臺灣博碩士論文加值系統

高齡者人口比例逐年上升，使好發於高齡者的跌倒成為嚴重議題，發生時易使高齡者受傷甚至死亡。為防止跌倒碰撞造成的損傷，需在跌倒前辨識出跌倒發生，並偵測辨識至碰撞之領先時間以提供防護所需。過去研究有採用動作擷取系統及肌電訊號進行辨識跌倒，但缺乏應用上的便利性；近年多採用慣性感測器偵測跌倒，但遠不及智慧行動裝置普及，若能結合含慣性感測晶片的智慧行動裝置，將大幅增加應用的可行性。過去研究之跌倒型式大多使受測者主動傾倒，僅有少數針對非預期跌倒，尤其針對跌倒發生率最大的非預期性滑倒，更無研究對此進行跌倒辨識與防護探討。因此本研究模擬出跌倒中較大比例的非預期絆倒與滑倒實驗，以真實重現生活中常發生的跌倒情況。本研究整合一跌倒辨識與防護系統，採用普及的智慧行動裝置針對非預期絆倒與滑倒進行即時跌倒辨識，並確認領先時間是否足夠用於觸發防護裝置所需以即時防護跌倒。
儀器使用具備三軸加速規與陀螺儀晶片的智慧型手機作為感測器，招募12位健康男性受測者(1.69±0.03 m; 67.33±7.52kg; 24.30±2.02 yrs)於模擬居家實驗空間進行日常生活動作實驗，分別為坐、坐到站、蹲、蹲到站、上下床、走、上下樓梯與慢跑各10次試驗，並建置絆倒與滑倒步道，進行正常速度行走中各1次非預期絆倒與滑倒。搭配手機作業系統開發程式紀錄各動作三軸加速度與角速度後分析疊加數值與跌倒時間。跌倒閾值設定為三軸疊加加速度4.6m/s2與人體向前與側向翻轉方向角速度疊加3.6rad/s，辨識跌倒的靈敏度為90.24%，特異性為94.75%，用於觸發防護系統之領先時間絆倒為173.00±52.80毫秒，最小值為101毫秒，排除坐下型滑倒後為156.50±18.34毫秒，最小值為135毫秒，95%信賴區間為(119.82,193.18)，而坐下型滑倒僅為94.71±27.96毫秒，最小值為51毫秒，95%信賴區間為(38.79,150.63)，範圍符合市售防護氣囊最小觸發時間35毫秒範圍。設定100毫秒做為觸發防護時間防護絆倒與躺下型滑倒，坐下型滑倒至少50毫秒之預先防護效果。招募30位男性受測者(1.69±0.04 m; 63.17±7.37kg; 24.36±2.04yrs)測試即時跌倒辨識，進行非預期絆倒與滑倒各一次，辨識訊號由聲響表示，絆倒靈敏度為100%，滑倒為91.67%。時間分析中，絆倒與躺下行滑倒領先時間為175.96±53.07毫秒，最小值為100毫秒，95%信賴區間為(69.81,282.10)。坐下型滑倒領先時間為107.71±45.51毫秒，最小值為67毫秒，95%信賴區間為(13.68,195.74)，部分坐下滑倒可能小於市售35毫秒防護，故皆須採用預先防護效果。結果顯示跌倒辨識靈敏度與特異性皆高於90%，擁有良好使用成效，且本研究達成使用智慧行動裝置即時偵測非預期跌倒，並提供過去未針對的非預期滑倒辨識進行分析。即時辨識實驗中驗證一般使用者使用時亦效果良好，保護時間大多亦能符合市售防護氣囊最小觸發時間35毫秒範圍，確立可用於即時防跌。

