| 研究生: |
郭玟伶 Guo, Wun-Ling |
|---|---|
| 論文名稱: |
整合智慧型行動裝置之個人化無縫式運動照護系統之開發 Development of A Personalized Seamless Exercise Care System by Integrating Smart Handheld Devices |
| 指導教授: |
蔣榮先
Chiang, Jung-Hsien |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 醫學資訊研究所 Institute of Medical Informatics |
| 論文出版年: | 2012 |
| 畢業學年度: | 100 |
| 語文別: | 中文 |
| 論文頁數: | 64 |
| 中文關鍵詞: | 遠距照護 、運動照護系統 |
| 外文關鍵詞: | Telecare, exercise healthcare system |
| 相關次數: | 點閱:110 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
二十世紀中期,因應社會結構開始轉變邁入高齡化社會,老年人口增加導致疾病類型改變所造成的醫療需求改變及高醫療花費,因此,對於銀髮族長期照護與醫療,將投入更多預算與心力,遠距照護更是政府重要政策。
遠距照護的推動核心在於結合醫療服務、照顧服務、生活及科技發展的福祉科技,以建構完善的照護服務。因此,藉由資通訊科技的導入,發展友善使用人機介面,帶動遠距醫療照護、居家照護、個人健康紀錄、運動照護、醫療電子產品及生理監測資訊支援等服務。其中,在運動照護上所提供的服務,醫院使用的運動照護系統資訊最為豐富與完整,透過完善的儀器設備即可應用於心肺疾病的檢測、運動復健療程與追蹤、運動處方的建立。然而,醫院所提供的運動照護系統資訊卻存在時間與空間上的限制,無法應用在日常生活中。為了達到全面性的運動照護之功能,因此,過去許多研究以及健身品牌、醫療器材公司等,推出相關產品如計步器、心跳計、血糖機、血氧儀等設計遠距健康照護相關功能,提供使用者隨身攜帶可隨時使用打破時間與空間的限制,並使用精確的生理數據以避免使用者對於本身的生理狀況有主客觀評估的落差,更有許多研究將生理資訊結合智慧型手機設計遠距照護系統。然而,其功能資訊大都利用單一生理訊號提供單一服務給使用者,無法直接應用於運動照護相關之服務。
論文之研究目標在於開發一整合智慧型行動裝置之個人化無縫式運動照護系統,不受限於時間和地點,運用隨身攜帶之智慧型手機整合動作辨識與生理訊號進行個人化運動照護警示系統之設計,提供使用者隨時了解自身運動安全的心肺功能照護,透過本研究作為運動照護的可行性系統評估。
Because the social structure began to change to ageing society in mid-21st century, the elderly populations increase which leading to the type of disease changing and changing the need and high cost in medicine. Therefore, long-term care for seniors and health care will be devoting more budgets and effort, the telecare is an important policy for government especially.
The telecare promote the core which combines of medical services, care services, life and well-being of scientific and technological development, science and technology, in order to construct the comprehensive care services. Therefore, by leading in the information and communications technology, developing friendly user interface to drive by telecare, home care, personal health records, sports care, and medical electronic products and physiological monitoring of information support services. The services which provided in the exercise care system are the most abundant and complete in hospital. Through the complete equipment which be applied to the detection of heart and lung disease, exercise rehabilitation treatment and tracking of the establishment of the exercise prescription. However, the exercise care system which provided by hospital exist limit on the time and space, which cannot be applied in daily life. In order to achieve comprehensive exercise care, in many previous studies, fitness brands and medical equipment companies, the introduction of related products such as the pedometers, heart rate monitors, blood glucose, pulse oximeter and other telecare system. The products provide the user to carry at any time to break the limitations of time and space, and the precise physiological data to avoid the difference between subjective and objective assessment of the user's own physical condition in exercise, there are many studies which combine physiological information and the smartphone to design of telecare system. However, this functional information of products mostly use single physiological signal to provide a single service to users, so it is difficult to directly applied to the exercise care-related services.
The purpose of this research is aimed to develop a personalized seamless exercise care system by integrating smart handheld devices which not are limited on the time and space, using the smartphone to integrate physiological signal and activity recognition to design personalized exercise care alert system, providing users to understand the sports safety with cardiopulmonary care. Using this study is to assess the feasibility of exercise care system.
[1] "行政院遠距照護服務發展計畫," 2012.
[2] (2012). Polar. Available: http://www.polartaiwan.com.tw/tw-zh
[3] (2012). Nike. Available: http://www.nike.com/nikeos/p/nike/zh_TW/
[4] (2012). Adidas. Available: http://www.adidas.com.tw/
[5] (2012). Nonin. Available: http://www.nonin.com/
[6] (2012). Delta. Available: http://www.delta.com.tw/
[7] (2012). Google play. Available: http://play.google.com/intl/zh-TW/about/index.html
[8] (2012). App store. Available: http://www.appstoreapps.com/
[9] (2012).Finger pulse oximeter. Available: http://y-master.so-buy.com/front/bin/partprint.phtml?Part=b-2&Category=0&Style=1
[10] (2012). Exercise test . Available: http://www.kln.doh.gov.tw/main_sec.php?index=public_se&page_name=detail&pageNo_p=1&iid=77&title=&pid=88&sid=06&bsid=checknu&pname=
[11] M. S. Islam, N. Alajlan, Y. Bazi, and H. S. Hichri, "HBS: A Novel Biometric Feature Based on Heartbeat Morphology," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 445-453, 2012.
