||Development of a rehabilitation assessment system and biofeedback training for the stroke patients
||Department of BioMedical Engineering
inertial measurement component
Due to the changes of lifestyle today, the number of chronic diseases is increasing day by day. The prevalence of stroke in Taiwan has increased year by year, and the age has also become younger. The aftereffects of stroke are numerous, and about 88% of patients show hemiplegia with different severity after stroke. Patients can improve their condition through long-term rehabilitation. The effectiveness of rehabilitation can be assessed by the gait characteristics between the feet. However, most of today's gait analysis systems can only be carried out in biomechanical laboratories or medical related institutions. It has been proven that the equipment is expensive and takes up a long time and is not suitable for home care applications. A simple rehabilitation assessment system has become an important clinical issue.
In this study, two inertial measurement components are respectively fixed on the instep of the two feet, then the acceleration and angular velocity in the inertial component are extracted as the basis for quantifying the gait characteristics. Due to the limitations of the accelerometer and the gyroscope itself, this study allows the two components to be calibrated to each other through the sensor fusion technique, and integrates the algorithms developed in the study to estimate the correct gait characteristics as much as possible. In order to allow patients to easily review and evaluate the test results, this study develops the system on the Android platform, calculates the original data of the inertial measurement component, and transmits it to the smart phone by wireless transmission through the technology of Bluetooth 4.0. To allow the patient to instantly view the gait quantification of walking on the smart phone's app, and to learn the calculated gait analysis after walking.
This study designed a set of experiments which subjects are required to perform a straight walk at a comfortable, natural speed under the simulation of normal two-legged gait and abnormal one-leg gait. Subjects are also allowed to see the quantitative gait characteristics on a smart phone, instantly. The ratio of the relevant gait characteristics was obtained at the end of the experiment to assess the effect of the subject's walking on the gait characteristics. It can be known from the experimental results that for a normal subject with both feet, the symmetrical ratio presented in the gait characteristics of both feet is about 1. For subjects with difficulty walking on one foot, the symmetrical ratio is greater than 1 or less than 1, indicating that any one foot will be significantly different from the other in some gait characteristics. It can be seen from the results that the correlation of gait characteristics between the normal and abnormal gait feet is consistent with the pre-experiment hypothesis.
List of Figures VII
List of Tables IX
Chapter 1 Introduction 1
1.1 Background of Stroke 1
1.1.1 The conditions of Post-Stroke 2
1.2 Gait Analysis 5
1.2.1 Normal Gait 5
1.2.2 Hemiplegic Gait 8
1.3 Devices of Gait Assessment 9
1.3.1 Visual Gait Analysis: Camera and Video 10
1.3.2 Motion Capture System 11
1.3.3 Force Sensor System 13
1.3.4 Inertial Measurement Units 14
1.4 Literature Review 17
1.5 Motivation and Aims 20
Chapter 2 Material and Methods 21
2.1 Experimental Design 21
2.2 System Architecture 22
2.2.1 Inertial Measurement Unit 22
2.2.2 Bluetooth Low Energy 23
2.2.3 Mobile Application 26
2.3 Algorithm 27
2.3.1 Sensor fusion 27
2.3.2 Remove gravity 28
Chapter 3 Results and Discussion 29
3.1 Mobile Application User Interface 29
3.2 Gait characteristics analysis 31
3.3 Influence of Characteristics on Abnormal Gait 34
Chapter 4 Conclusion 40
 葉伯壽, "台灣腦中風概況與急性腦梗塞的治療發展," 中國統計學報, vol. 55, no. 2, pp. 63-66, 2017.
 AVN Arogya Ayurvedic Hospital. Types of Stroke. Available: http://www.avnarogya.in/distinguishinga-stroke-and-a-heart-attack/
 C.L. Chen, F.T. Tang, H.C. Chen, C.Y. Chung, and M.K. Wong, "Brain lesion size and location: effects on motor recovery and functional outcome in stroke patients," Archives of physical medicine and rehabilitation, vol. 81, no. 4, pp. 447-452, 2000.
 National Stroke Association. (2014). Post-stroke conditions. Available: http://www.stroke.org/we-can-help/survivors/stroke-recovery/post-stroke-conditions
 B. Balaban and F. Tok, "Gait disturbances in patients with stroke," PM&R, vol. 6, no. 7, pp. 635-642, 2014.
 A. Armitage, Advanced Practice Nursing Guide to the Neurological Exam. Springer Publishing Company, 2015.
 S. J. Cuccurullo, Physical medicine and rehabilitation board review. Demos Medical Publishing, 2014.
 L. R. Sheffler and J. Chae, "Hemiparetic gait," Physical Medicine and Rehabilitation Clinics, vol. 26, no. 4, pp. 611-623, 2015.
