許多疾病或是事故的後遺症會造成人的肢體障礙，像是中風、運動傷害及交通事故，這些人往往需要到醫院進行復健治療，才能恢復傷害之前的活動力。以往的復健療程需要患者到醫院才能進行，且由治療師使用量角器量測並監控復健動作，才能得知患者的活動範圍是否有進步。一來復健地點會被侷限在醫院，造成患者的不方便；二來透過治療師使用量角器量測活動監控角度容易產生人為誤差。
為了改善上述的缺點及不方便，本論文開發出一套能監控使用者關節活動角度的穿戴式動作監控系統，除了讓使用地點不受限以外，也能提供較客觀的關節活動角度，此外本系統還具有下列四項優點：多節點擴充功能，讓使用者能夠快速地擴增多個肢段的監控；體積小且輕盈的低功耗穿戴式裝置，讓裝置方便攜帶且具有高續航力；感測器快速校正的功能，讓感測器能在不同的溫濕度下也能提供準確的數值；最後是虛擬實境的即時畫面回饋應用程式，讓使用者能透過螢幕即時得知自身的關節活動狀況。
本論文所開發的穿戴式動作監控裝置與光學三維動作追蹤系統(Qualisys)比較後，在馬達實驗驗證中得到在旋轉X軸下的平均誤差與標準差為0.44°±0.161°；在旋轉Y軸下的平均誤差與標準差為0.368°±0.338°；在旋轉Z軸下的平均誤差與標準差為0.193°±0.287°。另外，此裝置經過功率消耗測試，量測到的總電流平均約為5.44mA左右，其電流消耗遠小於目前市售裝置x-IMU的50mA~150mA。

Many diseases or accidents lead to physical disabilities, for example, stroke, sport injuries, or traffic accidents. Those whom suffered from physical disabilities may have to go to the hospital for rehabilitation to recover from sequela of diseases or injuries. However, the place for rehabilitation is limited to the hospital may leads to patient’s inconvenience, and the results of the range of motion obtained from the goniometry may diverse from different therapists.
To improve the inconvenience and disadvantages above, a wearable movement monitoring system is derived in this study to monitor user’s range of motion. The system can automatically provide the information about the range of motion and allows users to conduct the physical treatment at home. Besides, four benefits also come out: the extension of multi-node to extend the monitoring for more limbs, small and light-weighted low-power wearable device to improve the portability and the endurance, a fast calibration of the sensor to increase the accuracy under different environmental conditions, and a virtual-reality based real-time application to provide feedbacks of the movement information.
The results of the proposed wearable monitoring device have been compared to the optical motion capture system (Qualisys). In the motor-based experiment, a maximum mean error of 0.44°±0.161°, 0.368°±0.338°, and 0.193°±0.287° is obtained in the rotation of X-, Y-, and Z-axes, respectively. Furthermore, the power consumption of our device is about 5.44mA based on the power consumption test, which is much smaller than the commercial device x-IMU of 50mA ~ 150mA.

[1] 邱弘毅, "腦中風之現況與流行病學特徵," 台灣腦中風學會會訊, vol. 15, no. 3, pp. 2-4, 9 2008.
[2] T. Truelsen, S. Begg and C. Mathers, "The global burden of cerebrovascular disease," Cerebrovascular Disease 21-06-06, Global Burden of Disease, May 2000.
[3] C. Anderson, C. N. Mhurchu, S. Rubenach, M. Clark, C. Spencer and A. Winsor, "Home or Hospital for Stroke Rehabilitation? Results of a Randomized Controlled Trial : II: Cost Minimization Analysis at 6 Months, Stroke," American Heart Association, pp. 1032-1037, 2000.
[4] J. M. Geddes and M. A. Chamberlain, "Home-based rehabilitation for people with stroke: a comparative study of six community services providing co-ordinated, multidisciplinary treatment," Clinical Rehabilitation, vol. 15, pp. 589-599, Dec 2001.
