隨著年齡的增長與老化，銀髮族的慢性疾病發生率提高，照護需求增加。而身體機能也隨之衰退，逐漸影響其日常活動能力，甚而需要他人的協助。快速的人口老化趨勢使得銀髮照護成為社會沉重的負擔，因而世界衛生組織倡導活躍老化，希望藉由建立環境支持與健康促進，來提升銀髮族的自主生活能力，降低照護需求。
提供適切的照護計畫對於提高銀髮族自主日常生活活動有重大的效益，而開發輔助身體評估技術也具有同等的重要意義。在臨床上觀察身體的活動與動作型態是重要的評估工作。現行的臨床作業主要是由醫護人員實施定期評估及訪談，以其專業知識進行判讀，屬主觀性的評量。近年來，隨著資通訊技術發展逐漸成熟，應用相關技術來擷取及量化身體活動與動作型態，將可提供連續、客觀的資訊，進而提供照護決策參考、規劃適切的照護服務方案及相關的日常生活輔助。
本研究目的為透過內嵌融入於環境的感測技術，設計與發展日常生活活動感知系統，自動識別並記錄與健康照護相關之日常活動，輔助健康相關生活品質評估。特定目標主要為：1)發展環境感知系統，以陣列式壓力感測墊來收集、量測、儲存臥床壓力分佈，用以偵測臥姿與臥床活動；以聲音感測收集、儲存生活空間的聲音，用以偵測聲音事件；2)發展統計機率式臥床活動識別架構，有效率的建構臥姿與臥床活動模型，提供量化數據用以輔助健康紀錄與生活型態監測。採用高斯混合模型建立分類器，結合小分類錯誤訓練法，建立穩健的臥姿分類；3)發展具骨突處識別之臥姿辨識機制，提供壓瘡風險監測。使用模糊 c-means演算法轉換壓力輪廓並定位出壓力集中的感興趣區域，用以預防壓瘡。以潛在語意分析從轉換的感興趣區域影像萃取出顯著特徵，用以發展類神經網路模型，提供臥姿辨識；4)發展聲學觸動之生活空間活動識別架構，提供身體活動功能健康狀態與社會連結的模型。以馬可夫模型結合所設計的行為語法樹建立自動聲音事件辨識。並以高斯混合模型結合應用多維尺度空間提供快速語者分類；5) 以模擬生活情境劇本所發展之日常生活資料庫進行系統可行性評估，用以改善機構住民之安全與照護的有效性。
利用客觀評量與實際場域試驗來研究臥姿與活動偵測的效能。實驗結果顯示當將壓力分佈值區隔為4個叢集時，平均臥姿辨識率可達到95.89%，聲音事件辨識與語者分類偵測率亦高。模糊 c-means結合潛在語意分析之轉換改善臥姿辨識率，並可定位出具風險的骨突區域。聲學觸發之方法亦顯示其可行性，可以有效地勾勒日常活動並提供健康狀態與社會連結的量化證據。從個案研究證明在所應用的領域具有潛力與實用性，並顯示能夠有效且客觀地評量與日常生活活動功能量表、工具性日常生活活動能力量表及與健康相關生活品質量表有關的身體功能資訊。
未來可將應用本研究成果開發臨床應用監測系統，提供即時骨突處警示機制，以及應用自動量化記錄發展特定應用健康相關生活品質量表評量指標。

Providing an appropriate care plan for the elderly to participate in everyday life activities is important, as is developing the technological means to support physical assessments. In clinical practice, observation of physical activities and movement patterns is crucial, though current protocol is generally episodic from subjective assessments and interviews. With the maturing development of information and communication technologies, application of said technologies for quantizing activities could provide continuous and objective data to adjust caregiving and support assisted living. The purpose of this research is to design and develop an activity awareness system via ambient sensing technology to automatically identify and record daily in-house living activities for assisting healthcare charting and monitoring needs for the health related quality of life (HRQL) assessment.
