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| Research of Diagnostic Method for Upper Limb MotorFunction Based on Fusion of Biological Motion Information |
| Intelligent Control & Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou 310018， China |
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Abstract A comprehensive method, fused with kinematics information and bioelectricity information, for upper limb motorfunction evaluation was proposed. The method was aimed to evaluate the motor functions of the shoulder, elbow and wrist joints of the hemiplegic patients objectively and quantitatively. Acceleration signal and EMG signal were collected and selected, and then feature extraction and feature selection were done during the movement of the upper limb. Utilizing the distinct superiorities of the two signals, characteristic value of the signals was optimally combined. Several linear regression models were constructed to realize comprehensive assessment of the upper limb motorfunction based on Fugl-Meyer assessment. In the function diagnostic experiment of 7 movements of the shoulder, elbow and wrist (namely hand grasps, hand extension, wrist flexion, wrist extension, elbow flexion, elbow extension, front raise) with 10 participants, the evaluation method not only could realize information extraction and function diagnosis in real time, but also had a strong consistency with Fugl-Meyer assessment with a correlation coefficient above 99%. The above experimental results showed that the diagnostic method could quantify the upper limb motorfunction more detailed instead of traditional assessment method.
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［1］Duncan PW, Propst M, Nelson SG. Reliability of the fuglmeyer assessment of sensorimotor recoveryfollowing cerebrovascular accident ［J］. Physical therapy, 1983, 63(10): 1606-1610.
［2］Ferraro M, Demaio JH, Krol J, et al. Assessing the motor status score: a scale for the evaluation of upper limb motor outcomes in patients after stroke ［J］. Neurorehabilitation and Neural Repair, 2002, 16(3):283-289.
［3］欧海宁, 康健. 脑卒中患者肢体运动功能的综合康复治疗［J］. 中国康复医学杂志, 1999, 14(3):106-108.
［4］Whitlock JA, Hamilton BB. Functional outcome after rehabilitation for severe traumatic brain injury ［J］. Archives of Physical Medicine and Rehabilitation, 1995, 76(12): 1103-1112.
［5］Peter O, Fazekas G, Zsiga K, et al. Robotmediated upper limb physiotherapy: Review and recommendations for future clinical trials ［J］. International Journal of Rehabilitation Research, 2011, 34(3):196-202.
［6］阳小勇, 王子羲, 季林红，等. 上肢肩肘关节运动功能的综合性运动学评价指标 ［J］. 清华大学学报(自然科学版), 2006, 46(2): 172-175.
［7］Hall KM, Cohen ME, Wright J, et al. Characteristics of the functional independence measure in traumatic spinal cord injury ［J］. Archives of Physical Medicine and Rehabilitation, 1999, 80(11): 1471-1476.
［8］Knorr B, Hughes R, Sherrill D, et al. Quantitative measures of functional upper limb movement in persons after stroke ［C］ // Akay M. Proceedings of 2nd International IEEE EMBS Conferences on Neural Engineering. Arlington, VA: IEEE Pres, 2005: 252-255.
［9］Cesarelli M, D′Addio G, Romano M, et al. Kinematics patterns of upper arm reaching movement in robotmediated therapy ［C］ //Savino M,eds Proceedings of the 2011 IEEE International Symposium on Medical Measurements and Applications. Bari: IEEE Pres, 2011: 465-468.
［10］Scott RN, Parker PA. Myoelectric prostheses: state of the art ［J］. Journal of Medical Engineering & Technology, 1988, 12(4):143-151.
［11］Shao Qi, Bassett DN, Manal K, et al. An EMGdriven model to estimate muscle forces and joint moments in stroke patients ［J］. Computers in Biology and Medicine, 2009, 39(12): 1083- 1088.
［12］Nakano T, Nagata K, Yamada M, et al. Application of least square method for muscular strength estimation in hand motion recognition using surface EMG ［C］ // He B,eds. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis: IEEE Press, 2009: 2655-2658.
［13］季林红, 张宇博, 王子羲，等.基于自适应Chirplet分解的偏瘫肌强直症状评估 ［J］.清华大学学报(自然科学版), 2007,47(5):627-630.
［14］李锦，宁新宝. 短时高频心电图的基本尺度熵分析 ［J］. 中国生物医学工程学报，2007, 26(4): 508-512.
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