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研究生:林昱安
研究生(外文):Lin, Yu-An
論文名稱:穿戴式裝置於陸上與水中運動之信效度檢驗
論文名稱(外文):Measurement of Reliability and Validity of Wearable Sensor for Land and Aquatic Physical Activities
指導教授:李恆儒李恆儒引用關係
指導教授(外文):Lee, Heng-Ju
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:體育學系
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:41
中文關鍵詞:穿戴式裝置效度信度
外文關鍵詞:wearable sensorvalidityreliability
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前言:近年來運動風氣盛行,適當的調整運動強度可以有效的提升運動效果,穿戴式感測器能有效的監控身體活動數值,但目前較少數感測器能運用在陸上與水中環境,且能準確地反應數值。目的:探討穿戴式裝置在陸上與水中運動時,各測量數值的信效度。方法:以10名健康人與10名大專甲組游泳選手分別為陸上運動組和水中運動組進行實驗。陸上運動組於跑步機上進行固定速度(3km/h、10km/h)的實驗,利用十台Vicon紅外線攝影機 (200 Hz)擷取運動時的步頻與髖關節角度、Delsys感測器擷取運動時的衝擊,再於田徑場進行固定頻率(120step/min、90step/min)以及固定步輻(100cm/stride、140cm/stride)實驗;水中運動組進行50公尺游泳,由2名檢測員分別計算划手次數和游泳秒數,使用攝影機拍攝運動時的划幅、划頻。以Pearson積差相關來檢測穿戴式裝置與實驗儀器間的效標關聯效度,並以ICC來檢測穿戴式裝置的再測信度,統計水準α皆訂為.05。結果:在陸上運動中,走路時的數據(步頻、衝擊、髖關節活動範圍)與跑步時的步頻與衝擊皆有高度相關(r=0.89-0.99),在跑步時,髖關節活動範圍呈現中度相關(r=0.66);在水中運動時的秒數、划手次數與划幅皆有高度相關(r=0.86-0.88),在划頻則呈現中度相關(r=0.55),感測裝置在陸上運動的數據(步頻、衝擊、髖關節活動範圍)的信度考驗皆有高度再測信度(ICC=0.98-0.99),而在水中的數值,秒數為中度再測信度(ICC=0.65),划手次數為低度再測信度(ICC=0.56)。
Introduction: Wearable sensor could measure exercise intensity to provide instant feedback. However, very few sensors could accurately measure values both in land and aquatic environment. Purpose:The aim of this study was to assess validity and reliability of wearable sensor usage in land and aquatic environment. Methods:10 healthy subjects and 10 college swimmer were recruited for this study. In land environment, the subjects required to walk and run on treadmill with different speeds (3km/h、10km/h). Ten Vicon cameras (200 Hz) were used to capture marker trajectory to calculate gait cadence and joint range of motion. One Delsys sensor was used to capture foot impacts when walking at different speeds (120steps/min、90steps/min) and different stride distance (100cm/stride、140cm/stride). In aquatic environment, the subjects required to swim 50m with freestyle. The stroke distance and stroke rate were captured by camera. Pearson correlation coefficient was used to for statistical analysis. ICC represented the reliability of measured variables. Results: In land environment, walking and running parameters were highly correlated (r=0.89-0.99). The range of motion during running was moderately correlated In aquatic environment, swim time、stroke and stroke distance were highly correlated (r=0.86-0.88). The stroke rate was moderately correlated. In land environment, all parameters were highly correlated (ICC=0.98-0.99). In aquatic environment, swim time and stroke were moderately correlated.
中文摘要 i
英文摘要 ii
目次 iii
圖次 v
表次 vi

第壹章 緒論 1
第一節、前言 1
第二節、問題背景 2
第三節、研究目的 3
第四節、研究假設 3
第五節、研究範圍與限制 3
第六節、名詞操作性定義 4

第貳章 文獻探討 5
第一節、身體活動量化概念 5
第二節、感測器於身體活動的運應用 6
第三節、穿戴式裝置的應用 8
第四節、文獻總結 9

第參章 研究方法 10
第一節、研究參與者 10
第二節、研究工具 10
第三節、實驗實施程序 14
第四節、資料處理 20
第五節、統計分析 22

第肆章 結果 23
第一節、穿戴式裝置的信度 23
第二節、穿戴式裝置與效標與效標間的效度 24

第伍章 討論與結論 31
第一節、穿戴式裝置在陸上運動狀態的信效度 31
第二節、穿戴式裝置在水中運動狀態的信效度 32
第三節、結論 33

引用文獻 34
附錄 40
附錄一 游泳秒數的平均數與標準差 40
附錄二 游泳秒數的平均數與標準差 41
Aziz, O., & Robinovitch, S. N. (2011). An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 19(6), 670-676.

Bayat, R., Barbeau, H., & Lamontagne, A. (2005). Speed and temporal-distance adaptations during treadmill and overground walking following stroke. Neurorehabilitation and neural repair, 19(2), 115-124.