This research was aims to: 1) develop an ambient awareness system with pressure and acoustic sensing mechanisms to collect, measure and store lying posture and environmental sounds for monitoring bedside activities. Lying pressure distribution data was gathered from a sensor pad developed for this purpose. Acoustic streams were recorded for designed scenarios within a mock living space; 2) develop a probabilistic based activity recognition system to efficiently model life activities and in-bed posture for providing quantitative measurements towards healthcare charting and lifestyle monitoring. A Gaussian mixture model (GMM) based classifier trained with a minimum classification error (MCE) criterion was adopted for robust posture classification; 3) develop an identification system for bedsore risk monitoring. The fuzzy c-means (FCM) algorithm was used to transform the pressure contours and identify regions of interest (ROI) having high pressure for ulcer prevention. Latent semantic analysis (LSA) extracted the significant features from the transformed ROI images in order to develop an artificial neural network model for posture recognition; 4) develop an acoustic activated recognition system to efficiently model daily bedside activities for providing quantitative evidence of physical health and social interactivity. A hidden Markov model with a behavior grammar network developed for this research automatically recognized acoustic events. A Gaussian mixture model combined with multidimensional scaling was proposed for fast speaker diarization; 5) evaluate the performance of the developed system towards resident safety and caregiving efficacy by using scenario-driven daily activity datasets.
Several objective evaluations and field trials were performed to investigate the performance of lying posture and activity detection. Experimental results show the average posture recognition rate was 95.89% when the pressure distributions were divided into 4 clusters. FCM with LSA transformation improved the recognition rate and could be used to locate the corresponding risky compressed bony prominences. The high detection rates in both the recognition of acoustic events and speakers demonstrated the feasibility of efficiently modeling daily activities and providing quantitative evidence of health conditions and social interaction. The case study and simulation tests show the potential and applicability for in-bed and bedside activity monitoring, and also shows suitability for objectively evaluating the physical functions in the activity of daily life (ADL), instrument activity of daily life (IADL) and HRQL assessments.
Some ways to build upon the contributions of the current study include: improving the developed prototypes, such as, new pressure pads to provide more efficient in-bed monitoring, or enhance the quality/resolution of the captured sound to increase the rate of acoustic event recognition performance. Extended applications include real-time alerts for excess pressure duration on bony prominences, and specific HRQL assessment indexes.

REFERENCES
[1] A. Kalache and I. Kickbusch, “A Global Strategy for Healthy Ageing,” World Health, vol. 4, pp. 4-5, July/August 1997.
[2] M.E. Dellefield, “Implementation of the resident assessment instrument/minimum data set in the nursing home as organization: implications for quality improvement in RN clinical assessment,” Geriatric nursing, vol. 28, no. 6, pp. 377-386, 2007.
[3] G. Onder et al. “Assessment of nursing home residents in Europe: the services and health for elderly in long term care (SHELTER) study,” BMC Health services research, vol. 12, no.5, 2012. doi:10.1186/1472-6963-12-5.
[4] J. J. Gallo, T. Fulmer, G. J. Paveza and W. Reichel, “Functional assessment,” In: Handbook of Geriatric Assessment, Jones & Bartlett, Sudbury, MA, 2000, pp. 109-110.
[5] M. K. Jiricka, P. Ryan, M. A. Carvalho and J. Bukvich J. "Pressure ulcer risk factors in an ICU population," American Journal of Critical Care, vol. 4, no. 5, pp. 361–367, 1995.
[6] R. G. Logsdon, L. Teri, M. F. Weiner, L. E. Gibbons, M. Raskind, E. Peskind, M. Grundman, E. Koss, R. G. Thomas and L. J. Thal, “Assessment of agitation in Alzheimer's disease: the agitated behavior in dementia scale. Alzheimer's Disease Cooperative Study,” Journal of the American Geriatrics Society, vol. 47, no.11, pp. 1354-1358, 1999.
