穿戴式偵測墜落及跌倒裝備於本國建築工地之研發測試

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：67

、訪客IP：3.133.135.8

姓名

詹斯晴(Ssu-Ching Chan) 查詢紙本館藏

畢業系所

土木工程學系在職專班

論文名稱

穿戴式偵測墜落及跌倒裝備於本國建築工地之研發測試
(Development of a Wearable Fall Detection Device for Domestic Construction)

相關論文

★ 時域反射法於土壤含水量與導電度遲滯效應之影響因子探討	★ TDR監測資訊平台之改善與感測器觀測服務之建立
★ 用過核子燃料最終處置場緩衝材料之熱-水耦合實驗及模擬	★ 堰塞壩破壞歷程分析及時域反射法應用監測
★ 深地層最終處置場緩衝材料小型熱-水耦合實驗之分層含水量量測改善	★ 應用時域反射法於地層下陷監測之改善研發
★ 深地層處置場緩衝材料小型熱-水-力耦合實驗精進與模擬比對	★ 淺層崩塌物聯網系統與深層型時域反射邊坡監測技術之整合
★ Modification of TDR Penetrometer for Water Content Profile Monitoring	★ 利用線上遊戲於國小一年級至三年級學童防災教育推廣效益之研究—以桃園防災教育館為例
★ 低放射性最終處置場混合型緩衝材料之工程特性及潛變試驗與模擬	★ Improved TDR Deformation Monitoring by Integrating Centrifuge Physical Modeling
★ 用於滑坡監測的 PS- 和 SBAS-InSAR 處理的參數研究——以阿里山為例	★ 機器學習在水庫入流與濁度預測之應用-以石岡壩為例
★ 深度學習與資料擴增於山崩監測預測之可行性評估	★ 利用光學與熱影像融合進行邊坡之長期穩定性監測評估

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本國中小型營造業自1950年代開始逐漸式微，萬丈高樓從此開始從平地拔起，而勞工安全卻隨著樓層高度，被拋之其後，而其中最大的原因，來自於勞工安全設備被資方認為是隱形成本，若無人員傷亡，就會被視為浪費，導致常常被營造商忽略，從根本來看，若可以有效降低添購及維繫的成本、操作的難易度，以及讓其更容易為人攜帶，則能夠大幅提升營造業對勞工安全設施添購的意願。因此本研究開發一種基於加速度計的跌倒預兆檢測系統，該系統採用基於閾值的檢測方式，由一個小型的自包裝傳感器單元及傳輸控制協定(TCP, Transmission Control Protocol)組成該設備。傳感器單元包括一個三軸加速度計+陀螺儀模組(MPU9250)，並由一套開發版（ESP8266）所組成。本研究設計15組模擬情境實驗，分別為7組跌倒情境及8組日常動作情境，來評估所提出系統的性能，而該系統分別安裝於受試者的頭部、腰部、腳部。結果顯示，雖然加速度計的判別都趨於精準，但頭部及腳部較容易誤將日常生活運動判定為跌倒，而在安裝在腰部的系統則可以得到較高的準確率，因此加速度計閾值設定之門檻為0.25 g。另外本研究採用歐拉角作為第二門檻判釋，減少誤報機率。基於上述成果，建築工人可以方便的攜帶此裝置以獲得三軸的加速度及歐拉角，將獲得的數據計算後傳輸至雲端監控端，依據歷史資料庫之特徵值設定閾值檢測並識別出跌倒或墜落。如果檢測到並識別出跌倒，將傳送訊號至手機，手機將發出警報並自動呼叫緊急聯繫人以進行及時救援。或可透過LORA低功耗長距離無線模式直接傳送訊息至工務所，啟動救援行動。

摘要(英)

Since the 1950s, the small and medium-sized construction industry in Taiwan has gradually declined, the tall buildings have been uprooted from the flat ground, but labor safety has been ignored in the construction. The biggest reason is that the labor safety equipment is considered as an invisible cost by the management. If there are no casualties, it will be regarded as a waste; therefore, the construction industry often ignores it. If it can effectively reduce the cost of purchase, maintenance, and operation difficulties, besides, it is easier for people to carry, it can significantly enhance the willingness of the construction industry to purchase labor safety facilities. However, it still exists many limitations in the existing research and corresponding devices for detecting falls. Therefore, this research develops a fall detection system based on an accelerometer. The system adopts the detection method based on a threshold composed of a small self-packaged sensor unit and Transmission Control Protocol (TCP). The sensor unit includes a three-axis accelerometers + gyroscope module (MPU 9250) and a development version (ESP8266). This study designed 15 groups of simulated situation experiments, including seven fall situations and eight daily activity situations, to evaluate the performance of the proposed system, which was installed on the subjects’ heads, waist, and feet, respectively. The results show that although the accelerometer′s discrimination tends to be accurate, it is easier for the head and feet to judge the movement of daily life as falling mistakenly, and higher accuracy can be obtained in the system installed at the waist. Based on the experiment results, the threshold of the accelerometer threshold is revised as 0.25g. In addition, this study uses Euler angles as the second threshold to reduce the false alarm. Conclusively, construction workers can easily carry this device to obtain the triaxial acceleration and Euler angles, calculate and transmit the data to the cloud-monitoring terminal, set the threshold according to the characteristic values of the historical database then identify falls. If a fall is detected and recognized, a signal will be sent to the mobile phone, which will give an alarm and automatically call the emergency contact for a timely rescue. Alternatively, it can send messages directly to the public works station through LORA low-power long-distance wireless mode to start rescue operations.