Injuries and deaths always occur in the elderly when falls happened. The utilization of inertial measurement units (IMU) for fall detector could recognize fall and even prevent injuries caused by fall impact during activities of daily living (ADL). Studies have tried methods of IMUs using, real-time detecting in falls and triggering unexpected falls. They were researched separately so the present study tries to use a smart phone which includes IMUs and operating system to act as a detector with fall recognition, trigger signal output and real-time recognition. This is because it is highly portable and easy to use. This research also focus on real-time slip detection those previous studies did not mentioned.
12 young healthy subjects (height: 1.69±0.03m; weight: 67.33±7.52kg; age: 24.30±2.02yrs) performed ADL and encountered unexpected falls. Trunk motion through acceleration (in accelerometer) and angular velocities (in gyroscope) were measured through a smart phone. The acceleration and velocity of trunk motion were measured by the accelerometer and gyroscope in smart phone. In fall experiments, subjects were asked to walk on a path with designed mechanisms to trigger trip and oily spilled in trigger slip.
Thresholds in smart phone were set to 4.6 m/s2 in 3 axis superposition accelerations and 3.6 rad/s in pitch, roll superposition angular velocities. Time delay between 2 thresholds was set 225ms to ensure trip and slip are true. Falls distinguishing from ADL showed 90.24% sensitivity and 94.75% specificity. Lead time of trip and slip (lying down) is 156.50±18.34 ms. The minimum is135 ms. 95%CI is(119.82,193.18) ms. Lead time of slip(sitting) is 94.71±27.96 ms. The minimum is 51ms. 95% CI is (38.79, 150.63) ms. All of above lead time are smaller than 35 ms that can suit protector triggering on the market.
30 young healthy subjects (height: 1.69±0.04m; weight: 63.17±7.37kg; age: 24.36±2.04yrs) performed real-time fall recognition tests and the device showed 100% sensitivity in trips and 91.67% sensitivity in slips. The signal of recognition is presented as a voice. Lead time of trip and slip (lying down) is 175.96±53.07 ms. The minimum is100 ms. 95%CI is (69.81, 282.10) ms. Lead time of slip (sitting) is 107.71±45.51 ms. The minimum is 67ms. 95% CI is (13.68,195.74) ms. Pre-protection is needed because part of lead time smaller than 35 ms. The real-time recognition is corresponded to the experiment of the previous stage on ADL and falls distinguishing.
Results show high sensitivity, specificity and enough lead time to trigger protector in unexpected fall through a smart phone. It not only showed high accuracy but provides convenience for general user. The system is better than previous studies which use less conditions and is suitable for those who living in a home environment. This research also analyzed unexpected slip to understand kinds of slip and their impact.

中文摘要 iii
英文摘要 iv
目錄 v
常見用字中英文對照表 vii
圖目錄 ix
一、 緒論 1
1.1 研究背景 1
1.2 跌倒辨識系統 4
1.3 智慧行動裝置 7
二、 文獻回顧 9
2.1 跌倒定義與分類 9
2.2 跌倒預防 12
2.3 非預期跌倒模擬 13
2.4 辨識準確率 15
2.5 慣性感測器為基礎之跌倒辨識 17
2.6 智慧行動裝置應用及發展 20
2.7 儀器擺放位置 23
2.8 研究目標 24
三、 研究方法 27
3.1 前置實驗 28
3.1.1 手機用途測試 28
3.1.2 干擾測試 29
3.2 日常生活動作與跌倒測試 29
3.2.1實驗受測者 30
3.2.3實驗流程 36
3.2.4 數據分析 37
3.2.5 個人化設定評估 41
3.3使用演算法與系統測試非預期性絆倒與滑倒成效實驗 41
3.3.1 演算法選用 41
3.3.2 實驗受測者 42
3.3.3實驗流程 43
3.4即時感測與防護效能評估 44
3.4.1 防護裝置製作設計與評估 44
3.4.2 防護裝置啟動時間測試 44
四、 研究結果 45
4.1 前置實驗結果 46
4.1.1 手機用途分析 46
4.1.2 干擾分析 52
4.2 日常生活動作數據 54
4.3 跌倒數據 64
4.4 辨識閾值設定 70
4.5 辨識能力分析 71
4.5.1 辨識效果 71
4.5.2 辨識時間 79
4.5.3 跌倒辨識之靈敏度與特異性 85
4.6 辨識系統設定與流程 85
4.7 個人化分析與使用流程 87
4.8 辨識系統成效分析 89
4.9 系統架構與流程 94
五、 討論 97
六、 結論 112
七、 未來研究 114
參考文獻 115

Alexander, B. H., F. P. Rivara, et al. (1992). "The cost and frequency of hospitalization for fall-related injuries in older adults." American Journal of Public Health 82(7): 1020-1023.

Altman, D. G. and J. M. Bland (1994). "Diagnostic tests. 1: Sensitivity and specificity." British Medical Journal 308(6943): 1552.

Arfken, C. L., H. W. Lach, et al. (1994). "The prevalence and correlates of fear of falling in elderly persons living in the community." American Journal of Public Health 84(4): 565-570.

Begg, R., R. Best, et al. (2007). "Minimum foot clearance during walking: strategies for the minimisation of trip-related falls." Gait &; Posture 25(2): 191-198.

Berg, W. P., H. M. Alessio, et al. (1997). "Circumstances and consequences of falls in independent community-dwelling older adults." Age and Ageing 26(4): 261.