[7] D. J. Buysse, C. F. 3rd Reynolds, T. H. Monk, S. R. Berman and D. J. Kupfer, “The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research.” Psychiatry Research, vol. 28, no. 2, pp. 193-213, 1989.
[8] G. H. Guyatt, H. D. Feeny and D. L. Patrick, “Measuring Health-related Quality of Life.” Annals of Internal Medicine, vol. 118, pp. 622-629, 1993.
[9] R.A. Kane, et. al. 2003. “Quality of life measures for nursing home residents.” Journal of gerontology: medical sciences 58 (3): 240–248.
[10] D. G. Fryback, M. Palta, D. Cherepanov, D. Bolt and Jee-Seo Kim, “Comparison of five health-related quality-of-life indexes using item response theory analysis,” Medical Decision Making, vol. 30, no. 1, pp. 5–15, 2010.
[11] J. E. Ware, “SF-36 health survey update,” SPINE, vol. 25, no. 24, pp. 3130 –3139, 2000.
[12] J. Brazier, J. Roberts and M. Deverill, “The estimation of a preference-based measure of health from the SF-36,” Journal of Health Economics, vol. 21, no. 2, pp. 271-92, 2002.
[13] A. Szende, M. Oppe and N. Devlin. eds., EQ-5D value sets: INventory, Comparative review and user guide, Dordrecht, The Netherlands: Springer. 2007.
[14] Public Population Project in Genomics. “P3G Health-Related Quality of Life (QoL) Assessment Comparison Chart.” http://www.p3gobservatory.org/repository/qualityOf LifeComparison.htm , 2008.
[15] H. B. Degenholtz, J. Rosen, N. Castle, V. Mittal and D. Liu, “The Association Between Changes in Health Status and Nursing Home Resident Quality of Life,” The Gerontologist, vol. 48, no. 5, pp. 584–592, 2008.
[16] L. Brajković, A. Godan and L. Godan, “Quality of Life After Stroke in Old Age: Comparison of Persons Living in Nursing Home and Those Living in Their Own Home.” Croatian Medical Journal, vol. 50, no. 2, pp. 182-187, 2009.
[17] V. A. C. Galhardo, M. G. Magalhães, L. Blanes, Y. Juliano and L. M. Ferreira, “Health-related Quality of Life and Depression in Older Patients With Pressure Ulcers.” Wounds, vol. 22, no. 1, pp. 20–26, 2010.
[18] H. Bouma, “Creating adaptive technological environments,” Gerontechnology, vol. 1, no. 1, pp. 1-3, 2001.
[19] T. S. Barger, D. E. Brown and M. Alwan, “Health status monitoring through analysis of behavioral patterns,” IEEE transactions on systems, man, and cybernetics—part a: systems and humans, vol. 35, no. 1, pp. 22-27, 2005.
[20] C. N. Huang, C. Y. Chiang, G. C. Chen, S. J. Hsu, W. C. Chu and C. T. Chan, “Fall detection system for healthcare quality improvement in residential care facilities,” Journal of medical and biological engineering, vol. 30, no. 4, pp. 247-252. 2010.
[21] Ni Scanail C., S. Carew, P. Barralon, N. Noury, D. Lyons and G. M. Lyons. “A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment,” Annals of Biomedical Engineering, 2006, vol. 34, no. 4, pp. 547–563. doi: 10.1007/s10439-005-9068-2.
[22] A. L. Betker, Z. M. Moussavi and T. Szturm, “Ambulatory Center of Mass Prediction Using Body Accelerations and Center of Foot Pressure,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 11, pp. 2491-2498, 2008.
[23] Y. T. Chen, Y. C. Lin and W. H. Fang, “A Video-based Human Fall Detection System for Smart Homes,” Journal of the Chinese Institute of Engineers, vol. 33, no. 5, pp. 681–690, 2010.
