研究介紹：帕金森氏症是一種與年齡相關的神經退化疾病。帕金森氏病評級量表通常
需要透過訓練有素的神經科醫生以追蹤病患的病情進展和調整劑量。MARG 傳感器
大多應用於智慧型手錶或是手機中，它能夠提供有關佩戴者運動的信息，並有可能
能夠替代部分的臨床檢查。為了研究MARG 感測器的所記錄的資料與臨床實驗結果
之間的關係，我們選擇了UPDRS 中的七個運動測試進行了實驗並同時錄製實驗影片
和MARG 感測器的數據。
研究目標：本論文記錄了平板應用程式的開發，此應用程式可用於引導、支援受試
者並自動化數據的處理。此外我們也記錄了同步實驗影片和MARG 感測器數據的方
法。
研究方法：我們根據由史丹佛大學普拉特納設計學院所提出的設計思維原則開發了一
款能夠協助受試者進行動作任務的應用程式，且在此程式內添加了閃爍訊號以利透
過機器視覺分析受試者的動作。感測器與實驗影片的同步我們則是利用機器視覺藉
由影片模擬感測器於空間上的姿態變化，並透過主成分分析將感測器資料過濾雜訊
及干擾並找到最大變化的方向。最後透過相關係數以及峰值匹配的方法找到兩者間
的時間差。該應用是按照設計思維方法開發的。
研究結果：我們最新版的應用程式成功地引導受試者執行統一帕金森氏症評定量表中
的動作任務並減少了診斷者的參與，因此減少了受試過程中發生錯誤影響資料收集
的機率。透過機器視覺，我們利用自定義的特徵點並採用機器視覺成功模擬了感測
器在空間上的姿態變化，並利用此結果與感測器資料同步，其準確度達到150 毫秒
的誤差。
研究結論：在此研究中，我們提供了一款提供協助帕金森氏症動作評估的應用程式並
藉此提升了資料品質。另外我們透過模擬感測器於空間上的姿態變化與感測器資料
同步，透過此同步影片與感測器資料，我們未來能夠更加快速且準確地將資料分割以利分析。

Introduction: Parkinson’s disease (PD) is an age-related progressive neurodgenerative disorder. No cure exist but the symptoms can be reduced through medication. The Unified Parkinson's Disease Rating Scale (UPDRS) is commonly used to track the progression and adjust the dosage. Application of the UPDRS requires a trained neurologist. Magnetic, angular rate and gravity (MARG) sensors are cheap and abundant in smart watches and phones. They provide information about the wearers' motion and could potentially, in part, substitute
clinical examination. To investigate the relation between MARG sensor data and clinical findings using UPDRS, we have standardized an experimental protocol for seven of the motor tests in UPDRS. We recorded both video and MARG sensor data simultaneously.
Objectives: This thesis document the development of a tablet App for instructing the subject, supporting the examiner, and automating the data processing, as well as a method for synchronizing the video and MARG sensor data.
Methods: The App was developed following the Design Thinking approach. To synchronize the video and MARG sensor, we record the orientation of the MARG sensor in 3D space by applying computer vision techniques. We pre-process the MARG data by applying the revised Automatic Heading Reference System (AHRS) algorithm. Then we for both data find the direction with largest change using Principal component analysis (PCA). Afterwards, the correlation coefficient and peak matching were applied on the video and MARG sensor to find their time difference.
Results: The App successfully guides subjects, and reduces the examiner's involvement and number of errors during data recording. The accuracy of synchronization of video and MARG sensor has reached to 137 milliseconds (33 frames) difference.
Conclusion: Our App for assisting Parkinson's disease motor symptoms experiment improve the data quality. The synchronization of MARG and video data enable future studies on their relationship.

Aghanavesi, S., Nyholm, D., Senek, M., Bergquist, F., and Memedi, M. (2017). A smartphone-based system to quantify dexterity in parkinson’s disease patients. Informatics in Medicine Unlocked, 9:11–17.
Anand, V., Bilal, E., Ho, B., and Rice, J. J. (2020). Towards motor evaluation of parkinson's disease patients using wearable inertial sensors. In AMIA Annual Symposium Proceedings, volume 2020, page 203. American Medical Informatics Association.
