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研究生： 官敬堯
Ching-Yao Kuan
論文名稱： 基於加速度計低功耗精確量測步數演算法在穿戴式裝置的實現
Low Power Implementation of an Accurate Accelerometer-Based Step Counting Algorithm in Wearable Device
指導教授： 王淵弘
Yung-Hung Wang
口試委員:
學位類別： 碩士
Master
系所名稱： 工學院 - 機械工程學系
Department of Mechanical Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 55
中文關鍵詞： 量測步數 、計算時間 、自相關函數 、穿戴式裝置 、功耗
外文關鍵詞： step counting, computation time, autocorrelation function, wearable device, power consumption
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    • 近年來，每日的行走步數普遍被視為自我健康監測的指標，市面上已有許多穿戴式裝置都具計算行走步數的功能，若能開發一種低功耗且精確量測步數的演算法，則能夠減少裝置的充電次數，進而提升裝置的方便程度以及延長其電池壽命。我們以自適性自相關函數計步(Adaptive Autocorrelation Step Counting, APASC)演算法為基礎，分析並最佳化其核心演算法自相關函數的計算量，再簡化此演算法的流程，提出一種低功耗的自適性自相關函數計步(Low Power Adaptive Autocorrelation Step Counting, LPAPASC)演算法，我們對此演算法進行了準確度分析，並在穿戴式裝置上實現之。最終，本研究提出的LPAPASC演算法與APASC演算法相比，在長達22小時的資料測試中，其準確率僅降低0.86%。然而，LPAPASC演算法在穿戴式裝置上的執行所需的耗電量卻比APASC演算法降低了88%。

    In recent years, the number of steps taken per day has generally been regarded as an indicator of self-health monitoring. There are many wearable devices on the market track user's daily step count. If an algorithm with low power consumption and accurate step measurement can be developed, the charging times of the device can be reduced, thereby improving the convenience of the device and prolonging its battery life. Based on the Adaptive Autocorrelation Step Counting (APASC) algorithm, we analyze and optimize the calculation amount of the autocorrelation function of its core algorithm, then simplify the process of this algorithm, propose Low Power Adaptive Autocorrelation Step Counting (LPAPASC) algorithm and implemented it in wearable device. Finally, compared with the APASC algorithm, the accuracy of the LPAPASC algorithm proposed in this study is only reduced by 0.86% in the 22-hour data test. However, the power consumption required for the execution of the LPAPASC algorithm in wearable device is 88% lower than that of the APASC algorithm.

    中文摘要........................................i 英文摘要.......................................ii 誌謝..........................................iii 目錄...........................................iv 圖目錄.........................................vi 表目錄........................................vii 符號說明.....................................viii 一、緒論........................................1 1-1研究動機...................................1 1-2文獻回顧...................................1 1-3研究目的...................................3 1-4章節結構...................................3 二、自適性自相關函數計步演算法的基礎...............5 2-1自適性自相關函數計步演算法...................5 2-2演算法的工作記憶體.........................10 2-3演算法的計算量分析.........................10 三、低功耗的自適性自相關計步演算法................15 3-1最佳化自相關函數的計算量....................15 3-1-1降低滯後範圍...........................15 3-1-2降低訊號取樣頻率.......................16 3-1-3最佳化前後比較.........................17 3-2最佳化相似度的計算量.......................18 3-3簡化演算法的流程...........................23 3-4最佳化後的演算法流程.......................24 3-5演算法在最佳化前後之準確率分析..............26 四、嵌入式微控制器實驗..........................30 4-1計步演算法執行時間測試.....................30 4-1-1硬體規格..............................30 4-1-2韌體架構..............................31 4-1-3實驗結果..............................31 4-2計步演算法之功耗計算.......................33 五、總結與結論..................................38 5-1總結......................................38 5-2結論......................................39 參考文獻.......................................40

    ［1］ F. Guo, Y. Li, M. S. Kankanhalli, and M. S. Brown, “An evaluation of wearable activity monitoring devices,” Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia, pp. 31-34, 2013.
    ［2］ Fitbit. https://www.fitbit.com (accessed May 17, 2023)
    ［3］ Xiaomi. https://www.mi.com (accessed May 17, 2023)
    ［4］ Apple. https://www.apple.com (accessed May 17, 2023)
    ［5］ J. Burns, A. Kassam, N. Sinha, L. Downie, L. Solnickova, B. Way, and J. Dahn, “Predicting and extending the lifetime of Li-ion batteries,” Journal of The Electrochemical Society, vol. 160, no. 9, pp. A1451, 2013.
    ［6］ Y.-H. Wang, I.-Y. Chen, H. Chiueh, and S.-F. Liang, “A low-cost implementation of sample entropy in wearable embedded systems: An example of online analysis for sleep eeg,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-12, 2021.
    ［7］ Z.-E Xu, “Accelerometer-based accurate step counting algorithm,” Master, Department of Mechanical Engineering, National Central University, Taoyuan, 2023. https://etd.lib.nctu.edu.tw/cgi-bin/gs32/ncugsweb.cgi?o=dncucdr&s=id=%22GC110323073%22.&searchmode=basic
    ［8］ Y.-S. Liu, “Actigraphy platform for objective and long-term sleep assessment,” Master, Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, 2020. https://hdl.handle.net/11296/d76gme
    ［9］ C.-X. Li, “Development and evaluation of a wake-sleep identification method for wearable embedded systems,” Master, Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, 2021. https://hdl.handle.net/11296/9237yt
    ［10］ J. J. Shynk, “Adaptive IIR filtering,” IEEE Assp Magazine, vol. 6, no. 2, pp. 4-21, 1989.
    ［11］ L. Rabiner, “On the use of autocorrelation analysis for pitch detection,” IEEE transactions on acoustics, speech, and signal processing, vol. 25, no. 1, pp. 24-33, 1977.
    ［12］ A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, “Zee: Zero-effort crowdsourcing for indoor localization,” Proceedings of the 18th annual international conference on Mobile computing and networking, pp. 293-304, 2012.
    ［13］ W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C: the art of scientific computing, Second Edition. Cambridge university press Cambridge, 1992.
    ［14］ T.-H. Hsieh, M.-H. Liu, C.-E. Kuo, Y.-H. Wang, and S.-F. Liang, “Home-use and real-time sleep-staging system based on eye masks and mobile devices with a deep learning model,” Journal of medical and biological engineering, vol. 41, pp. 659-668, 2021.

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