以慣性感測器量化中風病患下肢運動能力暨功能分級之研究

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姓名

韓紹禮(Shao-Li Han) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

以慣性感測器量化中風病患下肢運動能力暨功能分級之研究

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摘要(中)

本論文在探討使用穿戴式裝具來量化中風受試者的神經恢復等級與日常生活失能的程度，目的在使用穿戴式裝具以達成降低醫療專業人員評估病患的負擔。論文分為兩大部分，一是使用整合表面肌電圖與慣性感測器來預測神經的分級，另一部分則是使用慣性感測器來推論中風受試者神經恢復分級與生活功能的等級。
第一部份先使用不同機器學習演算法，結合表面肌電圖與慣性感測器，分類神經恢復的等級；其次基於此研究結果，嘗試使用一顆放在足背的慣性感測器，作為預測神經恢復分級的方式；第二部分則是使用4顆慣性感測器來擷取運動學資料，並進行臨床常用的10公尺步行測試，推論運動學與神經恢復等級以及日常生活能力障礙相關性，同時也提出新的步態對稱指標，使用受試者身高來修正對稱指數，用來推論受試者的失能與恢復狀況。
第一部分的試驗結果顯示，結合表面肌電圖與慣性感測器的演算法中，無論是支援向量機（Support Vector Machine, SVM）、k-近鄰演算法（k-Nearest Neighbor, k-NN）以及類神經網路（Artificial Neural Network, ANN）均可達不錯的分級能力，正確率範圍在82.86%到100%；其中以支援向量機的正確性最佳，高達92.5%到100%；最後使用單一顆慣性感測器結合運動生理學的演算法，正確率從86.8%到100%。
第二部分，結果顯示平均步行測試與傅格-梅爾量表下肢部分及伯格平衡量表均存在中度的正相關，相關係數分別為0.62與0.68；使用修正後步態對稱指數，與傅格-梅爾量表下肢部分和伯格平衡量表則呈現中度的負相關，相關係數傅格-梅爾量表下肢部分為-0.51，而伯格平衡量表為-0.52，顯示修正後步態對稱指數，具有可推論神經學與平衡恢復的能力。
綜上所述，從結合兩種感測器到最後使用4顆慣性感測器的研究，可以看出使用慣性感測器結合中風生理學的恢復特性，有機會用來推論受試者的神經恢復與日常失能的等級。修正後步態對稱指數則值得進一步探討與中風病患日常生活功能缺陷的關係。

摘要(英)

In this thesis, two serial studies attempted to quantify neurological and functional defects by correlating clinical assessment scales and kinematics obtained from wearable devices, including surface electromyography and inertial sensors. This thesis contains two domains. One includes classifying neurological recovery status by integrating surface electromyography and inertial sensors to classify clinical Brunnstrom stages. Then, we managed to classify the neurological level through a single inertial sensor.
The results explore that the Support Vector Machine has the best accuracy in classifying the Brunnstrom stage than k-Nearest Neighbor and the Artificial Neural Network. The overall accuracy ranges from 82.86% to 100%, while the Support Vector Machine owns an accuracy ranging from 92.5% to 100%. Additionally, after adopting the threshold values, we get a good accuracy, ranging from 86.78% to 100% among different stages, to classify the Brunnstrom stages, by using a single inertial sensor on the subjects’ feet. This system has been proven feasible for predicting the lower limb Brunnstrom stage for hemiparetic and hemiplegic patients.
The other domain correlates clinical assessment scales and gait parameters from a 10-m walk test. In addition to gait parameters, we also set up a modified gait symmetric index to infer functional recovery results, the Fugl-Meyer assessment scale, and the Berg balance scale. These results explore only the average walking speeds correlate moderately with these two scales (γ = 0.62, 0.68, n = 23), while the modified gait symmetry index owns the moderate negative correlation(γ =-0.51, -0.52, n=23).
Conclusively, with appropriate algorithms and suitable locations to deploy inertial sensors, inertial-based wearable devices demonstrate promising applications in classifying neuromotor and functional recovery among patients after stroke. The modified gait symmetry index warrants further exploring the possibility of inferring functional recovery.