Bhatt, T., J. Wening, et al. (2005). "Influence of gait speed on stability: recovery from anterior slips and compensatory stepping." Gait &; Posture 21(2): 146-156.

Bianchi, F., S. J. Redmond, et al. (2010). "Barometric pressure and triaxial accelerometry-based falls event detection." IEEE Transactions on Neural Systems and Rehabilitation Engineering 18(6): 619-627.

Bourke, A. and G. Lyons (2008). "A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor." Medical Engineering &; Physics 30(1): 84-90.

Bourke, A., J. O'brien, et al. (2007). "Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm." Gait &; Posture 26(2): 194-199.

Bourke, A., K. O’Donovan, et al. (2008). "The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls." Medical Engineering &; Physics 30(7): 937-946.

Bourke, A. K., P. W. J. van de Ven, et al. (2008). Design and test of a long-term fall detection system incorporated into a custom vest for the elderly. Signals and Systems Conference, Irish, The Institution of Engineering and Technology.

Buchner, D., M. Hornbrook, et al. (1993). "Development of the common data base for the FICSIT trials." Journal of the American Geriatrics Society 41(3): 297.

Carey, B. J. and J. F. Potter (2001). "Cardiovascular causes of falls." Age and Ageing 30(suppl 4): 19.

Carter, N. D., P. Kannus, et al. (2001). "Exercise in the prevention of falls in older people: a systematic literature review examining the rationale and the evidence." Sports Medicine 31(6): 427-438.

Carter, N. D., K. M. Khan, et al. (2002). "Community-based exercise program reduces risk factors for falls in 65-to 75-year-old women with osteoporosis: randomized controlled trial." Canadian Medical Association Journal 167(9): 997-1004.

Chambers, A. J. and R. Cham (2007). "Slip-related muscle activation patterns in the stance leg during walking." Gait &; Posture 25(4): 565-572.

Cho, H., S. Kim, et al. (2012). Motion recognition with smart phone embedded 3-axis accelerometer sensor. IEEE International Conference on Systems, Man, and Cybernetics, Seoul, Korea, IEEE.

Courtney, T. K., G. S. Sorock, et al. (2001). "Occupational slip, trip, and fall-related injuries–can the contribution of slipperiness be isolated?" Ergonomics 44(13): 1118-1137.

DeGoede, K. M., J. A. Ashton-Miller, et al. (2003). "Fall-related upper body injuries in the older adult: a review of the biomechanical issues." Journal of Biomechanics 36(7): 1043-1053.

Fukaya, K. and M. Uchida (2008). "Protection against impact with the ground using wearable airbags." Industrial Health 46(1): 59-65.

Gibson, M. J. S., R. O. Andres, et al. (1987). "The prevention of falls in later life." Danish Medical Bulletin 34(Suppl 4): 1-24.

Gillespie, W. J., L. D. Gillespie, et al. (2009). "Hip protectors for preventing hip fractures in older people." Cochrane Database of Systematic Reviews 10(3): 1-40.

Harada, A., M. Mizuno, et al. (2001). "Hip fracture prevention trial using hip protectors in Japanese nursing homes." Osteoporosis International 12(3): 215-221.

Hartholt, K. A., J. A. Stevens, et al. (2011). "Increase in Fall-Related Hospitalizations in the United States, 2001-2008." Journal of Trauma-Injury Infection and Critical Care 71(1): 255-258.

Hartikainen, S., E. Lonnroos, et al. (2007). "Medication as a risk factor for falls: critical systematic review." The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 62(10): 1172.

Horak, F. B. (2006). "Postural orientation and equilibrium: what do we need to know about neural control of balance to prevent falls?" Age and Ageing 35(suppl 2): ii7-ii11.

Hsiao, E. and S. Robinovitch (1997). "Common protective movements govern unexpected falls from standing height." Journal of Biomechanics 31(1): 1-9.

Iso, T. and K. Yamazaki (2006). Gait analyzer based on a cell phone with a single three-axis accelerometer. Human-computer interaction with mobile devices and services, Helsinki, Finland, Association for Computing Machinery.

Kangas, M., A. Konttila, et al. (2008). "Comparison of low-complexity fall detection algorithms for body attached accelerometers." Gait &; Posture 28(2): 285-291.

Kangas, M., A. Konttila, et al. (2007). Determination of simple thresholds for accelerometry-based parameters for fall detection. 29th Annual International Conference of the IEEE EMBS, Lyon, France, IEEE.

Kannus, P., J. Parkkari, et al. (1999). "Fall-induced injuries and deaths among older adults." JAMA: the Journal of the American Medical Association 281(20): 1895-1899.

