 2009.
[11] Y. Kim, H. Shin, and H. Cha. Smartphone-based Wi-Fi pedestrian tracking system tolerating the RSS variance problem. In Proc. of IEEE Int’l Conference on Pervasive Computing and Communications(PerCom), 2012.
[12] C.-C. Lo, C.-P. Chiu, Y.-C. Tseng, S.-A. Chang, and L.-C. Kuo. A walking velocity update technique for pedestrian dead-reckoning applications. In Proc. of IEEE Int’l Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2011.
[13] K. Park, H. Shin, and H. Cha. Smartphone-based pedestrian tracking in indoor corridor environments. Personal and ubiquitous computing, 17(2):359–370, 2013.
[14] A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen. Zee: zero-effort crowdsourcing for indoor localization. In Proc. of ACM Int’l Conference on Mobile Computing and Networking (MobiCom), 2012.
[15] H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. R.Choudhury. No need to war-drive: unsupervised indoor localization. In Proc. of ACM Int’l Conference on Mobile Systems, Applications, and Services (MobiSys), 2012.
[16] R. Zhang, A. Bannoura, F. Hoflinger, L. Reindl, and C. Schindelhauer. Indoor localization using a smart phone. In Proc. of IEEE Sensors Applications Symposium (SAS), 2013.
[17] R. Zhang and L. Reindl. Pedestrian motion based inertial sensor fusion by a modified complementary separate-bias kalman filter. In Proc. of IEEE Sensors Applications Symposium (SAS), 2011
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