關鍵字(中)

★ 功能量表
★ 步態分析
★ 穿戴式裝具
★ 腦中風
★ 慣性感測器
★ 臨床評估

關鍵字(英)

★ functional assessment
★ gait analysis
★ wearable device
★ stroke
★ inertial sensor
★ clinical assessment

論文目次

中文摘要 i
Abstract ii
致謝 iii
表目錄 vii
圖目錄 viii
一、緒論 1
1-1 研究動機與目的 1
1-2 文獻探討 2
1-3 論文主題與研究方向 6
二、理論基礎 8
2-1 中風恢復的神經學順序 8
2-2 評估量表的建立 8
2-3 現行臨床評估工具的優缺點 10
2-3-1 肌肉力量 10
2-3-2 活動角度 11
2-3-3 肌肉痙攣測試 12
2-3-4 布朗氏神經肌肉恢復分級 13
2-3-5 傅格-梅爾評估量表 14
2-3-6 伯格平衡量表 15
2-3-7 步行測試 17
2-4 動作分析常見的感測器以及原理 19
2-4-1 攝影機與力板 19
2-4-2 表面肌電圖 20
2-4-3 慣性感測器 21
2-5 整合感測器與臨床測試量表 22
2-5-1 神經恢復量表來推論運動學 22
2-5-2 運動學來推論神經恢復量表 23
2-6 目前的趨勢與優缺點 23
三、運動訊號與神經學分級的關係 25
3-1 結合表面肌電圖與慣性感測器 25
3-2 試驗架構 25
3-3 設計的動作 26
3-3-1 慣性感測器模組 28
3-3-2 表面肌電圖儀器以及相關參數 29
3-3-3 受試對象 31
3-4 研究倫理 32
3-5 相關演算法 32
3-6 結果分析與討論 35
四、單顆慣性感測器用於神經學分級 37
4-1 運動資料擷取與流程 37
4-2 演算模式的原理 37
4-3 尋找閥值與數據分析 39
4-4 統計結果 40
4-5 討論 41
五、運動學資料推論功能量表 43
5-1 建立新的感測器模組與驗證 43
5-1-1 慣性感測器模組 43
5-1-2 資料傳輸 45
5-1-3 測量角度驗證 45
5-1-4 臨床實際測試 48
5-1-5 實驗流程 48
5-1-6 大域座標系與局部座標系 49
5-1-7 穿戴感測器 50
5-1-8 關節活動角度計算 51
5-1-9 運動學參數 53
5-2 收案對象 55
5-3 檢定樣本數與統計分析方式 55
5-4 測試方法 56
5-5 研究倫理 57
5-6 統計結果 57
5-7 結果分析與討論 61
六、結論與未來展望 64
七、參考文獻 66