Arora, S., Baig, F., Lo, C., Barber, T. R., Lawton, M. A., Zhan, A., Rolinski, M., Ruffmann,
C., Klein, J. C., Rumbold, J., et al. (2018). Smartphone motor testing to distinguish idiopathic rem sleep behavior disorder, controls, and pd. Neurology, 91(16):e1528–e1538.
Arroyo-Gallego, T., Ledesma-Carbayo, M. J., Sánchez-Ferro, A., Butterworth, I., Mendoza, C. S., Matarazzo, M., Montero, P., López-Blanco, R., Puertas-Martin, V., Trincado, R., et al. (2017). Detection of motor impairment in parkinson's disease via mobile touchscreen typing. IEEE Transactions on Biomedical Engineering, 64(9):1994–2002.
Ashyani, A., Lin, C.-L., Roman, E., Yeh, T., Kuo, T., Tsai, W.-F., Lin, Y., Tu, R., Su, A.,
Wang, C.-C., Tan, C.-H., and Nordling, T. E. M. (2022). Digitization of updrs upper limb motor examinations towards automated quantification of symptoms of parkinson's disease. Manuscript in preparation.
Bobić, V., Djurić-Jovičić, M., Dragašević, N., Popović, M. B., Kostić, V. S., and Kvaščev,
G. (2019). An expert system for quantification of bradykinesia based on wearable inertial sensors. Sensors, 19(11):2644.
BroutonLab (2020). A complete review of the opencv object tracking algorithms.
Cavallo, F., Moschetti, A., Esposito, D., Maremmani, C., and Rovini, E. (2019). Upper limb motor pre-clinical assessment in parkinson’s disease using machine learning. Parkinsonism & related disorders, 63:111–116.
Chang, J. R. and Nordling, T. E. M. (2021). Skin feature point tracking using deep feature encodings. CoRR, abs/2112.14159.
Chén, O. Y., Lipsmeier, F., Phan, H., Prince, J., Taylor, K. I., Gossens, C., Lindemann, M., and De Vos, M. (2020). Building a machine-learning framework to remotely assess parkinson's disease using smartphones. IEEE Transactions on Biomedical Engineering, 67(12):3491–3500.
Creagh, A., Simillion, C., Scotland, A., Lipsmeier, F., Bernasconi, C., Belachew, S., van
Beek, J., Baker, M., Gossens, C., Lindemann, M., et al. (2020). Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the draw a shape test. Physiological measurement, 41(5):054002.
Dai, J. S. (2015). Euler–rodrigues formula variations, quaternion conjugation and intrinsic connections. Mechanism and Machine Theory, 92:144–152.
Dam, R. F. (2021). 5 Stages in the Design Thinking Process | Interaction Design Foundation (IxDF).
Ferraris, C., Nerino, R., Chimienti, A., Pettiti, G., Cau, N., Cimolin, V., Azzaro, C., Albani, G., Priano, L., and Mauro, A. (2018). A self-managed system for automated assessment of updrs upper limb tasks in parkinson’s disease. Sensors, 18(10):3523.
Goetz, C. G., Poewe, W., Rascol, O., Sampaio, C., Stebbins, G. T., Fahn, S., Lang, A. E.,
Martinez-Martin, P., Tilley, B., Van Hilten, B., Kleczka, C., and Seidl, L. (2003). The
unified parkinson’s disease rating scale (updrs): status and recommendations. Movement Disorders, 18(7):738–750.
Hartley, R. and Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge university press.
Hassani, H. (2007). Singular spectrum analysis: methodology and comparison.
kinker Mishra, R., Park, C., Zhou, H., Najafi, B., and Thrasher, T. A. (2022). Evaluation of motor and cognitive performance in people with parkinson's disease using instrumented trail-making test. Gerontology, 68(2):234–240.
Lalvay, L., Lara, M., Mora, A., Alarcón, F., Fraga, M., Pancorbo, J., Marina, J. L., Mena,
M. Á., Lopez Sendón, J. L., and García de Yébenes, J. (2017). Quantitative measurement of akinesia in parkinson's disease. Movement disorders clinical practice, 4(3):316–322.