[5] M. Balaam, S. R. Egglestone, G. Fitzpatrick, T. Rodden, A.-M. Hughes, A. Wilkinson, T. Nind, L. Axelrod, E. Harris, I. Ricketts, S. Mawson and J. Burridge, "Motivating Mobility: Designing for Lived Motivation in Stroke Rehabilitation," in Session: Rehabilitation, pp. 3073-3082, Nov 2011.
[6] T.-Y. Pan, Y.-X. Wong, T.-C. Lee and M.-C. Hu, "A Kinect-based oral rehabilitation system," in Orange Technologies (ICOT), Dec 2015.
[7] A. Shapi'i, N. N. Bahari, H. Arshad, N. A. M. Zin and Z. R. Mahayuddin, "Rehabilitation exercise game model for post-stroke using Microsoft Kinect camera," in Biomedical Engineering, Mar 2015.
[8] W. C. Hsu, T. M. Wang, M. W. Liu, C. F. Chang, H. L. Chen and T. W. Lu, "Control of body's center of mass motion during level walking and obstacle crossing in older patients with knee osteoarthritis," Journal of Mechanics, vol. 26, pp. 229-237, Jun 2010.
[9] W. C. Hsu, T. W. Lu and M. W. Liu, "Lower limb joint position sense in patients with type II diabetes mellitus," Biomedical Engineering: Applications, Basis and Communications, vol. 21, pp. 271-278, Feb 2009.
[10] N. Omarkulov, K. Telegenov, M. Zeinullin, I. Tursynbek and A. Shintemirov, "Preliminary mechanical design of NU-Wrist: A 3-DOF self-aligning Wrist rehabilitation robot," in Biomedical Robotics and Biomechatronics (BioRob), 2016.
[11] K. X. Khor, P. J. H. Chin, A. R. Hisyam, C.F. Yeong, A. L. T. Narayanan and E. L. M. Su, "Development of CR2-Haptic: A compact and portable rehabilitation robot for wrist and forearm training," in Biomedical Engineering and Sciences (IECBES), 2014.
[12] H.-P. Brückner, R. Nowosielski, H. Kluge and H. Blume, "Mobile and wireless inertial sensor platform for motion capturing in stroke rehabilitation sessions," in Advances in Sensors and Interfaces (IWASI), 2013 5th IEEE International Workshop on, Bari, Italy, 2013.
[13] F. Parisi, G. Ferrari, A. Baricich, M. D'Innocenzo, C. Cisari and A. Mauro, "Accurate gait analysis in post-stroke patients using a single inertial measurement unit," in Wearable and Implantable Body Sensor Networks (BSN), 2016 IEEE 13th International Conference on, San Francisco, CA, USA, 2016.
[14] K. Leuenberger, R. Gonzenbach, E. Wiedmer, A. Luft and R. Gassert, "Classification of stair ascent and descent in stroke patients," in Wearable and Implantable Body Sensor Networks Workshops (BSN Workshops), 2014 11th International Conference on, Zurich, Switzerland, 2014.
[15] Y. Prathivadi, J. Wu, T. R. Bennett and R. Jafari, "Robust activity recognition using wearable IMU sensors," in SENSORS, 2014 IEEE, Valencia, Spain, 2014.
[16] L. Yu, L. Guo, X. Gu, J. Fu and Q. Fang, "LVQ neural network applied for upper limb motion recognition for home-based stroke rehabilitation," in Bioelectronics and Bioinformatics (ISBB), 2011.
[17] C. Cifuentes, A. Braidot, L. Rodríguez, M. Frisoli, A. Santiago and A. Frizera, "Development of a Wearable ZigBee Sensor System for Upper Limb," in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on, Rome, Italy, 2012.
[18] J. I. Pan, H.-W. Chung and J.-J. Huang, "Intelligent Shoulder Joint Home-Based Self-Rehabilitation," International Journal of Smart Home, vol.7, No.5, pp. 395-404, 2013.
