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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67640Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 趙福杉 | |
| dc.contributor.author | Yu-Wei Hsu | en |
| dc.contributor.author | 許祐維 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:41:39Z | - |
| dc.date.available | 2021-08-02 | |
| dc.date.copyright | 2017-08-02 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-27 | |
| dc.identifier.citation | [1] N. Noury, P. Rumeau, A.K. Bourke, G. Olaighin, and J.E. Lundy, “A proposal for the classification and evaluation of fall detectors,” Ingnierie et recherche biomdicale (IRBM), Vol. 29, Issue 6, pp. 340–349, December 2008.
[2] M. Tolkiehn, L. Atallah, B. Lo and G. Z. Yang, 'Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor,' Proceedings of 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, 2011, pp. 369-372. [3] F. Bagalà., C. Becker, A. Cappello, L. Chiari, K. Aminian, J. M. Hausdorff, et al, 'Evaluation of accelerometer-based fall detection algorithm in realworld falls', PLoS ONE, vol. 7, no. 5, pp.1-9, May 2012. [4] A. Stisen, H. Blunck, S. Bhattacharya, T.S. Prentow, M.B. Kjærgaard, A. Dey, T. Sonne, and M.M. Jensen, “Smart devices are different: assessing and mitigating mobile sensing heterogeneities for activity recognition,” Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, Korea, 2015, pp 127–140. [5] S. Abbate., M. Avvenuti., F. Bonatesta, G. Cola, P. Corsini, and A. Vecchio, “A smartphone-based fall detection system,” Pervasive and Mobile Computing, vol. 8, issue 6, pp. 883-899, December 2012. [6] J. Dai, X. Bai, Z. Yang, Z. Shen, and D. Xuan, 'Mobile phone-based pervasive fall detection,' Personal Ubiquitous Computing, vol. 14, issue 7, pp. 633-643, October 2010. [7] T. Shi, X. Sun, Z. Xia, L. Chen, and J. Liu, “Fall detection algorithm based on triaxial accelerometer and magnetometer,” Engineering Letters, vol. 24, issue 2, pp. 157-163, 2016. [8] Y. Shi, Y.shi, and X.Wang, “Fall detection on mobile phones using features from a five-phase model,” Proceedings of the 9th International Conference on Ubiquitous Intelligence & Computing and on Autonomic & Trusted Computing (UIC/ATC), Fukuoka, Japan, 2012, pp 951–956. [9] B.T. Nukala, N. Shibuya, A.I. Rodriguez, J. Tsay, T.Q. Nguyen, S. Zupancic, and D.Y.C. Lie, “A real-time robust fall detection system using a wireless gait analysis sensor and an artificial neural network,” Proceedings of Healthcare Innovation Conference (HIC), Seattle, Washington, USA, 2014, pp. 219-222. [10] A. O. Kansiz, M. A. Guvensan, and H. I. Turkmen. 'Selection of time-domain features for fall detection based on supervised learning.' Proceedings of the World Congress on Engineering and Computer Science, vol. 2, San Francisco, CA, USA. 2013, pp. 796-801. [11] O. Aziz, M. Musngi, E. J. Park, G. Mori, S. N. Robinovitch, “A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials,” Medical & biological engineering & computing, pp. 1-11, 2016. [12] K.H. Chen, J.J. Yang, F.S. Jaw, “Accelerometer-based fall detection using feature extraction and support vector machine algorithm,” Instrumentation Science & Technology, vol. 44, issue 4, pp. 333–342, 