[24] C. F. Juang and K. C. Ku, “A Recurrent Fuzzy Network for Fuzzy Temporal Sequence Processing and Gesture Recognition.” IEEE Transaction on Systems, Man, and Cybernetics – Part B: Cybernetics, vol. 35, no. 4, pp. 646–658, 2005. doi: 10.1109/TSMCB.2005.844594.
[25] C. F. Juang and C. M. Chang, “Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application,” IEEE Transaction on System, Man, and Cybernetics – Part A: Systems and Humans, vol. 37, no. 6, pp. 984–994, 2007. doi: 10.1109/TSMCB.2005.844594.
[26] L. M. Lee and U. K. Yusof, “Hands-free Messaging Application (iSay-SMS): A Proposed Framework,” 2010 International Conference on Science and Social Research (CSSR 2010), December 5-7, 2010, Kuala Lumper, Malaysia.
[27] S. Primorac and M. Russo. 2012. “Android application for sending SMS messages with speech recognition interface,” Proceedings of the 35th International Convention, MIPRO, 21-25 May 2012, Opatija, Croatia, 1763- 1767.
[28] R. Singh and S. Dixit, “Health-Related Quality of Life and Health Management,” Journal of Health Management, vol. 12, no. 2, pp. 153–172, 2010.
[29] M. Vacher, S. Jean-François and C. Stéphane, “Sound Classification in a Smart Room Environment: an Approach using GMM and HMM Methods,” in Proceedings of the 4th conference on speech technology and human-computer dialogue, Romania, 2007, pp. 135-146.
[30] A. Fleury, N. Noury, M. Vacher, H. Glasson and J. F. Serignat, “Sound and speech detection and classification in a Health Smart Home,” in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Vancouver, 2008, pp. 4644-4647.
[31] D. A. Reynolds and P. Torres-Carrasquillo. “Approaches and applications of audio diarization,” Proceedings of the IEEE International Conference on Audio and Speech Signal Processing, Philadelphia, PA, USA. 2005, vol. 5, pp. 953-956.
[32] D. Vijayasenan, F. Valente, and H. Bourlard, “Agglomerative information bottleneck for speaker diarization of meetings data,” in Proceedings of IEEE Automatic Speech Recognition Understanding Workshop (ASRU), Kyoto, Japan, 2007, pp. 250-255.
[33] D. Vijayasenan, F. Valente, H. Bourlard, “Combination of agglomerative and sequential clustering for speaker diarization,” in Proceedings of the IEEE International Conference on Audio and Speech Signal Processing, Las Vegas, Nevada, 2008, pp. 4361-4364.
[34] Y. Huang, O. Vinyals, G. Friedland, C. M¨uller, N. Mirghafori and C. Wooters, “A fast-match approach for robust, faster than real-time speaker diarization,” in Proceedings of IEEE Workshop on Automatic Speech Recognition & Understanding, Kyoto, Japan, 2007, pp. 693-698.
[35] S. S. Chen and P. S. Gopalakrishnan, “Speaker, environment and channel change detection and clustering via the Bayesian information criterion,” in Proceedings of DARPA Speech Recognition Workshop, Lansdowne VA, 1998.
[36] A. Tritschler and R. Gopinath, “Improved speaker segmentation and segments clustering using the Bayesian information criterion,” in Proceedings of EUROSPEECH’ 99, Budapest, Hungary, 1999, vol. 2, pp. 679–682.
[37] E. Capezuti, B. L. Brushb, S. Lanec, H. U. Rabinowitz and M. Secic. “Bed-exit alarm effectiveness.” Archives of Gerontology and Geriatrics, vol. 49, pp. 27–31, 2009. doi:0.1016/j.archger.2008.04.007
[38] A. I. Sonia, R. Cole, C. Alessi, M. Chambers, W. Moorcrof and C. P. Pollak. “The Role of Actigraphy in the Study of Sleep and Circadian Rhythms” SLEEP, vol. 26, no. 3, pp. 342–392, 2003.