關鍵字(中)

★ 穿戴式設備
★ 建築工地跌倒檢測
★ 加速度計
★ 歐拉角

關鍵字(英)

★ Wearable Device
★ Fall Detection
★ Accelerometer
★ Euler Angle

論文目次

摘　要 i
Abstract iii
致　謝 v
目　錄 vii
表目錄 xi
第一章　緒論 1
1.1研究背景與動機 1
1.2研究目的 1
第二章　文獻回顧 3
2.1我國營造業墜落安全防護現況 3
2.1.1國內安全衛生管理情形 3
2.1.2歷來營造業職災統計整理 5
2.1.3歷來我國營造業跌倒、墜落滾落死亡及傷病統計 9
2.1.4中小型營造業職業安全衛生管理現況 10
2.1.5中小型營造業職業安全衛生管理推動問題 16
2.2 國外墜落事件分析 17
2.3 穿戴式跌落偵測裝置研發與應用 20
2.3.1基於可穿戴傳感器的系統（加速度感測器） 20
2.3.2基於環境感應的系統 23
2.3.3光學傳感器(圖像處理法) 25
2.3.4 Apple Watch行動裝置跌倒偵測 27
2.4跌落檢測機制 28
2.4.1閾值檢測法 28
2.4.2物聯網與機器學習法 30
2.5穿戴式裝置在工地中的應用 33
2.6文獻綜合評析 35
第三章研究方法 37
3.1 Arduino穿戴式裝置研發 37
3.1.1開發板選擇 37
3.1.2感測元件 39
3.1.3傳輸系統 40
3.2 研究架構與流程 43
3.3初步人體試驗規劃 44
3.3.1試驗配置 44
3.3.2方向測量定義 44
3.4演算法設定 45
3.4.1雜訊與濾波設定 45
3.4.2閾值設定 48
3.4.3 裝置之軟體分析流程 49
(分析流程與程式碼) 49
3.5 雛型測試規劃 53
3.5.1物理模型測試（傾倒、墜落） 53
3.5.2人體測試（傾倒、墜落） 56
3.6 LORA無線傳輸測試規劃 57
第四章實驗結果與討論 58
4.1 Arduino穿戴式裝置功能 58
4.2物理模型測試結果 59
4.3 人體測試結果 62
4.4 綜合評析 80
4.5 LORA無線傳輸應用測試 85
5.1 結論 89
5.2 建議 89
參考文獻 91
附錄Ａ-女生跌倒情境 96
附錄B-女生日常動作情境 104
附錄C-口試委員意見彙整 113