2016, DOI:10.1080/10739149.2015.1123161. [13] S. Dey, N. Roy,W. Xu, R. R. Choudhury, and S. Nelakuditi.Accelprint: Imperfections of accelerometers make smartphones trackable. Network and Distributed System Security Symp. (NDSS), 2014. [14] A.K. Bourke, P. Van de Ven, M. Gamble, R. O'Connor, K. Murphy, E. Bogan, et al, “Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithm during scripted and continuous unscripted activities,” Journal of biomechanics, vol. 43, issue 15, pp. 3051-3057, November 2010. [15] P. Pierleoni, L. Pernini, A. Belli, L. Palma, S. Valenti and M. Paniccia, “SVM-based fall detection method for elderly people using Android low-cost smartphones,” Proceedings of Sensors Applications Symposium (SAS), Zadar, 2015, pp. 1-5. [16] K.H. Chen, Y.W. Hsu, J.J. Yang, F.S. Jaw, 'Evaluating of the Specifications of Built-In Accelerometers in Smartphones on Fall Detection Performance'. Under review. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67640 | - |
| dc.description.abstract | 隨著人口老化而進入高齡化社會後，年長者的長照是十分重要的議題，其中跌倒之自動偵測在居家照護中是重要的環節，在年長者跌倒後可即時的偵測並通報以減少跌倒長躺後造成之影響，本論文利用智慧型手機內建感測器-加速規來實現跌倒之自動化偵測。
本論文共包含兩個系統，第一個為三特徵值跌倒偵測系統，擷取三個富含物理意義之特徵值送入支持向量機(SVM)作辨識，並使用二種正規化方法，使演算法能適用於不同廠牌之手機。另一個系統為多特徵值跌倒自動偵測系統，希望能更進一步提升第一種方法之準確度，透過計算重力方向上之加速度來獲取更適合辨識跌倒之特徵值，同時設計更符合跌倒模式之trigger key來減少進入運算之次數以節省手機之運算量。 本論文建立了高準確度之跌倒自動偵測系統，亦解決智慧型手機日常使用中沒有固定的配戴位置和方向之問題，並克服手機廠牌間之sensor差異，同時也考量了對智慧型手機合適之運算量和傳輸量。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:41:39Z (GMT). No. of bitstreams: 1 ntu-106-R04548024-1.pdf: 2735005 bytes, checksum: 438a407499c36af8a4ea5e0bc6d9499b (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 第一章 緒論………………………………………………………….. 1
第二章 系統架構……………………………………………………….. 3 2.1第一種系統:三特徵值跌倒偵測系統………………………………….… 4 2.1.1 系統架構與特徵擷取……………………….……………….. 6 2.1.2 辨識過程……………………………….……………………….. 12 2.2 第二種系統:多特徵值跌倒偵測系統………………………………… 16 2.2.1 系統架構與特徵擷取……………………………….………….. 18 2.2.2 辨識過程……………………….……………………………….. 22 第三章 實驗結果…..……………………………………………………….. 23 3.1 三特徵值系統與多支手機辨識實驗結果……………….…….. 26 3.2 多特徵值系統與單支手機辨識結果………………………….…….. 33 第四章 結論與討論……………………………………………………….. 46 參考文獻…………………………………………………………………….…… 48 | |
| dc.language.iso | zh-TW | |
| dc.subject | 正規化 | zh_TW |
| dc.subject | 跌倒偵測 | zh_TW |
| dc.subject | 加速規 | zh_TW |
| dc.subject | 特徵擷取 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 智慧型手機 | zh_TW |
| dc.subject | accelerometer | en |
| dc.subject | fall detection | en |
| dc.subject | support vector machine | en |
| dc.subject | smartphone | en |
| dc.subject | normalize | en |
| dc.subject | feature extraction | en |
| dc.title | 高準確跌倒偵測之智慧型手機演算法 | zh_TW |
| dc.title | Smartphone-based high accuracy algorithm for fall detection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林啟萬,曾乙立 | |
| dc.subject.keyword | 跌倒偵測,加速規,特徵擷取,支持向量機,智慧型手機,正規化, | zh_TW |
| dc.subject.keyword | fall detection,accelerometer,feature extraction,normalize,smartphone,support vector machine, | en |
| dc.relation.page | 49 | |
| dc.identifier.doi | 10.6342/NTU201702021 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-07-28 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| Appears in Collections: | 醫學工程學研究所 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-106-1.pdf Restricted Access | 2.67 MB | Adobe PDF |
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