[39] A. Sadeh, K. M. Sharkey and M. A. Carskadon. “Activity-Based Sleep-Wake Identification: An Emirical Test of Methodological Issues,” Sleep, vol. 17, no. 3, pp. 201-207, 1994.
[40] W. H. Liao, J. H Kuo, C. M. Yang and I. Y. Chen, “ iWakeUp: A Video-Based Alarm Clock for Smart Bedrooms,” Journal of the Chinese Institute of Engineers, vol. 33, no. 5, pp. 661-668, 2010.
[41] T. Tamura, J. Zhou, H. Mizukami and T. Togawa, “A system for monitoring temperature distribution in bed and its application to the assessment of body movement,” Physiological Measurement, vol. 14, pp. 33-41, 1993.
[42] N. Bu, N. Ueno and O. Fukuda, “Monitoring of respiration and heartbeat during sleep using a flexible piezoelectric film sensor and empirical mode decomposition,” in Proceedings of the 29th annual international conference of the IEEE EMBS, Lyon, France, 2007, pp. 1362-1366.
[43] J. Hao et al. “An intelligent elderly healthcare monitoring system using fiber-based sensors,” Journal of the Chinese institute of engineers, vol. 33, no. 5, pp. 653-660, 2010.
[44] T. Harada, T. Mori, Y. Nishida and T. Yoshimi, “Body parts positions and posture estimation system based on pressure distribution image,” in Proceedings of IEEE international conference on robotics and automation, Detroit, MI, pp. 968-975, 1999.
[45] T. Harada, T. Sato and T. Mori. “Estimation of bed-ridden human’s gross and slight movement based on pressure sensors distribution bed,” in Proceedings of IEEE international conference on robotics and automation, Washington, DC, 2002, pp. 3795-3800.
[46] Y. Nishida, et. al. 1997. “Monitoring patient respiration and posture using human symbiosis system,” in Proceedings of IEEE international conference on intelligent robots and systems, Grenoble, France, 1997, pp632-639.
[47] K. H. Seo, C. Oh, T. Y. Choi, J. J. Lee, “Bed-type robotic system for the bedridden,” in Proceedings of IEEE/ASME international conference on advanced intelligent mechatronics, California, 2005, pp. 1170-1175.
[48] R. Yousefi, S. Ostadabbas, M. Faezipour, M. Farshbaf, M. Nourani, L.Tamil, and M. Pompeo, “Bed posture classification for pressure ulcer prevention.” in Proceedings of 2011 Annual international conference of the IEEE engineering in medicine and biology society, EMBC, Boston, MA, 2011 pp. 7175-7178.
[49] Interlink Electronics. Force Sensing Resistor Integration Guide & Evaluation Parts Catalog, Camarillo, CA. USA: Interlink Electronics, 90-45632 Rev.D. 2010.
[50] M. Short and M. J. Pont. “Fault-tolerant time-triggered communication using CAN,” IEEE Transaction on Industrial and Informatics, vol. 3, no. 2, pp. 131-142, 2007.
[51] T. Harada, A. Saito, T. Sato and T. Mori. “Infant Behavior Recognition System Based on Pressure Distribution Image,” in Proceeding of the 2000 IEEE International Conference on Robotics and Automation, ICRA 2000, vol. 4, pp. 4082-4088.
[52] ACLCLP (The Association for Computational Linguistics and Chinese Language Processing). 2012. MAT Speech Database – TCC300. Retrieved at 2012/12. http://www.aclclp.org.tw/use_mat.php#tcc300edu
[53] C. H. Wu, Y. H. Chiu, C. J. Shia and C. Y. Lin, “Automatic Segmentation and Identification of Mixed-Language Speech Using Delta-BIC and LSA-Based GMMs,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 1, pp. 266-276, 2006. doi: 10.1109/TSA.2005.852992.