[12] A. Depeursinge, S. Duc, I. Eggel, and H. Muller, "Mobile Medical Visual Information Retrieval," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 53-61, 2012.
[13] G. Rigas, A. T. Tzallas, M. G. Tsipouras, P. Bougia, E. E. Tripoliti, D. Baga, D. I. Fotiadis, S. G. Tsouli, and S. Konitsiotis, "Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable Sensors," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 478-487, 2012.
[14] T. H. N. Vu, N. Park, Y. K. Lee, Y. Lee, J. Y. Lee, and K. H. Ryu, "Online discovery of Heart Rate Variability patterns in mobile healthcare services," Journal of Systems and Software, vol. 83, pp. 1930-1940, 2010.
[15] J. Vales-Alonso, P. López-Matencio, F. J. Gonzalez-Castaño, H. Navarro-Hellín, P. J. Baños-Guirao, F. J. Pérez-Martínez, R. P. Martínez-Álvarez, D. González-Jiménez, F. Gil-Castiñeira, and R. Duro-Fernández, "Ambient Intelligence Systems for Personalized Sport Training," Sensors, vol. 10, pp. 2359-2385, 2010.
[16] K. Segerståhl and H. Oinas-Kukkonen, "Designing personal exercise monitoring employing multiple modes of delivery: Implications from a qualitative study on heart rate monitoring," International Journal of Medical Informatics, vol. 80, pp. e203-e213, 2011.
[17] E. Seto, K. J. Leonard, J. A. Cafazzo, J. Barnsley, C. Masino, and H. J. Ross, "Developing healthcare rule-based expert systems: Case study of a heart failure telemonitoring system," International Journal of Medical Informatics.
[18] H. M. Al-Angari and A. V. Sahakian, "Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 463-468, 2012.
[19] A. M. Cheriyan, Z. Kalbarczyk, R. K. Iyer, A. O. Jarvi, T. M. Gallagher, and K. L. Watkin, "Pervasive embedded real time monitoring of EEG & SpO2," in Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference on, 2009, pp. 1-4.
[20] A. Nobuyuki, N. Yasuhiro, T. Taiki, Y. Miyae, M. Kiyoko, and H. Terumasa, "Trial of measurement of sleep apnea syndrome with sound monitoring and SpO2 at home," in e-Health Networking, Applications and Services, 2009. Healthcom 2009. 11th International Conference on, 2009, pp. 66-69.
[21] B. Xie and M. Hlaing, "Real-Time Sleep Apnea Detection by Classifier Combination," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 469-477, 2012.
[22] P. Medeiros, Jr., G. Lorenzi-Filho, S. P. Pimenta, R. A. Kairalla, and C. R. Carvalho, "Sleep desaturation and its relationship to lung function, exercise and quality of life in LAM," Respir Med, vol. 106, pp. 420-8, Mar 2012.
[23] J. Morak, H. Kumpusch, D. Hayn, R. Modre-Osprian, and G. Schreier, "Design and Evaluation of a Telemonitoring Concept Based on NFC-Enabled Mobile Phones and Sensor Devices," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 17-23, 2012.
[24] H. H. Asada, P. Shaltis, A. Reisner, R. Sokwoo, and R. C. Hutchinson, "Mobile monitoring with wearable photoplethysmographic biosensors," Engineering in Medicine and Biology Magazine, IEEE, vol. 22, pp. 28-40, 2003.
[25] L. Eron, P. King, M. Marineau, and C. Yonehara, "Treating Acute Infections by Telemedicine in the Home," Clinical Infectious Diseases, vol. 39, pp. 1175-1181, 2004.
[26] B. Hyun Jae, C. Gih Sung, K. Ko Keun, and P. Kwang Suk, "A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 150-158, 2012.
[27] C. Jongyoon, B. Ahmed, and R. Gutierrez-Osuna, "Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 279-286, 2012.
[28] A. Ridolfi, R. Vetter, J. Solà, and C. Sartori, "Physiological monitoring system for high altitude sports," Procedia Engineering, vol. 2, pp. 2889-2894, 2010.
[29] D. Apiletti, E. Baralis, G. Bruno, and T. Cerquitelli, "Real-Time Analysis of Physiological Data to Support Medical Applications," Information Technology in Biomedicine, IEEE Transactions on, vol. 13, pp. 313-321, 2009.