Kannus, P., J. Parkkari, et al. (2005). "Fall-induced deaths among elderly people." American Journal of Public Health 95(3): 422-424.

Karantonis, D. M., M. R. Narayanan, et al. (2006). "Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring." IEEE Transactions on Information Technology in Biomedicine 10(1): 156-167.

Kelsey, J. L., S. D. Berry, et al. (2010). "Indoor and outdoor falls in older adults are different: the maintenance of balance, independent living, intellect, and Zest in the Elderly of Boston Study." Journal of the American Geriatrics Society 58(11): 2135-2141.

Kim, K. J. and J. A. Ashton-Miller (2003). "Biomechanics of fall arrest using the upper extremity: age differences." Clinical Biomechanics 18(4): 311-318.

Krenzel, D., S. Warren, et al. (2012). Wireless slips and falls prediction system. 34th Annual International Conference of the IEEE EMBS, San Diego, California USA, IEEE.

Lach, H., A. Reed, et al. (1991). "Falls in the elderly: reliability of a classification system." Journal of the American Geriatrics Society 39(2): 197.

Lai, C. F., Y. M. Huang, et al. (2010). "Adaptive body posture analysis for elderly-falling detection with multisensors." Intelligent Systems 25(2): 20-30.

Lamb, S. E., E. C. Jørstad‐Stein, et al. (2005). "Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus." Journal of the American Geriatrics Society 53(9): 1618-1622.

Lord, C. J. and D. P. Colvin (1991). Falls in the elderly: Detection and assessment. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE. 13: 1938-1939.

Lord, S. R., H. B. Menz, et al. (2006). "Home environment risk factors for falls in older people and the efficacy of home modifications." Age and Ageing 35(suppl 2): ii55-ii59.

Luukinen, H., M. Herala, et al. (2000). "Fracture risk associated with a fall according to type of fall among the elderly." Osteoporosis International 11(7): 631-634.

MacKinnon, C. D. and D. A. Winter (1993). "Control of whole body balance in the frontal plane during human walking." Journal of Biomechanics 26(6): 633-644.

Marigold, D. S. and A. E. Patla (2002). "Strategies for dynamic stability during locomotion on a slippery surface: effects of prior experience and knowledge." Journal of Neurophysiology 88(1): 339.

Masud, T. and R. O. Morris (2001). "Epidemiology of falls." Age and Ageing 30(suppl 4): 3.

Means, K. M., D. E. Rodell, et al. (1996). "Rehabilitation of elderly fallers: Pilot study of a low to moderate intensity exercise program." Archives of Physical Medicine and Rehabilitation 77(10): 1030-1036.

Menz, H. B., S. R. Lord, et al. (2003). "Age related differences in walking stability." Age and Ageing 32(2): 137.

Morse, J. (2008). Preventing patient falls. New York, NY, Springer Publishing Company.

Nagata, H. and H. Ohno (2007). "Analysis of backward falls caused by accelerated floor movements using a dummy." Industrial health 45(3): 462-466.

Nyan, M., F. Tay, et al. (2006). "Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization." Medical Engineering &; Physics 28(8): 842-849.

Nyan, M., F. E. H. Tay, et al. (2008). "A wearable system for pre-impact fall detection." Journal of Biomechanics 41(16): 3475-3481.

Pai, Y. C., M. W. Rogers, et al. (1998). "Static versus dynamic predictions of protective stepping following waist–pull perturbations in young and older adults." Journal of Biomechanics 31(12): 1111-1118.

Perolle, G., P. Fraisse, et al. (2006). Automatic fall detection and activity monitoring for elderly. Proceedings of Med-e-Tel, Luxembourg.

Pijnappels, M., M. F. Bobbert, et al. (2005). "How early reactions in the support limb contribute to balance recovery after tripping." Journal of Biomechanics 38(3): 627-634.

Roudsari, B. S., B. E. Ebel, et al. (2005). "The acute medical care costs of fall-related injuries among the US older adults." Injury 36(11): 1316-1322.

Salkeld, G., I. D. Cameron, et al. (2000). "Quality of life related to fear of falling and hip fracture in older women: a time trade off study." British Medical Journal 320(7231): 341-345.

Scheffer, A., M. Schuurmans, et al. (2008). "Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons." Age and Ageing 37(1): 19.

Shany, T., S. J. Redmond, et al. (2012). "Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls." Sensors Journal 12(3): 658-670.

Shi, G., C. S. Chan, et al. (2006). Development of a human airbag system for fall protection using MEMS motion sensing technology. International Conference on Intelligent Robots and Systems, Beijing, China, IEEE.