Bize, R., Johnson, J. A., & Plotnikoff, R. C. (2007). Physical activity level and health-related quality of life in the general adult population: a systematic review. Preventive medicine, 45(6), 401-415.

Blair, S. N., & Connelly, J. C. (1996). How much physical activity should we do? The case for moderate amounts and intensities of physical activity. Research quarterly for exercise and sport, 67(2), 193-205.

Bohannon, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and ageing, 26(1), 15-19.

Brand, R. A., & Crowninshield, R. D. (1981). Locomotion studies—Caves to computers. Journal of biomechanics, 14(7), 497.

Brown, D. R., Carroll, D. D., Workman, L. M., Carlson, S. A., & Brown, D. W. (2014). Physical activity and health-related quality of life: US adults with and without limitations. Quality of Life Research, 23(10), 2673-2680.

Brown, H. E., Pearson, N., Braithwaite, R. E., Brown, W. J., & Biddle, S. J. (2013). Physical activity interventions and depression in children and adolescents. Sports medicine, 43(3), 195-206.

Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public health reports, 100(2), 126.

Ceesay, S. M., Prentice, A. M., Day, K. C., Murgatroyd, P. R., Goldberg, G. R., Scott, W., & Spurr, G. (1989). The use of heart rate monitoring in the estimation of energy expenditure: a validation study using indirect whole-body calorimetry. British Journal of Nutrition, 61(02), 175-186.

Chen, K. Y., & Bassett, D. R. (2005). The technology of accelerometry-based activity monitors: current and future. Medicine and science in sports and exercise, 37(11), S490.

Committee, P. A. G. A. (2008). Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services, 2008, A1-H14.

Edwards, J. (2012). Wireless Sensors Relay Medical Insight to Patients and Caregivers. IEEE Signal Processing Magazine, 1053(5888/12).

Gage, J. R. (1994). The clinical use of kinetics for evaluation of pathological gait in cerebral palsy. The Journal of Bone & Joint Surgery, 76(4), 622-631.

Hagem, R. M., O'Keefe, S. G., Fickenscher, T., & Thiel, D. V. (2013). Self contained adaptable optical wireless communications system for stroke rate during swimming. Sensors Journal, IEEE, 13(8), 3144-3151.

Kerrigan, D. C., Todd, M. K., Della Croce, U., Lipsitz, L. A., & Collins, J. J. (1998). Biomechanical gait alterations independent of speed in the healthy elderly: evidence for specific limiting impairments. Archives of physical medicine and rehabilitation, 79(3), 317-322.

Laporte, R. E., Montoye, H. J., & Caspersen, C. J. (1985). Assessment of physical activity in epidemiologic research: problems and prospects. Public health reports, 100(2), 131.

Larun, L., Nordheim, L., Ekeland, E., Hagen, K., & Heian, F. (2006). Exercise in prevention and treatment of anxiety and de-pression among children and young people. Cochrane Database of Systematic Reviews, 4.

Lee, J. C. (2008). Hacking the nintendo wii remote. Pervasive Computing, IEEE, 7(3), 39-45.

Lee, S.-W., Mase, K., & Kogure, K. (2006). Detection of spatio-temporal gait parameters by using wearable motion sensors. Paper presented at the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the.

Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., . . . Andrews, K. G. (2013). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The lancet, 380(9859), 2224-2260.

Malhi, K., Mukhopadhyay, S. C., Schnepper, J., Haefke, M., & Ewald, H. (2012). A zigbee-based wearable physiological parameters monitoring system. Sensors Journal, IEEE, 12(3), 423-430.

Mayagoitia, R. E., Nene, A. V., & Veltink, P. H. (2002). Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. Journal of biomechanics, 35(4), 537-542.

Meijer, G. A., Westerterp, K. R., Verhoeven, F. M., Koper, H., & Ten Hoor, F. (1991). Methods to assess physical activity with special reference to motion sensors and accelerometers. Biomedical Engineering, IEEE Transactions on, 38(3), 221-229.

Miller, N., Jenkins, O. C., Kallmann, M., & Mataric, M. J. (2004). Motion capture from inertial sensing for untethered humanoid teleoperation. Paper presented at the Humanoid Robots, 2004 4th IEEE/RAS International Conference on.

Montoye, H. J., Washburn, R., Servais, S., Ertl, A., Webster, J. G., & Nagle, F. J. (1982). Estimation of energy expenditure by a portable accelerometer. Medicine and science in sports and exercise, 15(5), 403-407.

Mukhopadhyay, S. C. (2015). Wearable sensors for human activity monitoring: A review. Sensors Journal, IEEE, 15(3), 1321-1330.

Nene, A., Mayagoitia, R., & Veltink, P. (1999). Assessment of rectus femoris function during initial swing phase. Gait & posture, 9(1), 1-9.

O’Donovan, K. J., Kamnik, R., O’Keeffe, D. T., & Lyons, G. M. (2007). An inertial and magnetic sensor based technique for joint angle measurement. Journal of biomechanics, 40(12), 2604-2611.

Pan, M.-S., Huang, K.-C., Lu, T.-H., & Lin, Z.-Y. (2016). Using accelerometer for counting and identifying swimming strokes. Pervasive and Mobile Computing.