[30] F. Shih-Chen, W. Ming-Hui, H. Chun-Tang, H. Chung-Hao, H. Shang-Hwa, C. Ming-Chuen, J. K. Zao, and L. Chin-Teng, "Health pal: a PDA phone that will take care of your health," in Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, 2007, pp. 3703-3708.
[31] F. Zhou, H.-I. Yang, J. Álamo, J. Wong, and C. Chang, "Mobile Personal Health Care System for Patients with Diabetes
Aging Friendly Technology for Health and Independence." vol. 6159, Y. Lee, Z. Bien, M. Mokhtari, J. Kim, M. Park, J. Kim, H. Lee, and I. Khalil, Eds., ed: Springer Berlin / Heidelberg, 2010, pp. 94-101.
[32] L. Jung-Eun, O. H. Choi, N. Hong-Seok, and B. Doo-Kwon, "A Context-Aware Fitness Guide System for Exercise Optimization in U-Health," Information Technology in Biomedicine, IEEE Transactions on, vol. 13, pp. 370-379, 2009.
[33] T. Tamura, I. Mizukura, M. Sekine, and Y. Kimura, "Monitoring and Evaluation of Blood Pressure Changes With a Home Healthcare System," Information Technology in Biomedicine, IEEE Transactions on, vol. 15, pp. 602-607, 2011.
[34] J. Parkka, J. Merilahti, E. M. Mattila, E. Malm, K. Antila, M. T. Tuomisto, A. V. Saarinen, M. van Gils, and I. Korhonen, "Relationship of Psychological and Physiological Variables in Long-Term Self-Monitored Data During Work Ability Rehabilitation Program," Information Technology in Biomedicine, IEEE Transactions on, vol. 13, pp. 141-151, 2009.
[35] F. Buttussi and L. Chittaro, "Smarter Phones for Healthier Lifestyles: An Adaptive Fitness Game," Pervasive Computing, IEEE, vol. 9, pp. 51-57, 2010.
[36] S. Consolvo, J. A. Landay, and D. W. McDonald, "Designing for Behavior Change in Everyday Life," Computer, vol. 42, pp. 86-89, 2009.
[37] J. Choi, J. Chun, K. Lee, S. Lee, D. Shin, S. Hyun, D. Kim, and D. Kim, "MobileNurse: hand-held information system for point of nursing care," Computer Methods and Programs in Biomedicine, vol. 74, pp. 245-254, 2004.
[38] K. Eunju, H. Sumi, and D. Cook, "Human Activity Recognition and Pattern Discovery," Pervasive Computing, IEEE, vol. 9, pp. 48-53, 2010.
[39] A. M. Khan, L. Young-Koo, S. Y. Lee, and K. Tae-Seong, "A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer," Information Technology in Biomedicine, IEEE Transactions on, vol. 14, pp. 1166-1172, 2010.
[40] L. Ming, Rozgic, x, V., G. Thatte, L. Sangwon, A. Emken, M. Annavaram, U. Mitra, D. Spruijt-Metz, and S. Narayanan, "Multimodal Physical Activity Recognition by Fusing Temporal and Cepstral Information," Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 18, pp. 369-380, 2010.
[41] Z. Shuai, S. I. McClean, and B. W. Scotney, "Probabilistic Learning From Incomplete Data for Recognition of Activities of Daily Living in Smart Homes," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 454-462, 2012.
[42] G. Tao, W. Liang, W. Zhanqing, T. Xianping, and L. Jian, "A Pattern Mining Approach to Sensor-Based Human Activity Recognition," Knowledge and Data Engineering, IEEE Transactions on, vol. 23, pp. 1359-1372, 2011.
[43] (1970). Target Heart Rate. Available: http://en.wikipedia.org/wiki/Heart_rate
[44] (1987). The Compendium of Physical Activities. Available: http://prevention.sph.sc.edu/tools/compendium.htm
[45] (1970). Borg Rating of Perceived Exertion Scale. Available: http://www.cdc.gov/physicalactivity/everyone/measuring/exertion.html
[46 ] N. Héraud, C. Préfaut, F. Durand, and A. Varray, "Does correction of
exercise-induced desaturation by O2 always improve exercise tolerance in COPD? A preliminary study," Respiratory Medicine, vol. 102, pp. 1276-1286, 2008.
[47] A. M. Khan, Y.-K. Lee, S. Y. Lee and T.-S. Kim, “A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer,” IEEE Transactions on Information Technology in Biomedicine., vol. 14, no. 5, Sep. 2010.
[48] Ling Bao, Stephen S. Intille, “Activity Recognition from User-Annotated Acceleration Data.” Pervasive Computing, Second International Conference, p1-17, 2004.
[49] 羅兆呈,“使用智慧型手機內建之加速度計於動能感知系統之開發與應用”,
[50] Redmond, S.J., Ambikairajah, E., Celler, B.G., Lovell, N.H., “Can Triaxial Accelerometry Accurately Recognize Inclined Walking Terrains?,” IEEE Transations on B iomedical Engineering ;57(10):2506-2516, 2010.
校內:2020-12-31公開