 M. W. Whittle, Gait analysis: an introduction. Butterworth-Heinemann, 2014.
 K. K. Patterson, W. H. Gage, D. Brooks, S. E. Black, and W. E. McIlroy, "Evaluation of gait symmetry after stroke: a comparison of current methods and recommendations for standardization," Gait & posture, vol. 31, no. 2, pp. 241-246, 2010.
 S. M. Woolley, "Characteristics of gait in hemiplegia," Topics in stroke rehabilitation, vol. 7, pp. 1-18, 2001.
 E. J. Roth, C. Merbitz, K. Mroczek, S. A. Dugan, and W. W. Suh, "Hemiplegic gait: Relationships between walking speed and other temporal parameters1," American journal of physical medicine & rehabilitation, vol. 76, pp. 128-133, 1997.
 D. Levine, J. Richards, and M. W. Whittle, Whittle's Gait Analysis-E-Book. Elsevier Health Sciences, 2012.
 C. K. Balasubramanian, R. R. Neptune, and S. A. Kautz, "Variability in spatiotemporal step characteristics and its relationship to walking performance post-stroke," Gait & posture, vol. 29, no. 3, pp. 408-414, 2009.
 W. H. Organization, International Classification of Functioning, Disability and Health: ICF. World Health Organization, 2001.
 B.W. Hwang, S. Kim, and S.W. Lee, "2D and 3D full-body gesture database for analyzing daily human gestures," Advances in Intelligent Computing, pp. 611-620, 2005.
 VICON. The Vicon Biomechanics and Sports Science community encompasses many applications, including research, sports performance and animal science. Available: https://www.vicon.com/motion-capture/biomechanics-and-sport
 Y. Shih, C.S. Ho, and T.Y. Shiang, "Measuring kinematic changes of the foot using a gyro sensor during intense running," Journal of sports sciences, vol. 32, no. 6, pp. 550-556, 2014.
 W. W. Lee et al., "A smartphone-centric system for the range of motion assessment in stroke patients," IEEE journal of biomedical and health informatics, vol. 18, no. 6, pp. 1839-1847, 2014.
 K. Oyake et al., "Validity of gait asymmetry estimation by using an accelerometer in individuals with hemiparetic stroke," Journal of physical therapy science, vol. 29, no. 2, pp. 307-311, 2017.
 R. Caldas, M. Mundt, W. Potthast, F. B. de Lima Neto, and B. Markert, "A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms," Gait & posture, vol. 57, pp. 204-210, 2017.
 W. Vaughn. (2016). Better Mattresses and Mattress Selection through Pressure Mapping. Available: http://www.beds.org/blog/better-mattresses-and-mattress-selection-through-pressure-mapping/
 E. Barkallah, "Researchers develop wearable tech that can recognize harmful body postures at work," 2017.
 鄧正隆, 慣性技術. Hyweb Technology Co. Ltd., 2011.
 W. Tao, T. Liu, R. Zheng, and H. Feng, "Gait analysis using wearable sensors," Sensors, vol. 12, no. 2, pp. 2255-2283, 2012.
 Wiki. (2007). Yaw, Pitch and Roll in an aircraft. Available: https://en.wikipedia.org/wiki/Aircraft_principal_axes
 X. Wang and Y. Wang, "Gait analysis of children with spastic hemiplegic cerebral palsy," vol. 7, no. 20, p. 1578, 2012.
 E. Molteni, E. Beretta, D. Altomonte, F. Formica, and S. Strazzer, "Combined robotic-aided gait training and 3D gait analysis provide objective treatment and assessment of gait in children and adolescents with Acquired Hemiplegia," in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 4566-4569, 2015.
 R. A. Clark, S. Vernon, B. F. Mentiplay, K. J. Miller, J. L. McGinley, Y. H. Pua, et al., "Instrumenting gait assessment using the Kinect in people living with stroke: reliability and association with balance tests," Journal of neuroengineering and rehabilitation, vol. 12, p. 15, 2015.
 R. Lemoyne and T. Mastroianni, "Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson’s disease hand tremor," Mobile Health Technologies: Methods and Protocols, pp. 335-358, 2015.
 A. M. Sabatini, C. Martelloni, S. Scapellato, and F. Cavallo, "Assessment of walking features from foot inertial sensing," vol. 52, no. 3, pp. 486-494, 2005.