隨著高齡化社會的趨勢，老年人口逐年增加。帕金森氏症（Parkinson’s disease）為老年人常罹患的神經疾病之一，目前並無根治帕金森氏症的治療方法，除了利用藥物控制病情，也需要藉由復健來維持身體肌肉的強度與協調性。步行為日常活動的基本動作，帕金森氏症病患在要開始行走時以及轉彎過程中常因為僵直或是步態凍結導致無法如正常人行走，甚至有跌倒的風險。復健的進行除了到醫院之外，絕大部分的情況下，還是要由病患自行復健。但是病患往往因為缺乏即時回饋；無從得知復健的成效，因而降低持續練習的意願。
本研究與臨床物理治療師合作開發輔助帕金森氏症行走復健的APP與記錄患者復健流程的資料庫伺服器。利用智慧型手機的慣性裝置計算病患在自行練習行走時，直線行走與轉彎時所耗費的步數與時間；提供病患即時回饋，並將每日練習的狀況回傳資料庫伺服器，提供復健師調整復健處方之參考，並能給予病患適時的鼓勵，以協助病患能持續長期自行復健。
透過智慧型手機APP與網路通訊技術規劃出一套能夠輔助病患評估復健的架構，有別於以往的復健過程，病患於居家中能得知自我的復健成效。系統目前經過20位受測者的試驗，結果顯示本系統具有良好的信效度，具備可信度的評估工具；整體的系統架構也由臨床物理治療師以適用性量表評估本研究復健架構的適用性。

With the current trend toward an aging society, the elderly population has been increasing yearly. Parkinson’s disease is one of the neurological diseases that the elderly often suffer from, and there is currently no treatment to cure it. Apart from using drugs to control the disease, rehabilitation is also necessary to maintain muscle strength and coordination. Walking is a basic movement during daily activities, and patients who have Parkinson’s disease are unable to walk like healthy individuals in initiating walk and turning, owing to rigidity or gait freezing, and then maybe the risk of falling. Rehabilitation in a hospital is insufficient; most rehabilitation should be performed by the patient themselves. However, patients usually do not appreciate the effectiveness of self-rehabilitation efforts owing to the lack of immediate feedback, thus reducing their willingness to continue practicing.
This study aimed to develop an app for walking rehabilitation and a database server for recording the rehabilitation process of Parkinson’s patients in collaboration with clinical physiotherapists. A smartphone inertial device was used to determine the number of steps and duration of both walking in a straight line and turning. Instant feedback was provided to the patients, and the status of daily self-practice was then stored in the database server to provide a reference for the adjustment of rehabilitation prescriptions by the rehabilitation division. Thus, appropriate encouragement could be given to the patients, which could help them continue long-term self-rehabilitation.
A framework to assist patients in rehabilitation evaluation was designed using the smartphone app and networking technology. This differs from the previous processes of rehabilitation, as the patient becomes aware of the effects of self-rehabilitation at home. The system has been tested by 20 subjects. The results indicated that the system has good reliability and validity, and is a credible evaluation tools. The overall system framework has also been assessed by clinical physical therapists to test the applicability of the rehabilitation framework.

[1] World Health Organization, Interesting facts about ageing. Ageing and Life Course, from http://www.who.int/ageing/about/facts/en/.
[2] 行政院主計總處。國情統計通報(第190號)。2013年10月。
[3] S. Hassan, B. Alex, C. Angela and L. Raynarth, “Causes of Parkinson’s disease： Literature Review,” Journal of Parkinsonism & Restless Legs Syndrome, Vol. 1, No. 1, pp.5–7, 2011.
[4] World Health Organization, “Neurological disorders： public health challenges,” WHO Library Cataloguing-in-Publication Data, pp.140–141, 2006.
[5] Ministry of Health and Welfare, Statistics Department. National Health Insurance of Health Statistics Annual Report 2003–2012, Taiwan, 2014.
[6] P. A. Crawford, The Washington Manual Cardiology Subspecialty Consult, Philadelphia： Lippincott Williams & Wilkins, 2003.
[7] M. L. Latash, “Postural control and locomotion in Parkinson’s disease,” Neurophysiological basis of movement, pp. 325–326, 1998.
[8] J. Jankovic, “Parkinson’s disease： clinical features and diagnosis,” J Neurol Neurosurg Psychiatry, Vol. 79, No. 3, pp. 368–376, April 2008.
[9] M. M. Hoehn and M. D. Yahr, “Parkinsonism：onset, progression, and mortality,” Neurology, Vol. 17, No. 1, pp. 427–442, 1967.