參考文獻

1. 王珮茹、曾仁杰、陳奕丞、陳映璇，營建工區勞工安全科技化管理之建置先期研究，科技部補助專題研究計畫成果報告，2019，建國科技大學土木工程系暨土木與防災研究所
2. 伍勝民，(2019)，.營造工地安全衛生管理之探討，碩士論文，朝陽科技大學，營建工程所，台中市，台灣。
3. 江川義之、庄司卓郎、中村隆宏，“Change of Working Behaviors by Spreading Safety Information in Construction Sites”，產業安全研究所特別研究報告，第28期，第21-32 頁，2003
4. 吳卓夫，(2005)，灰系統理論應用在建築工地職災發生頻率預測之研究，碩士論文，營建管理研究所，新竹市，台灣。
5. 我國職業災害因素分析與防護策略研究—製造業與營造業，(2019)，勞動部勞動及職業安全衛生研究所
6. 林楨中、戴基福，「營造業勞工不安全行為及其原因之探討」，工業安全衛生月刊，中華民國工業安全衛生協會，第179 卷，第5 期，第46-56 頁，2004。
7. 林祺桓，(2012)，中小型營造業導入職業安全衛生管理之探討，碩士論文，中華大學，營建管理所，新竹市，台灣。
8. 陳盈月，「混凝土橋樑上部結構施工安全之分析與探討—以懸臂式施工法與支撐先進工法為例」，碩士論文，國立台灣科技大學營建工程系，1999。
9. 勞動部職業安全衛生署,(2019),勞動檢查統計年報
10. 馮文政，(2009)，高科技廠房營建階段高處作業防墜措施之探討，碩士論文，工學院工程技術與管理學程，新竹市，台灣。
11. Albert M.V., Kording K., Herrmann M., Jayaraman A. Fall classification by machine learning using mobile phones. PLoS ONE. 2012;7:e36556. doi: 10.1371/journal.pone.0036556.
12. Anderson, D.; Keller, J.M.; Skubic, M.; Chen, X.; He, Z. Recognizing Falls from Silhouettes. In Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, 30 August–3 September 2006; pp. 6388–6391.
13. F. Hossain, M. L. Ali, M. Z. Islam, and H. Mustafa, “A direction-sensitive fall detection system using single 3D accelerometer and learning classifier,” in Proceedings of the 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec), pp. 1–6, Dhaka, Bangladesh, December 2016.
14. F. Wu, H. Zhao, Y. Zhao, and H. Zhong, “Development of a wearable-sensor-based fall detection system,” International Journal of Telemedicine and Applications, vol. 2015, Article ID 576364, 11 pages, 2015.
15. Gasparrini, S.; Cippitelli, E.; Spinsante, S.; Gambi, E. A depth-based fall detection system using a Kinect® sensor. Sensors 2014, 14, 2756–2775.
16. He J., Bai S., Wang X. An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier. Sensors. 2017;17:1393. doi: 10.3390/s17061393.
17. He, J.; Bai, S.; Wang, X. An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier. Sensors 2017, 17, 1393
18. Heinrich, H. W., “Industrial Accident Prevention - A Scientific Approach (4th ed.) ”. :McGraw- Hill Book Company,New York,1959.
19. Ibukun Awolusi, Eric Marks, Matthew Hallowell,(2018).Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices, Automation in Construction ,85 (2018) ,96–106.
20. Ibukun Awolusi, Eric Marks,⁎, Matthew Hallowell,(2018)Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices,Automation in Construction 85 (2018) 96–106
21. J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy, “Wearable sensors for reliable fall detection,” in Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 3551–3554, Shanghai, China, January 2005.
22. Jefiza, E. Pramunanto, H. Boedinoegroho, and M. H. Purnomo, “Fall detection based on accelerometer and gyroscope using back propagation,” in Proceedings of the 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 1–6, Yogyakarta, Indonesia, September 2017.
23. Kurniawan A., Hermawan A.R., Purnama I.K. E. A Wearable Device for Fall Detection Elderly People Using Tri Dimensional Accelerometer; Proceedings of the 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA); Lombok, Indonesia. 28–30 July 2016; pp. 671–674.
24. L. Ciabattoni, G. Foresi, A. Monteriù, D. P. Pagnotta, and L. Tomaiuolo, “Fall detection system by using ambient intelligence and mobile robots,” in Proceedings of the 2018 Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 130-131, Novi Sad, Serbia, May 2018.
25. Lee, Y.S.; Chung, W.Y. Visual sensor based abnormal event detection with moving shadow removal in home healthcare applications. Sensors 2012, 12, 573–584.
26. M. Guvensan, A. Kansiz, N. Camgoz, H. Turkmen, A. Yavuz, and M. Karsligil, “An energy-efficient multi-tier architecture for fall detection on smartphones,” Sensors, vol. 17, no. 7, p. 1487, 2017.
27. M. Shahiduzzaman, “Fall detection by accelerometer and heart rate variability measurement,” Global Journal of Computer Science and Technology, vol. 15, no. 3, 2015.
28. M. V. Albert, K. Kording, M. Herrmann, and A. Jayaraman, “Fall classification by machine learning using mobile phones,” PLoS One, vol. 7, no. 5, Article ID e36556, 2012.
29. M.-S. Lee, J.-G. Lim, and K.-R. Park, “Unsupervised clustering for abnormality detection based on the tri-axial accelerometer,” in Proceedings of the ICCAS-SICE, pp. 134–137, Fukuoka City, Japan, August 2009.
30. Mao, X. Ma, Y. He, and J. Luo, “Highly portable, sensor-based system for human fall monitoring,” Sensors, vol. 17, no. 9, p. 2096, 2017.Özdemir and B. Barshan, “Detecting falls with wearable sensors using machine learning techniques,” Sensors, vol. 14, no. 6, pp. 10691–10708, 2014.