參考文獻

[1] D. R. Louie and J. J. Eng, "Berg Balance Scale score at admission can predict walking suitable for community ambulation at discharge from inpatient stroke rehabilitation," J Rehabil Med, vol. 50, no. 1, pp. 37-44, 2018.
[2] H. Makizako, N. Kabe, A. Takano, and K. Isobe, "Use of the Berg Balance Scale to predict independent gait after stroke: a study of an inpatient population in Japan," PM &R, vol. 7, no. 4, pp. 392-9, 2015.
[3] C. Benaim, D. A. Pérennou, J. Villy, M. Rousseaux, and J. Y. Pelissier, "Validation of a standardized assessment of postural control in stroke patients: the Postural Assessment Scale for Stroke Patients (PASS)," Stroke, vol. 30, no. 9, pp. 1862-8, 1999.
[4] C. A. Lima, N. A. Ricci, E. C. Nogueira, and M. R. Perracini, "The Berg Balance Scale as a clinical screening tool to predict fall risk in older adults: a systematic review," Physiotherapy, vol. 104, no. 4, pp. 383-94, 2018.
[5] D. J. Gladstone, C. J. Danells, and S. E. Black, "The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties," Neurorehabil Neural Repair, vol. 16, no. 3, pp. 232-40, 2002.
[6] S. Schwartz, M. E. Cohen, G. J. Herbison, and A. Shah, "Relationship between two measures of upper extremity strength manual muscle test compared to hand-held myometry," Arch Phys Med Rehabil, vol. 73, pp. 1063-8, 1992.
[7] R. W. Bohannon and M. B. Smith, "Interrater reliability of a modified Ashworth scale of muscle spasticity," Phys Ther, vol. 67, no. 2, pp. 206-7, 1987.
[8] L. D. Bogle Thorbahn and R. A. Newton, "Use of the Berg Balance Test to predict falls in elderly persons,"Phys Ther, vol. 76, no. 6, pp. 576-83, 1996.
[9] K. J. Sullivan, J. K. Tilson, S. Y. Cen, et al., "Fugl-Meyer assessment of sensorimotor function after stroke: standardized training procedure for clinical practice and clinical trials," Stroke, vol. 42, no. 2, pp. 427-32, 2011.
[10] K. D. Rech, A. P. Salazar, R. R. Marchese, et al., "Fugl-Meyer Assessment Scores Are Related With Kinematic Measures in People with Chronic Hemiparesis after Stroke," J Stroke Cerebrovasc Dis, vol. 29, no. 1, p. 104463, 2020.
[11] S. Naghdi, N. N. Ansari, K. Mansouri, and S. Hasson, "A neurophysiological and clinical study of Brunnstrom recovery stages in the upper limb following stroke," Brain Inj, vol. 24, no. 11, pp. 1372-8, 2010.
[12] D. G. Lee and G. C. Lee, "Correlation among Motor Function and Gait Velocity, and Explanatory Variable of Gait Velocity in Chronic Stroke Survivors," Phys Ther Rehabil Sci, vol. 11, no. 2, pp. 181-8, 2022.
[13] A.L. Hsu, P.F. Tang, and M.H. Jan, "Analysis of impairments influencing gait velocity and asymmetry of hemiplegic patients after mild to moderate stroke," Arch Phys Med Rehabil, vol. 84, no. 8, pp. 1185-93,2003.
[14] C. L. Chen, H. C. Chen, S. F. Tang, et al., "Gait performance with compensatory adaptations in stroke patients with different degrees of motor recovery," Am J Phys Med Rehabil, vol. 82, no. 12, pp. 925-35, 2003.
[15] A. L. Hsu, P. F. Tang, and M. H. Jan, "Analysis of impairments influencing gait velocity and asymmetry of hemiplegic patients after mild to moderate stroke," Arch Phys Med Rehabil, vol. 84, no. 8, pp. 1185-93, 2003.
[16] P. Tamburini, D. Mazzoli, and R. Stagni, "Towards an objective assessment of motor function in sub-acute stroke patients: Relationship between clinical rating scales and instrumental gait stability indexes," Gait Posture, vol. 59, pp. 58-64, 2018.
[17] H. T. Li, J. J. Huang, C. W. Pan, et al., "Inertial Sensing Based Assessment Methods to Quantify the Effectiveness of Post-Stroke Rehabilitation," Sensors , vol. 15, no. 7, pp. 16196-209, Jul 6 2015.
[18] T. Liu, Y. Inoue, and K. Shibata, "Development of a wearable sensor system for quantitative gait analysis," Measurement, vol. 42, no. 7, pp. 978-88, 2009.
[19] K. Saremi, J. Marehbian, X. Yan, et al., "Reliability and validity of bilateral thigh and foot accelerometry measures of walking in healthy and hemiparetic subjects," Neurorehabil Neural Repair, vol. 20, no. 2, pp. 297-305, 2006.