Smeesters, C., W. C. Hayes, et al. (2001). "Disturbance type and gait speed affect fall direction and impact location." Journal of Biomechanics 34(3): 309-317.

Sposaro, F. and G. Tyson (2009). iFall: An android application for fall monitoring and response. 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA,, IEEE.

Stevens, J., K. Mack, et al. (2008). "Self-reported falls and fall-related injuries among persons aged 65 years–United States, 2006." Journal of Safety Research 39: 345-349.

Studenski, S., S. Perera, et al. (2011). "Gait speed and survival in older adults." JAMA: the Journal of the American Medical Association 305(1): 50-58.

Tamura, T., T. Yoshimura, et al. (2009). "A wearable airbag to prevent fall injuries." IEEE Transactions on Information Technology in Biomedicine 13(6): 910-914.

Thelen, D. G., L. A. Wojcik, et al. (1997). "Age differences in using a rapid step to regain balance during a forward fall." The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 52(1): M8.

Tideiksaar, R. (2002). Falls in older people: Prevention &; management, Health Professions Press.

Tinetti, M. E., D. I. Baker, et al. (1994). "A multifactorial intervention to reduce the risk of falling among elderly people living in the community." New England Journal of Medicine 331(13): 821-827.

Tinetti, M. E. and C. S. Williams (1998). "The effect of falls and fall injuries on functioning in community-dwelling older persons." The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 53(2): M112-M119.

Tromp, A. M., S. M. F. Pluijm, et al. (2001). "Fall-risk screening test: A prospective study on predictors for falls in community-dwelling elderly." Journal of Clinical Epidemiology 54(8): 837-844.

Urton, M. M. (1991). "A community home inspection approach to preventing falls among the elderly." Public Health Reports 106(2): 192.

van de Ven, P., A. Bourke, et al. (2009). Integration of a suite of sensors in a wireless health sensor platform. IEEE sensors 2009 Conference, Christchurch, New Zealand, IEEE.

Vellas, B., S. Wayne, et al. (1997). "Fear of falling and restriction of mobility in elderly fallers." Age and Ageing 26(3): 189.

Wickham, C., C. Cooper, et al. (1989). "Muscle strength, activity, housing and the risk of falls in elderly people." Age and Ageing 18(1): 47.

Winter, D. A. (1995). "Human balance and posture control during standing and walking." Gait &; Posture 3(4): 193-214.

Wolf, S. L., H. X. Barnhart, et al. (1996). "Reducing frailty and falls in older persons: an investigation of Tai Chi and computerized balance training. Atlanta FICSIT Group. Frailty and Injuries: Cooperative Studies of Intervention Techniques." Journal of the American Geriatrics Society 44(5): 489.

Wu, G. (2000). "Distinguishing fall activities from normal activities by velocity characteristics." Journal of Biomechanics 33(11): 1497-1500.

Wu, G. and S. Xue (2008). "Portable preimpact fall detector with inertial sensors." Neural Systems and Rehabilitation Engineering, IEEE Transactions on 16(2): 178-183.

Zhang, T., J. Wang, et al. (2006). "Fall detection by embedding an accelerometer in cellphone and using KFD algorithm." International Journal of Computer Science and Network Security 6(10): 277.

王致中 (2009). 開發一利用慣性感測器與肌電訊號分辨日常生活與跌倒的偵測系統. 機械工程研究所, 國立交通大學. 碩士.

行政院主計處 (2005). 民國94年社會發展趨勢調查-健康安全. 行政院主計處.

行政院衛生署國民健康局 (2007). 民國96年中老年身心社會生活狀況長期追蹤調查(第六次). 行政院衛生署國民健康局.

行政院衛生署國民健康局 (2009). 老人健康促進計畫 (2009-2012). 行政院衛生署國民健康局.

行政部主計處 (2010). 2010人口比例統計. 行政部主計處.

李永捷 (2010). 建置於 IPhone 上之車禍與跌倒自動通報系統. 資訊工程學研究所, 靜宜大學. 碩士.

廖顯庭 (2011). A wearable fall detector using inertial sensors and electromyography. 機械工程研究所, 國立交通大學. 碩士.

鄭以晨, 曾雅梅, et al. (2011). "台灣 2009 年 65 歲以上老人跌墜傷患住院醫療利用及影響因子之探討." 臺灣老人保健學刊 7(1): 55-71.

薛元昀 (2010). 運用智慧型手機於跌倒偵測與緊急通報. 資訊工程學研究所, 國立台灣大學. 碩士.

顧馨文、曾維貞 (2010). MEMS 運動感測器在新興人機介面之應用發展趨勢. 南港IC設計育成中心.