Langevin, R., Ali, M. R., Sen, T., Snyder, C., Myers, T., Dorsey, E. R., and Hoque, M. E.
(2019). The park framework for automated analysis of parkinson's disease characteristics. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(2):1–22.
Lee, C. Y., Kang, S. J., Hong, S.-K., Ma, H.-I., Lee, U., and Kim, Y. J. (2016). A validation
study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in parkinson's disease. PloS one, 11(7):e0158852.
Lin, Z., Dai, H., Xiong, Y., Xia, X., and Horng, S.-J. (2017). Quantification assessment of bradykinesia in parkinson's disease based on a wearable device. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 803–806. IEEE.
Lipsmeier, F., Taylor, K. I., Kilchenmann, T., Wolf, D., Scotland, A., Schjodt-Eriksen, J.,
Cheng, W.-Y., Fernandez-Garcia, I., Siebourg-Polster, J., Jin, L., et al. (2018). Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 parkinson's disease clinical trial. Movement Disorders, 33(8):1287–1297.
Madgwick, S. (2014). Ahrs algorithms and calibration solutions tofacilitate new applications using low-cost mems.
Madgwick, S. O., Harrison, A. J., and Vaidyanathan, R. (2011). Estimation of imu and marg orientation using a gradient descent algorithm. In 2011 IEEE international conference on rehabilitation robotics, pages 1–7. IEEE.
Mahadevan, N., Demanuele, C., Zhang, H., Volfson, D., Ho, B., Erb, M. K., and Patel, S.
(2020). Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. NPJ digital medicine, 3(1):1–12.
Mahony, R. E., Hamel, T., and Pflimlin, J. M. (2005). Complementary filter design on the special orthogonal group so(3). Proceedings of the 44th IEEE Conference on Decision and Control, pages 1477–1484.
OpenCV (2022). Opencv: Camera calibration and 3d reconstruction. https://docs.
opencv.org/4.x/d9/d0c/group__calib3d.html, last visited on 2022-06-09.
Patel, S., Chen, B.-r., Buckley, T., Rednic, R., McClure, D., Tarsy, D., Shih, L., Dy, J., Welsh, M., and Bonato, P. (2010). Home monitoring of patients with parkinson's disease via wearable technology and a web-based application. In 2010 annual international conference of the IEEE engineering in medicine and biology, pages 4411–4414. IEEE.
Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M., and Bonato, P. (2009). Monitoring motor fluctuations in patients with parkinson's disease using wearable sensors. IEEE transactions on information technology in biomedicine, 13(6):864–873.
Piñol, D. C. (2019). Edge effects when resampling a signal on matlab. how to solve it? | by david castro piñol | analytics vidhya | medium.
Piro, N. E., Baumann, L., Tengler, M., Piro, L., and Blechschmidt-Trapp, R. (2014). Telemonitoring of patients with parkinson＇s disease using inertia sensors. Applied clinical informatics, 5(2):503.
Piro, N. E., Piro, L. K., Kassubek, J., and Blechschmidt-Trapp, R. A. (2016). Analysis
and visualization of 3d motion data for updrs rating of patients with parkinson's disease. Sensors, 16(6):930.
Post, B., Merkus, M. P., de Bie, R. M., de Haan, R. J., and Speelman, J. D. (2005). Unified parkinson's disease rating scale motor examination: Are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable? Movement Disorders, 20(12):1577–1584.
Shull, P. B., Jirattigalachote, W., Hunt, M. A., Cutkosky, M. R., and Delp, S. L. (2014).
Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait & posture, 40(1):11–19.
Yeager, K. (2021). LibGuides: SPSS Tutorials: Pearson Correlation. https://libguides.
library.kent.edu/SPSS/PearsonCorr, last visited on 2022-05-31.
Yeh, T.-H. (2019). 帕金森氏症治療現況. http://web.tccf.org.tw/lib/addon.php?
act=post&id=4500, last visited on 2022-04-27.
Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330–1334.