[10] C. G. Goetz et al., “Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS)： scale presentation and clinimetric testing results,” Movement Disorders, Vol. 23, No. 15, pp. 2129–2170, 2008.
[11] National Parkinson Foundation, from http://www.parkinson.org/parkinson-s-disease.aspx/.
[12] K. Samyra et al., European Physiotherapy Guideline for Parkinson’s Disease, Version 2013004, 2013.
[13] K. Daniel White et al., “Changes in walking activity and endurance following rehabilitation for people with Parkinson disease,” Archives of Physical Medicine and Rehabilitation, Vol. 90, No. 1, pp. 43–50, Jan. 2009.
[14] T. Ellis et al., “Effectiveness of an inpatient multidisciplinary rehabilitation program for people with Parkinson disease, ” Journal of the American Physical Therapy Association, Vol. 88, No. 7, pp. 812–819, July 2008.
[15] A. T. Caglar, H. N. Gurses, F. K. Mutluay and G. Kizitan, “Effects of home exercises on motor performance in patients with Parkinson’s disease,” Journal of Clinical Rehabilitation, Vol. 19, No.8, pp. 870–877, Aug. 2005.
[16] L. I. I. K. Lim et al, “Measuring gait and gait-related activities in Parkinson’s patients own home environment： a reliability, responsiveness and feasibility study,” Parkinsonism & Related Disorders, Vol. 11, No. 1, pp. 19–24, Jan. 2005.
[17] M. E. Morris et al., “Protocol for a home-based integrated physical therapy program to reduce falls and improve mobility in people with Parkinson's disease,” BMC Neurology, Vol. 12, No. 54, 2012.
[18] A. M. Willems et al., “Turning in Parkinson's disease patients and controls： the effect of auditory cues,” Movement Disorder Society, Vol. 22, No. 13, pp. 1871–8, 2007.
[19] Z. Kadivar, D. M. Corcos, J. Foto and J. M. Hondzinski, “Effect of step training and rhythmic auditory stimulation on functional performance in Parkinson patients,” Neurorehabilitation & Neural Repair, Vol. 25, No. 7, pp. 626–635, 2011.
[20] M. F. del Olmo and J. Cudeiro, “Temporal variability of gait in Parkinson disease： effects of a rehabilitation program based on rhythmic sound cues,” Parkinsonism & Related Disorders, Vol. 11, No. 1, pp. 25–33, Jan. 2005.
[21] 黃佳琦(2010)。神經復健：動作表現最佳化(第二版)。台北市：臺灣愛思唯爾發行。
[22] E. L. Stack, A. M. Ashburn and K. E. Jupp, “Strategies used by people with Parkinson's disease who report difficulty turning,” Parkinsonism and Related Disorders, Vol. 12, No. 2, pp. 87–92, 2006.
[23] J. E. Visser et al., “Quantification of trunk rotations during turning and walking in Parkinson's disease,” Clinical Neurophysiology, Vol. 118, No. 7, pp. 1602–6, 2007.
[24] M. E. Morris et al, “Protocol for a home-based integrated physical therapy program to reduce falls and improve mobility in people with Parkinson's disease,” BMC Neurology, Vol. 12, No. 54, 2012.
[25] J. Zhao, J. Jia, H. Wang and W. Li, “A novel threshold Accelerometer with postbuckling structures for Airbag Restraint Systems,” IEEE Sensors Journal, Vol. 7, No. 8, pp. 1102–1109, 2007.
[26] Analog Devices Inc., Digital Accelerometer, June 2007.
[27] H. Sternberg and H. Sternberg, “Qualification process for MEMS gyroscopes for the use in navigation systems,” in Proc. of the 5th Symposium on Mobile Mapping Technology, 2008, pp. 285–292.
[28] A. Edelstein, “Advances in magnetometry,” Journal of Physics： Condensed Matter, Vol. 19, No. 16, pp. 217–244, 2007.
[29] K. Y. Chen and D. R. Basset, “The technology of accelerometry-based activity monitors： current and future,” Medicine and Science in Sports Exercise, Vol. 37, No. 11, pp. 490–500, 2005.
[30] Y. Mitsuru, K. Yosuk, W. Kajiro, and M. Hiroshi, “Accelerometry-based gait analysis and its application to Parkinson’s disease assessment—Part 1： Detection of stride event,” Gait posture, Vol. 22, No. 3, pp. 613-622, 2014.
[31] W. G. Janssen, J. B. Bussmann, H. L. Horemans, and H. J. Stan, “Validity of accelerometry in assessing the duration of sit-to-stand movement,” Medical & Biological Engineering & Computing, Vol. 46, pp. 879–87, 2008.