[20] H. Zheng, N. D. Black, and N. D. Harris, "Position-sensing technologies for movement analysis in stroke rehabilitation," Med Biol Eng Comput, vol. 43, no. 4, pp. 413-20, 2005.
[21] L. V. Ojeda, A. M. Zaferiou, S. M. Cain, et al., "Estimating Stair Running Performance Using Inertial Sensors," Sensors, vol. 17, no. 11, 2017.
[22] F. Attal, S. Mohammed, M. Dedabrishvili, et al., "Physical Human Activity Recognition Using Wearable Sensors," Sensors, vol. 15, no. 12, pp. 31314-38, 2015.
[23] S. Brunnstrom, "Motor testing procedures in hemiplegia: based on sequential recovery stages," Phys Ther, vol. 46 4, pp. 357-75, 1966.
[24] T. J. Quinn, P. Langhorne, and D. J. Stott, "Barthel index for stroke trials: development, properties, and application," Stroke, vol. 42, no. 4, pp. 1146-51, 2011.
[25] A. Hazari, A. G. Maiya, and T. V. Nagda, "Concepts of Biomechanics," in Conceptual Biomechanics and Kinesiology. Singapore: Springer Singapore, 2021, pp. 1-18.
[26] A. Hemmerich, H. Brown, S. Smith, et al., "Hip, knee, and ankle kinematics of high range of motion activities of daily living," J Orthop Res, vol. 24, no. 4, pp. 770-81, 2006.
[27] M. A. Watkins, D. L. Riddle, R. L. Lamb, et al., "Reliability of goniometric measurements and visual estimates of knee range of motion obtained in a clinical setting," Phys Ther, vol. 71, no. 2, pp. 90-6, 1991.
[28] S.L. Han, H.T. Li, H.P. Chang, et al., "Adopting a Single Inertial Sensor and Designed Motion to Classify Brunnstrom Stages for Lower Extremities on Post-stroke Patients," J Phys:Conf Ser, 2020, vol. 1583, no. 1: IOP Publishing, p. 012010.
[29] H. Kim, J. Her, J. Ko, D.S. Park, et al., "Reliability, Concurrent Validity, and Responsiveness of the Fugl-Meyer Assessment (FMA) for Hemiplegic Patients," J Phys Ther Sci, vol. 24, no. 9, pp. 893-99, 2012.
[30] T. Nakazono, K. Takahashi, Y. Suzuki, et al., "Reliability and validity of Japanese version of Fugl-Meyer assessment for the lower extremities," Top Stroke Rehabil, vol. 29, no. 2, pp. 125-32, 2022.
[31] L. Blum and N. Korner-Bitensky, "Usefulness of the Berg Balance Scale in Stroke Rehabilitation A Systematic Review," Phys Ther, vol. 88, no. 5, pp. 559-66, 2008.
[32] C. Y. Chou, C. W. Chien, I. P. Hsueh, et al., "Developing a short form of the Berg Balance Scale for people with stroke," Phys Ther, vol. 86, no. 2, pp. 195-204, 2006.
[33] P. L. Enright, "The six-minute walk test," Respir Care, vol. 48, no. 8, pp. 783-5, 2003.
[34] W. S. Kim, S. Cho, D. Baek, et al., "Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring Using Depth-Sensing Camera in Hemiplegic Stroke Patients," PLoS One, vol. 11, no. 7, p. e0158640, 2016.
[35] R. Ismail, "Muscle Power Signal Acquisition Monitoring Using Surface EMG," Biomed Environ Sci, vol. 3, no. 5, pp. 663-7, 2022.
[36] C. Avian, S. W. Prakosa, M. Faisal, et al., "Estimating finger joint angles on surface EMG using Manifold Learning and Long Short-Term Memory with Attention mechanism," Biomed Signal Process Control, vol. 71, 2022.
[37] W. Wang, K. Li, S. Yue, et al., "Associations between lower-limb muscle activation and knee flexion in post-stroke individuals: A study on the stance-to-swing phases of gait," PLoS One, vol. 12, no. 9, p. e0183865, 2017.
[38] H.T. Li, S.L. Han, and M.C. Pan, "Lower-limb motion classification for hemiparetic patients through IMU and EMG signal processing," in: International Conference on Biomedical Engineering (BME-HUST), 2016: IEEE, pp. 113-8.
[39] J. H. Buurke, A. V. Nene, G. Kwakkel, et al., "Recovery of gait after stroke: what changes?," Neurorehabil Neural Repair, vol. 22, no. 6, pp. 676-83, 2008.
[40] P. Esser, H. Dawes, J. Collett, et al., "Assessment of spatio-temporal gait parameters using inertial measurement units in neurological populations," Gait Posture, vol. 34, no. 4, pp. 558-60, 2011.
[41] A. Köse, A. Cereatti, and U. Della Croce, "Bilateral step length estimation using a single inertial measurement unit attached to the pelvis," J Neuroeng Rehabil, vol. 9, no. 1, p. 9, 2012.
