A Basic Study on Development of Intelligent Motorized Prosthetics

智能电动假肢研制的基础研究

• 批准号: 10838004
• 负责人: YOKOI Hiroshi
• 金额: $ 2.05万
• 依托单位: HOKKAIDO UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1998
• 资助国家: 日本
• 起止时间: 1998 至 2000
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10838004/
• 关键词: Adaptive Learning Theory; Processing Dura Mater Signals; Adaptability for Individuals; Achieve lost Motor Function; Multi-site Electrode; FPGA; Electrophysiology; Control of Motorized Prosthetics; 脊髄損傷; 電動義装具; 知的制御系; 硬膜外電位; 刺激反応系モデル

This research project is focused on the technical back-up for the people with spinal cord injuries to achieve the motor function, which was lost by an accident. The aim of this research is to develop a methodology on the adaptive control of the motorized prosthetics for individuals using a current LSI technology and an adaptive learning theory in order to measure and process the action potential signal of the dura mater of the spinal cord at the brain-side from the damaged area. The important components of this research are development of an adaptive classification method, reconstruction of a control function using FPGA (Filed Programmable Gate Array), classification of electrophysiological signals of a rat cranial dura mater for a new communication and control, and development of multi-site electrode using micromachining technology.The findings achieved by this research are as follows.1. We applied the adaptive learning theory to classify the human electromyogram as the signal includi … More ng the differences of individuals, and performed to control the prosthetic hands. As a result, the adaptation for individuals and stable classification was verified, and the adaptive learning theory was effective.2. We measured from cranial dura mater, which is closer to the sensory cortex, in an anaesthetized rat electrophysiologically.. The cluster corresponding to the stimuli was generated by the off-line time series analysis using the adaptive learning theory.3. With the same method, we measured the signal from the area, which is concerned with the control of the lower limb, above the Layer-I of the motor cortex using electrodes located at cranial bone. And the result showed the possibility of classification and extraction for the signal corresponding to the movement of lower limb.4. We developed multi-layer, multi-site implantation microelectrode. And the measurement of the signal from the dura mater of the spinal cord with the various implantation methods using wire-electrode or multi-site electrode is tried.5. The signal processing system executable to synthesize an intended mapping function and the experience system by using FPGA was developed. The experience showed that this system has the computability to synthesize the signal processing circuit on the learning theory and the capability of the effective high speed processing in terms of parallel processing.Conclude this research : The aim of this research is to develop the device to classify the signal for individual's characteristics of action potential as the real-time adaptive processing system. For this purpose, we performed to construct adaptive classification method for nerve signal, and showed the possibility of synthesis of the signal processing circuit on the learning theory by FPGA for real-time processing. With considering the result of animal experience, we serve as a stepping-stone to develop the intelligent motorized prosthetics by adaptive classification of signal on the dura mater of the spinal cord. And we made the result of these researches public for dissertation and so on. Less

该研究项目的重点是脊髓损伤的人的技术备份，以实现运动功能，这因事故而丢失。这项研究的目的是开发一种有关使用当前LSI技术和自适应学习理论对个体自适应控制的方法的方法，以衡量和处理受损区域中大脑侧脊髓硬脑膜的动作潜在信号。这项研究的重要组成部分是开发一种自适应分类方法，使用FPGA（可编程的门阵列）重建控制函数，将大鼠颅硬脑膜的电生理信号分类以及使用微加工技术的多壁机电极开发。我们应用了自适应学习理论将人类肌电图分类为信号包括……更多ng个体的差异，并进行了控制假肢。结果，对个人的适应和稳定的分类进行了验证，自适应学习理论是有效的。2。我们从颅脑硬脑膜Mater（更接近感觉皮层）中测量了在麻醉的大鼠电生理学上。簇对刺激的簇对应关系是通过使用自适应学习理论的离线时间序列分析产生的。3。使用相同的方法，我们测量了该区域的信号，该区域与下肢的控制相关，该信号使用位于颅骨的电子中的运动皮层I层上方。结果显示了与下肢运动相对应的信号的分类和提取的可能性。4。我们开发了多层，多站点植入微电极。并尝试使用电线电极或多站点电极的各种植入方法测量脊髓硬脑膜的信号。5。可以使用FPGA来综合预期的映射函数，并使用FPGA进行体验系统。经验表明，该系统具有在学习理论上综合信号处理电路的计算以及有效的高速处理能力，从并行处理中。结论：这项研究的目的是，本研究的目的是开发该设备，以将个人特征分类为动作电位特征作为实时适应性处理系统。为此，我们进行了为神经信号构造自适应分类方法，并显示了FPGA在学习理论上合成信号处理电路的可能性。考虑到动物体验的结果，我们通过自适应在脊髓硬脑膜上的信号进行自适应分类来发展智能机动假体。我们将这些研究的结果公开以进行论文等。较少的

项目成果

Takahiro Kawashima, Jun Hakura, Hiroshi Yokoi, Masatoshi Takita, Yukinori Kakazu: "Identification of Reactive Signal in Rat Frantal Cortex Based on Cellular Field Model" Intelligent Engineering Systems Through Artificial Neural Networks. 643-648 (1998)

Takahiro Kawashima、Jun Hakura、Hiroshi Yokoi、Masatoshi Takita、Yukinori Kakazu：“基于细胞场模型的大鼠皮质反应信号识别”通过人工神经网络的智能工程系统。

Hiroki Yamaguchi, Wenwei Yu, Masaharu Maruishi, Hiroshi Yokoi, Yukio Mano, Yukinori Kakazu: "EMG Automatic Switch for FES Control for Hemiplegics Using Artificial Neural Network"Proceedings of 6th International Conference on Intelligent Autonomous Systems

Hiroki Yamaguchi、Wenwei Yu、Masaharu Maruishi、Hiroshi Yokoi、Yukio Mano、Yukinori Kakazu：“利用人工神经网络对偏瘫患者进行 FES 控制的 EMG 自动开关”第六届智能自治系统国际会议论文集

Tomokazu Shindo, Hiroshi Yokoi, Yukinori Kakazu: "Adaptive Logic Circuits based on Net-List Evolution"Journal of Robotics and Mechatronics. Vol.2, No.2. 144-149 (2000)

Tomokazu Shindo、Hiroshi Yokoi、Yukinori Kakazu：“基于网表进化的自适应逻辑电路”机器人与机电一体化杂志。

Tomokazu Shindo, Kensuke Takita, Hiroshi Yokoi, Yukinori Kakazu: "A study for Environmental Adaptation of Robot Using Evolvable Hardware"Intelligent Engineering Systems Through Artifical Neural Networks. vol.9. 445-450 (1999)

Tomokazu Shindo、Kensuke Takita、Hiroshi Yokoi、Yukinori Kakazu：“使用可进化硬件的机器人环境适应研究”通过人工神经网络的智能工程系统。

Hiroshi YOKOI,Takafumi MIZUNO, Masatoshi TAKITA and Yukinori KAKAZU: "Amoeba Searching Behavior Model for Traveling Salesman Problem"International Journal of SMART ENGINEERING SYSTEM DESIGN. Vol.2,Number1. 43-55 (1999)

Hiroshi YOKOI、Takafumi MIZUNO、Masatoshi TAKITA 和 Yukinori KAKAZU：“旅行商问题的阿米巴搜索行为模型”国际智能工程系统设计杂志。