[54] Y. W. Hung, Y. H. Chiu, W. H. Chen, and K. S. Cheng, “Monitoring non-ambulatory posture and activity using moment statistics and Bayesian classifier,” Journal of the Chinese Institute of Engineers. Published online: 25 Jul 2013. doi: 10.1080/02533839.2013.815014.
[55] A.P. Dempster, N. M. Laird and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the royal statistical society, vol. 39, no. 1, pp. 1-38. , 1977
[56] S. Katagiri, C. H. Lee and B. H. Juang, “New discriminative training algorithms based on the generalized probabilistic descent method,” in Proceedings of the IEEE-SP workshop on neural network for signal processing, Princeton, NJ, 1991, pp. 299-308.
[57] Y. W. Lim and S. U. Lee, “On The Color Image Segmentation Algorithm Based on the Thresholding and the Fuzzy c-means Techniques,” Pattern Recognition, vol. 23, no. 9, pp. 935-952, 1990.
[58] J. C. Bezdek Pattern recognition with fuzzy objective function algorithms. New York: Plenum, 1981.
[59] K. P. Burnham and D. R. Anderson. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. New York: Springer-Verlag, 2002.
[60] M. Negnevitsky 2 “Artificial Neural Networks.” In Artificial Intelligence, a Guide to Intelligent System,” Edinburgh Gate: Pearson Education Limited. 2004.
[61] G. Panchal, A. Ganatra, Y. P. Kosta and D. Panchal. “Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers,” International Journal of Computer Theory and Engineering, vol. 3, no. 2, pp. 332–337, 2011.
[62] HTK: speech recognition toolkit, available at http://htk.eng.cam.ac.uk (2012).
[63] M. E. Dunnachie, P. W. Shields, D. H. Crawford and Mike Davies, “Filler Models for Automatic Speech Recognition Created from Hidden Markov Models using the K-means Algorithm,” in Proceeding of the 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland, 2009, pp. 544-548,.
[64] I. Borg and P. Groenen, Modern Multidimensional Scaling: theory and applications. 2nd ed. New York: Springer-Verlag, pp. 207–212, 2005.
[65] J. Goldberger and H. Aronowitz. “A Distance Measure between GMMs based on the Unscented Transform and its Application to Speaker Recognition,” in Proceedings of Interspeech 2005, Lisbon, Portugal. pp.1985-1989, 2005.
[66] Yoseph Linde, Andres Buzo and Robert M. Gray, 1980. “An Algorithm for Vector Quantizer Design.” IEEE Transactions on Communications 28(1), 84–94.
[67] C. Barras, X. Zhu, S. Meignier and J. L. Gauvain, “Improving speaker diarization,” in Proceeding of Fall Rich Transcription Workshop (RT-04), 2004.
[68] J. B. Reswick and J. E. Rogers. “Experience at Rancho Los Amigos Hospital with Devices and Techniques to Prevent Pressure Ulcers,” Bed Sore Biomechanics, edited by R. M. Kenedi, J. M. Cowden and J. T. Scales, pp. 301–310. Baltimore: University Park Press. 1976.
[69] J. G. Webster, Prevention of Pressure Sores. New York: Adam Hilger, 1991.
[70] J.F. Schnelle, D. Osterweil and S. F. Simmons. “Improving the quality of nursing home care and medical-record accuracy with direct observational technologies,” The Gerontologist, vol. 45, no. 5, pp. 576–582, 2005.
[71] B. M. Bates-Jensen, M. Cadogan, D. Osterweil, L. Levy-Storms, J. Jorge, N. Al-Samarrai, V. Grbic, and J. F. Schnelle, “The minimum data set pressure ulcer indicator: does it reflect differences in care processes related to pressure ulcer prevention and treatment in nursing homes?” Journal of the American geriatrics society, vol. 51, no. 9, pp. 1203-1212. 2003.