[19] A. F. Alhajjar, S. Gobee and D. Vickneswari, "3D GUI system for upper limb rehabilitation using electromyography and inertia measurement unit sensor feedback," in Biomedical Engineering and Sciences (IECBES), 2014.
[20] D. E. Oarde, N. C. Libatique, G. L. Tangonan, D. M. Sotto and A. T. Pacaldo, "Browse Conferences > Humanoid, Nanotechnology, Inf... < Previous | Back to Results | Next >," in Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014 International Conference on, Palawan, Philippines, 2014.
[21] F. Wittmann, O. Lambercy, R. R. Gonzenbach, M. A. v. Raai, R. Höver, J. Held, M. L. Starkey, A. Curt, A. Luft and R. Gassert, "Assessment-driven arm therapy at home using an IMU-based virtual reality system," in Rehabilitation Robotics (ICORR), 2015.
[22] J. P. Amaro, S. Patrão, F. Moita and L. Roseiro, "Bluetooth low energy profile for MPU9150 IMU data transfers," in Bioengineering (ENBENG), 2017 IEEE 5th Portuguese Meeting on, Coimbra, Portugal, 2017.
[23] I. Inc., "MPU-9250 Porduct Specification Revision 1.0," 17 01 2014. [Online]. Available: https://cdn.sparkfun.com/assets/learn_tutorials/5/5/0/MPU9250REV1.0.pdf.
[24] M. Euston, P. Coote, R. Mahony, J. Kim and T. Hamel, "A complementary filter for attitude estimation of a fixed-wing UAV," in Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, 2008.
[25] S. Sabatelli, M. Galgani, L. Fanucci and A. Rocchi, "A Double-Stage Kalman Filter for Orientation Tracking With an Integrated Processor in 9-D IMU," IEEE Transactions on Instrumentation and Measurement, pp. 590-598, 2013.
[26] S. O. H. Madgwick, A. J. L. Harrison and R. Vaidyanathan, "Estimation of IMU and MARG orientation using a gradient descent algorithm," in Rehabilitation Robotics (ICORR), 2011 IEEE International Conference on, 2011.
[27] W. R. Hamilton, "On quaternions; or on a new system of imaginaries in Algebra," London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, pp. 489-495, Jul. 1844.
[28] R. Corporation, "Approval Sheet," Raytac Corporation, 22 05 2017. [Online]. Available: http://www.raytac.com/download/MDBT42/MDBT42Q%20spec-Version%20C.pdf.
[29] "Advanced hard and soft iron magnetometer calibration for dummies," 1 6 2014. [Online]. Available: http://diydrones.com/profiles/blogs/advanced-hard-and-soft-iron-magnetometer-calibration-for-dummies.
[30] N. SEMICONDUCTOR, "BLE Relay Example," 10 Mar. 2017. [Online]. Available: https://infocenter.nordicsemi.com/index.jsp?topic=%2Fcom.nordic.infocenter.sdk51.v10.0.0%2Fble_sdk_app_rscs_relay.html.
[31] "GATT Descriptors," Bluetooth SIG, Inc., [Online]. Available: https://www.bluetooth.com/specifications/gatt/descriptors.
[32] "Client Characteristic Configuration," Bluetooth SIG, Inc., [Online]. Available: https://www.bluetooth.com/specifications/gatt/viewer?attributeXmlFile=org.bluetooth.descriptor.gatt.client_characteristic_configuration.xml.
[33] x-io Technologies, "x-IMU-SPECIFICATIONS," [Online]. Available: http://x-io.co.uk/x-imu/. [Accessed 24 8 2017].
[34] K. Lebe, P. Boiss, M. Hame and C. Duva, "Inertial measures of motion for clinical biomechanics: comparative assessment of accuracy under controlled conditions - effect of velocity.," PLoS ONE, vol. 8, no. 11, p. e79945, Nov. 2013.