31. Ojetola, O.; Gaura, E.I.; Brusey, J. Fall Detection with Wearable Sensors-SAFE (Smart Fall Detection). In Proceedings of the 7th International Conference on Intelligent Environments, Nottingham, UK, 25–28 July 2011; pp. 318–321
32. P. Kostopoulos, A. I. Kyritsis, M. Deriaz, and D. Konstantas, “F2D: a location aware fall detection system tested with real data from daily life of elderly people,” in Proceedings of the 17th International Conference on E-Health Networking, Application & Services (HealthCom), pp. 397–403, Boston, MA, USA, October 2015.
33. P. Tsinganos Skodras, “On the comparison of wearable sensor data fusion to a single sensor machine learning technique in fall detection,” Sensors (Basel), vol. 18, no. 2, p. 592, 2018.
34. P. Vallabh, R. Malekian, N. Ye, and D. C. Bogatinoska, “Fall detection using machine learning algorithms,” in Proceedings of the 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–9, Split, Croatia, September 2016.
35. Pierleoni P., Belli A., Palma L., Pellegrini M., Pernini L., Valenti S. A high reliability wearable device for elderly fall detection. IEEE Sens. J. 2015;15:4544–4553. doi: 10.1109/JSEN.2015.2423562.
36. Pierleoni, P.; Belli, A.; Palma, L.; Pellegrini, M.; Pernini, L.; Valenti, S. A high reliability wearable device for elderly fall detection. IEEE Sens. J. 2015, 15, 4544–4553.
37. Poi VoonEr,Kok KiangTan.Wearable solution for robust fall detection.Assistive Technology for the Elderly. 2020, Pages 81-105
38. Rougier, C.; Meunier, J.; St-Arnaud, A.; Rousseau, J. Monocular 3D Head Tracking to Detect Falls of Elderly People. In Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, 30 August–3 September 2006; pp. 6384–6387.
39. S. Yu, H. Chen, and R. A. Brown, “Hidden Markov model-based fall detection with motion sensor orientation calibration: a case for real-life home monitoring,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 6, pp. 1847–1853, 2018.
40. S. Zhao, W. Li, W. Niu, R. Gravina, and G. Fortino, “Recognition of human fall events based on single tri-axial gyroscope,” in Proceedings of the IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), pp. 1–6, Zhuhai, China, March 2018.
41. Sorvala, A.; Alasaarela, E.; Sorvoja, H.; Myllyla, R. A Two-Threshold Fall Detection Algorithm for Reducing False Alarms. In Proceedings of the 6th International Symposium on Medical Information and Communication Technology (ISMICT), La Jolla, CA, USA, 25–29 March 2012; pp. 1–4
42. T. B. Rodrigues, D. P. Salgado, M. C. Cordeiro et al., “Fall detection system by machine learning framework for public health,” Procedia Computer Science, vol. 141, pp. 358–365, 2018.
43. T. Chaitep and J. Chawachat, “A 3-phase threshold algorithm for smartphone-based fall detection,” in Proceedings of the 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 183–186, Phuket, Thailand, June 2017.
44. Taramasco, T. Rodenas, F. Martinez et al., “A novel monitoring system for fall detection in older people,” IEEE Access, vol. 6, pp. 43563–43574, 2018.
45. X. Yang, A. Dinh, and L. Che, “A wearable real-time fall detector based on Naive Bayes Classifier,” in Proceedings of the 23rd Canadian Conference on Electrical and Computer Engineering (CCECE 2010), pp. 1–4, Calgary, AB, Canada, May 2010.
46. Y. Choi, A. S. Ralhan, and S. Ko, “A study on machine learning algorithms for fall detection and movement classification,” in Proceedings of the International Conference on Information Science and Applications, pp. 1–8, Jeju Island, South Korea, April 2011.
47. Y. Wang, K. Wu and L. M. Ni, "WiFall: Device-Free Fall Detection by Wireless Networks," in IEEE Transactions on Mobile Computing, vol. 16, no. 2, pp. 581-594, 1 Feb. 2017, doi: 10.1109/TMC.2016.2557792.
48. Yang, L.; Ren, Y.; Hu, H.; Tian, B. New fast fall detection method based on spatio-temporal context tracking of head by using depth images. Sensors 2015, 15, 23004–23019.
49. Yi-Cho Fang, Ren-Jye Dzeng. Accelerometer-based fall-portent detection algorithm for construction tiling operation.Automation in Construction.Volume 84, December 2017, Pages 214-230
50. Young-Hoon Nho, Jong Gwan Lim, Dong-Soo Kwon(2020),Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device, IEEE Access, ,40389 – 40401, DOI：10.1109/ACCESS.2020.2969453
51. Yu, M.; Rhuma, A.; Naqvi, S.M.; Wang, L.; Chambers, J. A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans. Inf. Technol. Biomed. 2012, 16, 1274–1286.

指導教授

鐘志忠(Chung-Chih Chung)

審核日期

2022-1-18

推文