[42] S. Yang, J. T. Zhang, A. C. Novak, et al., "Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors," Gait Posture, vol. 37, no. 3, pp. 354-8, 2013.
[43] Y. Yang, L. Chen, J. Pang, et al., "Validation of a spatiotemporal gait model using inertial measurement units for early-stage Parkinson′s disease detection during turns," IEEE Trans Biomed Eng, 2022.
[44] P. Bastos, B. Meira, M. Mendonca, et al., "Distinct gait dimensions are modulated by physical activity in Parkinson′s disease patients," J Neural Transm (Vienna), vol. 129, no. 7, pp. 879-87, 2022.
[45] S. Nadeau, A. B. Arsenault, D. Gravel, et al., "Analysis of the clinical factors determining natural and maximal gait speeds in adults with a stroke," Am J Phys Med Rehabil, vol. 78, no. 2, pp.123-30, 1999.
[46] M. K. Aaslund, R. Moe-Nilssen, B. B. Gjelsvik, et al., "A longitudinal study investigating how stroke severity, disability, and physical function the first week post-stroke are associated with walking speed six months post-stroke," Physiother Theory Pract, vol. 33, no. 12, pp. 932-42, 2017.
[47] I. Jonkers, S. Delp, and C. Patten, "Capacity to increase walking speed is limited by impaired hip and ankle power generation in lower functioning persons post-stroke," Gait Posture, vol. 29, no. 1, pp. 129-37, 2009.
[48] S. L. Han, M. J. Xie, C. C. Chien, et al., "Using MEMS-based inertial sensor with ankle foot orthosis for telerehabilitation and its clinical evaluation in brain injuries and total knee replacement patients," Microsyst Technol, vol. 22, no. 3, pp. 625-34, 2016.
[49] A. Stephenson, S. Howes, P. J. Murphy, et al., "Factors influencing the delivery of telerehabilitation for stroke: A systematic review," PLoS One, vol. 17, no. 5, p. e0265828, 2022.
[50] O. Postolache, D. J. Hemanth, R. Alexandre, et al., "Remote Monitoring of Physical Rehabilitation of Stroke Patients Using IoT and Virtual Reality," IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, pp. 562-73, 2021.
[51] 李欣達, "Classification of Lower Limb Motion for Hemiparetic Patients through IMU and EMG Signal Processing " Master, Mechanical Engineering, National Central University, Taiwan, 2016.
[52] K. H. Mauritz, "Gait training in hemiplegia," Eur J Neurol, vol. 9, 1, pp. 23-9, 2002.
[53] L. S. Vargas-Valencia, A. Elias, A. F. Neto, et al., "Body to sensor calibration procedure for lower limb joint angle estimation applied to IMU-based gait analysis," in XXIV Brazilian Congress on Biomedical Engineering – CBEB 2014.
[54] T. Sugiharto, "Wireless-IMU Based Gait Parameters Characterization for Objectively Assessing Stroke Patients Disability," Master, Mechanical Engineering, National Central University, Taiwan, 2019.
[55] S. L. Han, M. C. Pan, and T. Sugiharto, "Useful Information Brought From Wearable Devices To The Clinical Walk Tests In Stroke Patients," In: ASSISTIVE TECHNOLOGY. 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA: TAYLOR & FRANCIS INC, 2021.
[56] S. O. Madgwick, A. J. Harrison, and A. Vaidyanathan, "Estimation of IMU and MARG orientation using a gradient descent algorithm," (in eng), IEEE Int Conf Rehabil Robot,, p. 5975346, 2011.
[57] Y. Wang, M. Mukaino, K. Ohtsuka, et al., "Gait characteristics of post-stroke hemiparetic patients with different walking speeds," Int J Rehabil Res, vol. 43, no. 1, pp. 69-75, 2020.
[58] P. Schober, C. Boer, and L. A. Schwarte, "Correlation Coefficients: Appropriate Use and Interpretation," Anesth Analg, vol. 126, no. 5, 2018.
[59] M. Grau-Pellicer, A. Chamarro-Lusar, J. Medina-Casanovas, and B. C. Serdà Ferrer, "Walking speed as a predictor of community mobility and quality of life after stroke," Top Stroke Rehabil, vol. 26, no. 5, pp. 349-58, 2019.
[60] S. L. Han, M. L. Cai, H. H. Yang, et al., "Can kinematic variables deduce functional scales among chronic stroke survivors? A proof of concept for inertial sensors," Sensor Review, vol. 42, no. 3, pp. 316-25, 2022.

指導教授

潘敏俊

審核日期

2022-8-29

推文