[32] N. Gebruers et al., “Monitoring of physical activity after stroke： a systematic review of accelerometry-base measure,” Archives of Physical Medicine and Rehabilitation, Vol. 91, No. 4, pp. 288–297, 2010.
[33] R. Soangra and T. E. Lockhart, “A comparative study for performance evaluation of sit-to-stand task with body worn sensor and existing laboratory methods.” Biomed. Sci. Instrum, Vol. 48, No. 3, pp. 407–414, 2012.
[34] A. Weiss et al., “Can an accelerometer enhance the utility of the timed up & go test when evaluating patient with Parkinson’s disease?,” Medical Engineering & Physics, Vol 32, No. 1, pp. 119–125, 2010.
[35] C. E. Lang, D. F. Edwards, R. L Birkenmeier and A. W. Dromerick, “Estimating minimal clinical importance differences of upper-extremity measures early after stroke,” Archives of Physical Medicine and Rehabilitation, Vol. 89, No. 2, pp. 1693–700, 2008.
[36] A. Salarian et al., “Gait Assessment in Parkinson’s disease： toward an ambulatory system for long-term monitoring,” IEEE Transaction Biomedical Engineering. Vol. 51, No. 8, pp. 1434–1443, 2004.
[37] E. Jovanov et al., “deFOG–A real time system for detection and unfreezing of gait of Parkinson’s patients,” Engineering in Medicine and Biology Society, pp. 5151–5154, 2009.
[38] Z. Sheng, W. Li, A. M. Bernardos and S. Mei, “An accurate and adaptive pedometer integrated in mobile health application,” in Proc. of the IET Int. Conf. on Wireless Sensor Network, Beijing, Nov. 2010, pp. 78–83.
[39] M. Oner, J.A. Pulcifer-Stump, P. Seeling, and T. Kaya, “Towards the run and walk activity classification through step detection–An android application,” Engineering in Medicine and Biology Society (EMBC) , Vol. 22, No. 2, 2012.
[40] B. Mariani, M. C. Jiménez, F. J. Vingerhoets and K. Aminian, “On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease,” IEEE Transactions on Biomedical Engineering, Vol. 60, No. 1, January 2013.
[41] 資策會FIND/經濟部技術處。資策會FIND（2014）/服務創新體驗設計系統研究與推動計畫（2/4）。2014年07月。
[42] Texas Instruments Inc., TI HealthTech Fitness Guide, 2013.
[43] R. Meier, “Introducing the development framework,” Professional Android 4 Application Development, pp.14–20, 2012.
[44] D. M. Karantonis et al., “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring,” IEEE Information Technology in Biomedicine, Vol. 10, No. 1, pp.156–167, Jan. 2006.
[45] B. Mariani et al.,“3D gait assessment in young and elderly subjects using foot-worn inertial sensors,” J. Biomech., Vol. 43, No. 15, pp. 2999–3006, Nov. 2010.
[46] B. Evjen, “Application and page frameworks,” Professional ASP.NET 4 in C# and VB, pp. 32–40, 2010.
[47] D. Podsiadlo, S. Richardson, “The timed Up & Go： a test of basic functional mobility for frail elderly persons,” J Am Geriatr Soc, Vol. 39, No. 1, pp. 142–148, 1991.
[48] E. Auriel et al, “Effects of methylphenidate on cognitive function and gait in patients with Parkinson’s disease： a pilot study,” Clin Neuropharmacol, Vol. 29, No. 5, pp. 15–17, 2006.
[49] S. Schmid, R. Hilfiker and L. Radlinger, “Reliability and validity of trunk accelerometry-derived performance measurements in a standardized heel-rise test in elderly subjects,” J Rehabil Res Dev, Vol. 48, pp. 1137–1144, 2011.
[50] R. Moe-Nilssen and J. L. Helbostad, “Trunk accelerometry as a measure of balance control during quiet standing,” Gait Posture. Vol. 16, No. 1, pp. 60–68, 2002.
[51] C. D. Lewis, Industrial and business forecasting methods： A practical guide to exponential smoothing and curve fitting, London： Butterworth Scientific, 1982.
[52] L. Cohen, Statistics for Social Scientists. Harper & Row, 2005.
[53] J. Brooke, “SUS： a “quick and dirty’ usability scale,” Usability Evaluation in Industry, pp. 189-194, 1996.
[54] B. Aaron, K. Philip, M. James, “Determining what individual SUS scores mean： Adding an adjective rating scale,” Journal of Usability Studies, Vol. 4, No. 3, pp. 114–123, 2009.