Pappas, I. P., Popovic, M. R., Keller, T., Dietz, V., & Morari, M. (2001). A reliable gait phase detection system. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 9(2), 113-125.

Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation, 9(1), 1.

Patrick, J. (1991). Gait laboratory investigations to assist decision making. British journal of hospital medicine, 45(1), 35-37.

Penedo, F. J., & Dahn, J. R. (2005). Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Current opinion in psychiatry, 18(2), 189-193.

Ranhotigmage, C. (2013). Human activities & posture recognition: innovative algorithm for highly accurate detection rate: a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Systems Engineering at Massey University, Palmerston North, New Zealand.

Rasberry, C. N., Lee, S. M., Robin, L., Laris, B., Russell, L. A., Coyle, K. K., & Nihiser, A. J. (2011). The association between school-based physical activity, including physical education, and academic performance: a systematic review of the literature. Preventive medicine, 52, S10-S20.

Requa, R. K., DeAvilla, L. N., & Garrick, J. G. (1993). Injuries in recreational adult fitness activities. The American Journal of Sports Medicine, 21(3), 461-467.

Rodriguez, D. A., Brown, A. L., & Troped, P. J. (2005). Portable global positioning units to complement accelerometry-based physical activity monitors. Medicine and science in sports and exercise, 37(11), S572.

Roetenberg, D., Slycke, P. J., & Veltink, P. H. (2007). Ambulatory position and orientation tracking fusing magnetic and inertial sensing. Biomedical Engineering, IEEE Transactions on, 54(5), 883-890.

Rueterbories, J., Spaich, E. G., Larsen, B., & Andersen, O. K. (2010). Methods for gait event detection and analysis in ambulatory systems. Medical engineering & physics, 32(6), 545-552.

Sabatini, A. M., Martelloni, C., Scapellato, S., & Cavallo, F. (2005). Assessment of walking features from foot inertial sensing. Biomedical Engineering, IEEE Transactions on, 52(3), 486-494.

Saris, W., & Binkhorst, R. (1977). The use of pedometer and actometer in studying daily physical activity in man. Part I: reliability of pedometer and actometer. European journal of applied physiology and occupational physiology, 37(3), 219-228.

Schneider, P. L., Crouter, S. E., Lukajic, O., & Bassett, D. R. (2003). Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Medicine and science in sports and exercise, 35(10), 1779-1784.

Shaltis, P. A., Reisner, A. T., & Asada, H. H. (2008). Cuffless blood pressure monitoring using hydrostatic pressure changes. IEEE transactions on bio-medical engineering, 55(6), 1775-1777.

Shany, T., Redmond, S. J., Narayanan, M. R., & Lovell, N. H. (2012). Sensors-based wearable systems for monitoring of human movement and falls. Sensors Journal, IEEE, 12(3), 658-670.

Snijders, A. H., Van De Warrenburg, B. P., Giladi, N., & Bloem, B. R. (2007). Neurological gait disorders in elderly people: clinical approach and classification. The Lancet Neurology, 6(1), 63-74.

Steele, B. G., Holt, L., Belza, B., Ferris, S., Lakshminaryan, S., & Buchner, D. M. (2000). Quantitating physical activity in COPD using a triaxial accelerometer. CHEST Journal, 117(5), 1359-1367.

Steffen, T. M., Hacker, T. A., & Mollinger, L. (2002). Age-and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and gait speeds. Physical therapy, 82(2), 128-137.

Takeda, R., Tadano, S., Natorigawa, A., Todoh, M., & Yoshinari, S. (2009). Gait posture estimation using wearable acceleration and gyro sensors. Journal of biomechanics, 42(15), 2486-2494.

Tong, K., & Granat, M. H. (1999). A practical gait analysis system using gyroscopes. Medical engineering & physics, 21(2), 87-94.

Troped, P. J., Oliveira, M. S., Matthews, C. E., Cromley, E. K., Melly, S. J., & Craig, B. A. (2008). Prediction of activity mode with global positioning system and accelerometer data. Medicine and science in sports and exercise, 40(5), 972-978.

Vooijs, M., Alpay, L. L., Snoeck-Stroband, J. B., Beerthuizen, T., Siemonsma, P. C., Abbink, J. J., . . . Rövekamp, T. A. (2014). Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. Interactive journal of medical research, 3(4).

Watanabe, T., Saito, H., Koike, E., & Nitta, K. (2011). A preliminary test of measurement of joint angles and stride length with wireless inertial sensors for wearable gait evaluation system. Computational intelligence and neuroscience, 2011, 6.

Winter, D. A. (1984). Kinematic and kinetic patterns in human gait: variability and compensating effects. Human Movement Science, 3(1), 51-76.

Yang, S., & Li, Q. (2012). Inertial sensor-based methods in walking speed estimation: a systematic review. Sensors (Basel), 12(5), 6102-6116. doi:10.3390/s120506102

Yang, S., & Li, Q. (2012). Inertial sensor-based methods in walking speed estimation: A systematic review. Sensors, 12(5), 6102-6116.
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